Menda-3b-750

6
1
3.0B
1 language
by
weathermanj
Language Model
OTHER
3B params
New
6 downloads
Early-stage
Edge AI:
Mobile
Laptop
Server
7GB+ RAM
Mobile
Laptop
Server
Quick Summary

AI model with specialized capabilities.

Device Compatibility

Mobile
4-6GB RAM
Laptop
16GB RAM
Server
GPU
Minimum Recommended
3GB+ RAM

Code Examples

pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "weathermanj/Menda-3b-750"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain the concept of machine learning in simple terms."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
Detailed Benchmark Resultsjson
{
  "model": "qwen_grpo_750",
  "task": "hellaswag-0shot",
  "accuracy": 0.75,
  "correct": 15,
  "total": 20,
  "results": [
    {
      "index": 0,
      "context": "A man is sitting on a roof. he",
      "options": [
        "is using wrap to wrap a pair of skis.",
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        "is holding a rubik's cube.",
        "starts pulling up roofing on a roof."
      ],
      "correct_label": 3,
      "predicted_label": 3,
      "is_correct": true
    }
    // Additional results truncated for brevity
  ]
}
Detailed Benchmark Resultsjson
{
  "model": "qwen_grpo_750",
  "task": "hellaswag-0shot",
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  "correct": 15,
  "total": 20,
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    {
      "index": 0,
      "context": "A man is sitting on a roof. he",
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        "starts pulling up roofing on a roof."
      ],
      "correct_label": 3,
      "predicted_label": 3,
      "is_correct": true
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    // Additional results truncated for brevity
  ]
}
Detailed Benchmark Resultsjson
{
  "model": "qwen_grpo_750",
  "task": "hellaswag-0shot",
  "accuracy": 0.75,
  "correct": 15,
  "total": 20,
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      "index": 0,
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        "starts pulling up roofing on a roof."
      ],
      "correct_label": 3,
      "predicted_label": 3,
      "is_correct": true
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    // Additional results truncated for brevity
  ]
}
Detailed Benchmark Resultsjson
{
  "model": "qwen_grpo_750",
  "task": "hellaswag-0shot",
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  "correct": 15,
  "total": 20,
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    {
      "index": 0,
      "context": "A man is sitting on a roof. he",
      "options": [
        "is using wrap to wrap a pair of skis.",
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        "starts pulling up roofing on a roof."
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      "predicted_label": 3,
      "is_correct": true
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    // Additional results truncated for brevity
  ]
}
Detailed Benchmark Resultsjson
{
  "model": "qwen_grpo_750",
  "task": "hellaswag-0shot",
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  "correct": 15,
  "total": 20,
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    {
      "index": 0,
      "context": "A man is sitting on a roof. he",
      "options": [
        "is using wrap to wrap a pair of skis.",
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        "is holding a rubik's cube.",
        "starts pulling up roofing on a roof."
      ],
      "correct_label": 3,
      "predicted_label": 3,
      "is_correct": true
    }
    // Additional results truncated for brevity
  ]
}
Detailed Benchmark Resultsjson
{
  "model": "qwen_grpo_750",
  "task": "hellaswag-0shot",
  "accuracy": 0.75,
  "correct": 15,
  "total": 20,
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    {
      "index": 0,
      "context": "A man is sitting on a roof. he",
      "options": [
        "is using wrap to wrap a pair of skis.",
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        "is holding a rubik's cube.",
        "starts pulling up roofing on a roof."
      ],
      "correct_label": 3,
      "predicted_label": 3,
      "is_correct": true
    }
    // Additional results truncated for brevity
  ]
}
Detailed Benchmark Resultsjson
{
  "model": "qwen_grpo_750",
  "task": "hellaswag-0shot",
  "accuracy": 0.75,
  "correct": 15,
  "total": 20,
  "results": [
    {
      "index": 0,
      "context": "A man is sitting on a roof. he",
      "options": [
        "is using wrap to wrap a pair of skis.",
        "is ripping level tiles off.",
        "is holding a rubik's cube.",
        "starts pulling up roofing on a roof."
      ],
      "correct_label": 3,
      "predicted_label": 3,
      "is_correct": true
    }
    // Additional results truncated for brevity
  ]
