guru-7B

233
3
license:cc-by-nc-4.0
by
LLM360
Language Model
OTHER
7B params
New
233 downloads
Early-stage
Try on Hugging FaceAdd to Compare
Edge AI:
Mobile
Laptop
Server
16GB+ RAM
Mobile
Laptop
Server
Quick Summary

AI model with specialized capabilities.

Device Compatibility

Mobile
4-6GB RAM
Laptop
16GB RAM
Server
GPU
Minimum Recommended
7GB+ RAM

Code Examples

pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model = "LLM360/Guru-7B"
tokenizer = AutoTokenizer.from_pretrained(model)
model = AutoModelForCausalLM.from_pretrained(model, device_map="auto", torch_dtype="auto")

messages = [{"role": "user", "content": "What is reinforcement learning?"}]
prompt = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
outputs = model.generate(prompt, max_new_tokens=256, temperature=1.0, top_p=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model = "LLM360/Guru-7B"
tokenizer = AutoTokenizer.from_pretrained(model)
model = AutoModelForCausalLM.from_pretrained(model, device_map="auto", torch_dtype="auto")

messages = [{"role": "user", "content": "What is reinforcement learning?"}]
prompt = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
outputs = model.generate(prompt, max_new_tokens=256, temperature=1.0, top_p=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model = "LLM360/Guru-7B"
tokenizer = AutoTokenizer.from_pretrained(model)
model = AutoModelForCausalLM.from_pretrained(model, device_map="auto", torch_dtype="auto")

messages = [{"role": "user", "content": "What is reinforcement learning?"}]
prompt = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
outputs = model.generate(prompt, max_new_tokens=256, temperature=1.0, top_p=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model = "LLM360/Guru-7B"
tokenizer = AutoTokenizer.from_pretrained(model)
model = AutoModelForCausalLM.from_pretrained(model, device_map="auto", torch_dtype="auto")

messages = [{"role": "user", "content": "What is reinforcement learning?"}]
prompt = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
outputs = model.generate(prompt, max_new_tokens=256, temperature=1.0, top_p=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model = "LLM360/Guru-7B"
tokenizer = AutoTokenizer.from_pretrained(model)
model = AutoModelForCausalLM.from_pretrained(model, device_map="auto", torch_dtype="auto")

messages = [{"role": "user", "content": "What is reinforcement learning?"}]
prompt = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
outputs = model.generate(prompt, max_new_tokens=256, temperature=1.0, top_p=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model = "LLM360/Guru-7B"
tokenizer = AutoTokenizer.from_pretrained(model)
model = AutoModelForCausalLM.from_pretrained(model, device_map="auto", torch_dtype="auto")

messages = [{"role": "user", "content": "What is reinforcement learning?"}]
prompt = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
outputs = model.generate(prompt, max_new_tokens=256, temperature=1.0, top_p=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model = "LLM360/Guru-7B"
tokenizer = AutoTokenizer.from_pretrained(model)
model = AutoModelForCausalLM.from_pretrained(model, device_map="auto", torch_dtype="auto")

messages = [{"role": "user", "content": "What is reinforcement learning?"}]
prompt = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
outputs = model.generate(prompt, max_new_tokens=256, temperature=1.0, top_p=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model = "LLM360/Guru-7B"
tokenizer = AutoTokenizer.from_pretrained(model)
model = AutoModelForCausalLM.from_pretrained(model, device_map="auto", torch_dtype="auto")

messages = [{"role": "user", "content": "What is reinforcement learning?"}]
prompt = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
outputs = model.generate(prompt, max_new_tokens=256, temperature=1.0, top_p=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model = "LLM360/Guru-7B"
tokenizer = AutoTokenizer.from_pretrained(model)
model = AutoModelForCausalLM.from_pretrained(model, device_map="auto", torch_dtype="auto")

messages = [{"role": "user", "content": "What is reinforcement learning?"}]
prompt = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
outputs = model.generate(prompt, max_new_tokens=256, temperature=1.0, top_p=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model = "LLM360/Guru-7B"
tokenizer = AutoTokenizer.from_pretrained(model)
model = AutoModelForCausalLM.from_pretrained(model, device_map="auto", torch_dtype="auto")

messages = [{"role": "user", "content": "What is reinforcement learning?"}]
prompt = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
outputs = model.generate(prompt, max_new_tokens=256, temperature=1.0, top_p=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model = "LLM360/Guru-7B"
tokenizer = AutoTokenizer.from_pretrained(model)
model = AutoModelForCausalLM.from_pretrained(model, device_map="auto", torch_dtype="auto")

messages = [{"role": "user", "content": "What is reinforcement learning?"}]
prompt = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
outputs = model.generate(prompt, max_new_tokens=256, temperature=1.0, top_p=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model = "LLM360/Guru-7B"
tokenizer = AutoTokenizer.from_pretrained(model)
model = AutoModelForCausalLM.from_pretrained(model, device_map="auto", torch_dtype="auto")

messages = [{"role": "user", "content": "What is reinforcement learning?"}]
prompt = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
outputs = model.generate(prompt, max_new_tokens=256, temperature=1.0, top_p=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model = "LLM360/Guru-7B"
tokenizer = AutoTokenizer.from_pretrained(model)
model = AutoModelForCausalLM.from_pretrained(model, device_map="auto", torch_dtype="auto")

messages = [{"role": "user", "content": "What is reinforcement learning?"}]
prompt = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
outputs = model.generate(prompt, max_new_tokens=256, temperature=1.0, top_p=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model = "LLM360/Guru-7B"
tokenizer = AutoTokenizer.from_pretrained(model)
model = AutoModelForCausalLM.from_pretrained(model, device_map="auto", torch_dtype="auto")

