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.",
"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.",
"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.",
"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.",
"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.",
"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.",
"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.",
"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": [
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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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"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.",
"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": [
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],
"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
}
// 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.",
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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.",
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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": [
"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.",
"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
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