}
Detailed Benchmark Resultsjson
{
  "model": "qwen_grpo_750",
  "task": "hellaswag-0shot",
  "accuracy": 0.75,
  "correct": 15,
  "total": 20,
  "results": [
    {
      "index": 0,
      "context": "A man is sitting on a roof. he",
      "options": [
        "is using wrap to wrap a pair of skis.",
        "is ripping level tiles off.",
        "is holding a rubik's cube.",
        "starts pulling up roofing on a roof."
      ],
      "correct_label": 3,
      "predicted_label": 3,
      "is_correct": true
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    // Additional results truncated for brevity
  ]
}
Detailed Benchmark Resultsjson
{
  "model": "qwen_grpo_750",
  "task": "hellaswag-0shot",
  "accuracy": 0.75,
  "correct": 15,
  "total": 20,
  "results": [
    {
      "index": 0,
      "context": "A man is sitting on a roof. he",
      "options": [
        "is using wrap to wrap a pair of skis.",
        "is ripping level tiles off.",
        "is holding a rubik's cube.",
        "starts pulling up roofing on a roof."
      ],
      "correct_label": 3,
      "predicted_label": 3,
      "is_correct": true
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    // Additional results truncated for brevity
  ]
}
Detailed Benchmark Resultsjson
{
  "model": "qwen_grpo_750",
  "task": "hellaswag-0shot",
  "accuracy": 0.75,
  "correct": 15,
  "total": 20,
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      "index": 0,
      "context": "A man is sitting on a roof. he",
      "options": [
        "is using wrap to wrap a pair of skis.",
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        "is holding a rubik's cube.",
        "starts pulling up roofing on a roof."
      ],
      "correct_label": 3,
      "predicted_label": 3,
      "is_correct": true
    }
    // Additional results truncated for brevity
  ]
}
Detailed Benchmark Resultsjson
{
  "model": "qwen_grpo_750",
  "task": "hellaswag-0shot",
  "accuracy": 0.75,
  "correct": 15,
  "total": 20,
  "results": [
    {
      "index": 0,
      "context": "A man is sitting on a roof. he",
      "options": [
        "is using wrap to wrap a pair of skis.",
        "is ripping level tiles off.",
        "is holding a rubik's cube.",
        "starts pulling up roofing on a roof."
      ],
      "correct_label": 3,
      "predicted_label": 3,
      "is_correct": true
    }
    // Additional results truncated for brevity
  ]
}
Detailed Benchmark Resultsjson
{
  "model": "qwen_grpo_750",
  "task": "hellaswag-0shot",
  "accuracy": 0.75,
  "correct": 15,
  "total": 20,
  "results": [
    {
      "index": 0,
      "context": "A man is sitting on a roof. he",
      "options": [
        "is using wrap to wrap a pair of skis.",
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        "is holding a rubik's cube.",
        "starts pulling up roofing on a roof."
      ],
      "correct_label": 3,
      "predicted_label": 3,
      "is_correct": true
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    // Additional results truncated for brevity
  ]
}
Detailed Benchmark Resultsjson
{
  "model": "qwen_grpo_750",
  "task": "hellaswag-0shot",
  "accuracy": 0.75,
  "correct": 15,
  "total": 20,
  "results": [
    {
      "index": 0,
      "context": "A man is sitting on a roof. he",
      "options": [
        "is using wrap to wrap a pair of skis.",
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        "is holding a rubik's cube.",
        "starts pulling up roofing on a roof."
      ],
      "correct_label": 3,
      "predicted_label": 3,
      "is_correct": true
    }
    // Additional results truncated for brevity
  ]
}
Detailed Benchmark Resultsjson
{
  "model": "qwen_grpo_750",
  "task": "hellaswag-0shot",
  "accuracy": 0.75,
  "correct": 15,
  "total": 20,
  "results": [
    {
      "index": 0,
      "context": "A man is sitting on a roof. he",
      "options": [
        "is using wrap to wrap a pair of skis.",
        "is ripping level tiles off.",
        "is holding a rubik's cube.",
        "starts pulling up roofing on a roof."
      ],
      "correct_label": 3,
      "predicted_label": 3,
      "is_correct": true
    }
    // Additional results truncated for brevity
  ]
}
Detailed Benchmark Resultsjson
{
  "model": "qwen_grpo_750",
  "task": "hellaswag-0shot",
  "accuracy": 0.75,
  "correct": 15,
  "total": 20,
  "results": [
    {
      "index": 0,
      "context": "A man is sitting on a roof. he",
      "options": [
        "is using wrap to wrap a pair of skis.",
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        "starts pulling up roofing on a roof."
      ],
      "correct_label": 3,
      "predicted_label": 3,
      "is_correct": true
    }
    // Additional results truncated for brevity
  ]
}
Detailed Benchmark Resultsjson
{
  "model": "qwen_grpo_750",
  "task": "hellaswag-0shot",
  "accuracy": 0.75,
  "correct": 15,
  "total": 20,
  "results": [
    {
      "index": 0,
      "context": "A man is sitting on a roof. he",
      "options": [
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        "is holding a rubik's cube.",
        "starts pulling up roofing on a roof."
      ],
      "correct_label": 3,
      "predicted_label": 3,
      "is_correct": true
    }
    // Additional results truncated for brevity
  ]
}
Detailed Benchmark Resultsjson
{
  "model": "qwen_grpo_750",
  "task": "hellaswag-0shot",
  "accuracy": 0.75,
  "correct": 15,
  "total": 20,
  "results": [
    {
      "index": 0,
      "context": "A man is sitting on a roof. he",
      "options": [
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        "is holding a rubik's cube.",
        "starts pulling up roofing on a roof."
      ],
      "correct_label": 3,
      "predicted_label": 3,
      "is_correct": true
    }
    // Additional results truncated for brevity
  ]
}
Detailed Benchmark Resultsjson
{
  "model": "qwen_grpo_750",
  "task": "hellaswag-0shot",
  "accuracy": 0.75,
  "correct": 15,
  "total": 20,
  "results": [
    {
      "index": 0,
      "context": "A man is sitting on a roof. he",
      "options": [
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        "starts pulling up roofing on a roof."
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      "correct_label": 3,
      "predicted_label": 3,
      "is_correct": true
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    // Additional results truncated for brevity
  ]
}
Detailed Benchmark Resultsjson
{
  "model": "qwen_grpo_750",
  "task": "hellaswag-0shot",
  "accuracy": 0.75,
  "correct": 15,
  "total": 20,
  "results": [
    {
      "index": 0,
      "context": "A man is sitting on a roof. he",
      "options": [
        "is using wrap to wrap a pair of skis.",
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        "is holding a rubik's cube.",
        "starts pulling up roofing on a roof."
      ],
      "correct_label": 3,
      "predicted_label": 3,
      "is_correct": true
    }
    // Additional results truncated for brevity
  ]
}
Detailed Benchmark Resultsjson
{
  "model": "qwen_grpo_750",
  "task": "hellaswag-0shot",
  "accuracy": 0.75,
  "correct": 15,
  "total": 20,
  "results": [
    {
      "index": 0,
      "context": "A man is sitting on a roof. he",
      "options": [
        "is using wrap to wrap a pair of skis.",
        "is ripping level tiles off.",
        "is holding a rubik's cube.",
        "starts pulling up roofing on a roof."
      ],
      "correct_label": 3,
      "predicted_label": 3,
      "is_correct": true
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    // Additional results truncated for brevity
  ]
}
Detailed Benchmark Resultsjson
{
  "model": "qwen_grpo_750",
  "task": "hellaswag-0shot",
  "accuracy": 0.75,
  "correct": 15,
  "total": 20,
  "results": [
    {
      "index": 0,
      "context": "A man is sitting on a roof. he",
      "options": [
        "is using wrap to wrap a pair of skis.",
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        "starts pulling up roofing on a roof."
      ],
      "correct_label": 3,
      "predicted_label": 3,
      "is_correct": true
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    // Additional results truncated for brevity
  ]
}
Detailed Benchmark Resultsjson
{
  "model": "qwen_grpo_750",
  "task": "hellaswag-0shot",
  "accuracy": 0.75,
  "correct": 15,
  "total": 20,
  "results": [
    {
      "index": 0,
      "context": "A man is sitting on a roof. he",
      "options": [
        "is using wrap to wrap a pair of skis.",
        "is ripping level tiles off.",
        "is holding a rubik's cube.",
        "starts pulling up roofing on a roof."
      ],
      "correct_label": 3,
      "predicted_label": 3,
      "is_correct": true
    }
    // Additional results truncated for brevity
  ]
}
Detailed Benchmark Resultsjson
{
  "model": "qwen_grpo_750",
  "task": "hellaswag-0shot",
  "accuracy": 0.75,
  "correct": 15,
  "total": 20,
  "results": [
    {
      "index": 0,
      "context": "A man is sitting on a roof. he",
      "options": [
        "is using wrap to wrap a pair of skis.",
        "is ripping level tiles off.",
        "is holding a rubik's cube.",
        "starts pulling up roofing on a roof."
      ],
      "correct_label": 3,
      "predicted_label": 3,
      "is_correct": true
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    // Additional results truncated for brevity
  ]
}
Detailed Benchmark Resultsjson
{
  "model": "qwen_grpo_750",
  "task": "hellaswag-0shot",
  "accuracy": 0.75,
  "correct": 15,
  "total": 20,
  "results": [
    {
      "index": 0,
      "context": "A man is sitting on a roof. he",
      "options": [
        "is using wrap to wrap a pair of skis.",
        "is ripping level tiles off.",
        "is holding a rubik's cube.",
        "starts pulling up roofing on a roof."
      ],
      "correct_label": 3,
      "predicted_label": 3,
      "is_correct": true
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    // Additional results truncated for brevity
  ]
}
Detailed Benchmark Resultsjson
{
  "model": "qwen_grpo_750",
  "task": "hellaswag-0shot",
  "accuracy": 0.75,
  "correct": 15,
  "total": 20,
  "results": [
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      "index": 0,
      "context": "A man is sitting on a roof. he",
      "options": [
        "is using wrap to wrap a pair of skis.",
        "is ripping level tiles off.",
        "is holding a rubik's cube.",
        "starts pulling up roofing on a roof."
      ],
      "correct_label": 3,
      "predicted_label": 3,
      "is_correct": true
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    // Additional results truncated for brevity
  ]
}
Detailed Benchmark Resultsjson
{
  "model": "qwen_grpo_750",
  "task": "hellaswag-0shot",
  "accuracy": 0.75,
  "correct": 15,
  "total": 20,
  "results": [
    {
      "index": 0,
      "context": "A man is sitting on a roof. he",
      "options": [
        "is using wrap to wrap a pair of skis.",
        "is ripping level tiles off.",
        "is holding a rubik's cube.",
        "starts pulling up roofing on a roof."
      ],
      "correct_label": 3,
      "predicted_label": 3,
      "is_correct": true
    }
    // Additional results truncated for brevity
  ]
}
Detailed Benchmark Resultsjson
{
  "model": "qwen_grpo_750",
  "task": "hellaswag-0shot",
  "accuracy": 0.75,
  "correct": 15,
  "total": 20,
  "results": [
    {
      "index": 0,
      "context": "A man is sitting on a roof. he",
      "options": [
        "is using wrap to wrap a pair of skis.",
        "is ripping level tiles off.",
        "is holding a rubik's cube.",
        "starts pulling up roofing on a roof."
      ],
      "correct_label": 3,
      "predicted_label": 3,
      "is_correct": true
    }
    // Additional results truncated for brevity
  ]
}
Detailed Benchmark Resultsjson
{
  "model": "qwen_grpo_750",
  "task": "hellaswag-0shot",
  "accuracy": 0.75,
  "correct": 15,
  "total": 20,
  "results": [
    {
      "index": 0,
      "context": "A man is sitting on a roof. he",
      "options": [
        "is using wrap to wrap a pair of skis.",
        "is ripping level tiles off.",
        "is holding a rubik's cube.",
        "starts pulling up roofing on a roof."
      ],
      "correct_label": 3,
      "predicted_label": 3,
      "is_correct": true
    }
    // Additional results truncated for brevity
  ]
}
Detailed Benchmark Resultsjson
{
  "model": "qwen_grpo_750",
  "task": "hellaswag-0shot",
  "accuracy": 0.75,
  "correct": 15,
  "total": 20,
  "results": [
    {
      "index": 0,
      "context": "A man is sitting on a roof. he",
      "options": [
        "is using wrap to wrap a pair of skis.",
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Detailed Benchmark Resultsjson
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Detailed Benchmark Resultsjson
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Detailed Benchmark Resultsjson
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Detailed Benchmark Resultsjson
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Detailed Benchmark Resultsjson
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Detailed Benchmark Resultsjson
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Detailed Benchmark Resultsjson
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Detailed Benchmark Resultsjson
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Detailed Benchmark Resultsjson
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Detailed Benchmark Resultsjson
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Detailed Benchmark Resultsjson
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Detailed Benchmark Resultsjson
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json
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json
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json
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json
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json
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json
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json
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json
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    // Additional results truncated for brevity
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}

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