messages = [{"role": "user", "content": "What is reinforcement learning?"}]
prompt = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
outputs = model.generate(prompt, max_new_tokens=256, temperature=1.0, top_p=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model = "LLM360/Guru-7B"
tokenizer = AutoTokenizer.from_pretrained(model)
model = AutoModelForCausalLM.from_pretrained(model, device_map="auto", torch_dtype="auto")

messages = [{"role": "user", "content": "What is reinforcement learning?"}]
prompt = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
outputs = model.generate(prompt, max_new_tokens=256, temperature=1.0, top_p=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model = "LLM360/Guru-7B"
tokenizer = AutoTokenizer.from_pretrained(model)
model = AutoModelForCausalLM.from_pretrained(model, device_map="auto", torch_dtype="auto")

messages = [{"role": "user", "content": "What is reinforcement learning?"}]
prompt = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
outputs = model.generate(prompt, max_new_tokens=256, temperature=1.0, top_p=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model = "LLM360/Guru-7B"
tokenizer = AutoTokenizer.from_pretrained(model)
model = AutoModelForCausalLM.from_pretrained(model, device_map="auto", torch_dtype="auto")

messages = [{"role": "user", "content": "What is reinforcement learning?"}]
prompt = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
outputs = model.generate(prompt, max_new_tokens=256, temperature=1.0, top_p=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model = "LLM360/Guru-7B"
tokenizer = AutoTokenizer.from_pretrained(model)
model = AutoModelForCausalLM.from_pretrained(model, device_map="auto", torch_dtype="auto")

messages = [{"role": "user", "content": "What is reinforcement learning?"}]
prompt = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
outputs = model.generate(prompt, max_new_tokens=256, temperature=1.0, top_p=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model = "LLM360/Guru-7B"
tokenizer = AutoTokenizer.from_pretrained(model)
model = AutoModelForCausalLM.from_pretrained(model, device_map="auto", torch_dtype="auto")

messages = [{"role": "user", "content": "What is reinforcement learning?"}]
prompt = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
outputs = model.generate(prompt, max_new_tokens=256, temperature=1.0, top_p=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model = "LLM360/Guru-7B"
tokenizer = AutoTokenizer.from_pretrained(model)
model = AutoModelForCausalLM.from_pretrained(model, device_map="auto", torch_dtype="auto")

messages = [{"role": "user", "content": "What is reinforcement learning?"}]
prompt = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
outputs = model.generate(prompt, max_new_tokens=256, temperature=1.0, top_p=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model = "LLM360/Guru-7B"
tokenizer = AutoTokenizer.from_pretrained(model)
model = AutoModelForCausalLM.from_pretrained(model, device_map="auto", torch_dtype="auto")

messages = [{"role": "user", "content": "What is reinforcement learning?"}]
prompt = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
outputs = model.generate(prompt, max_new_tokens=256, temperature=1.0, top_p=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model = "LLM360/Guru-7B"
tokenizer = AutoTokenizer.from_pretrained(model)
model = AutoModelForCausalLM.from_pretrained(model, device_map="auto", torch_dtype="auto")

messages = [{"role": "user", "content": "What is reinforcement learning?"}]
prompt = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
outputs = model.generate(prompt, max_new_tokens=256, temperature=1.0, top_p=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model = "LLM360/Guru-7B"
tokenizer = AutoTokenizer.from_pretrained(model)
model = AutoModelForCausalLM.from_pretrained(model, device_map="auto", torch_dtype="auto")

messages = [{"role": "user", "content": "What is reinforcement learning?"}]
prompt = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
outputs = model.generate(prompt, max_new_tokens=256, temperature=1.0, top_p=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model = "LLM360/Guru-7B"
tokenizer = AutoTokenizer.from_pretrained(model)
model = AutoModelForCausalLM.from_pretrained(model, device_map="auto", torch_dtype="auto")

messages = [{"role": "user", "content": "What is reinforcement learning?"}]
prompt = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
outputs = model.generate(prompt, max_new_tokens=256, temperature=1.0, top_p=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model = "LLM360/Guru-7B"
tokenizer = AutoTokenizer.from_pretrained(model)
model = AutoModelForCausalLM.from_pretrained(model, device_map="auto", torch_dtype="auto")

messages = [{"role": "user", "content": "What is reinforcement learning?"}]
prompt = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
outputs = model.generate(prompt, max_new_tokens=256, temperature=1.0, top_p=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model = "LLM360/Guru-7B"
tokenizer = AutoTokenizer.from_pretrained(model)
model = AutoModelForCausalLM.from_pretrained(model, device_map="auto", torch_dtype="auto")

messages = [{"role": "user", "content": "What is reinforcement learning?"}]
prompt = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
outputs = model.generate(prompt, max_new_tokens=256, temperature=1.0, top_p=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model = "LLM360/Guru-7B"
tokenizer = AutoTokenizer.from_pretrained(model)
model = AutoModelForCausalLM.from_pretrained(model, device_map="auto", torch_dtype="auto")

messages = [{"role": "user", "content": "What is reinforcement learning?"}]
prompt = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
outputs = model.generate(prompt, max_new_tokens=256, temperature=1.0, top_p=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model = "LLM360/Guru-7B"
tokenizer = AutoTokenizer.from_pretrained(model)
model = AutoModelForCausalLM.from_pretrained(model, device_map="auto", torch_dtype="auto")

messages = [{"role": "user", "content": "What is reinforcement learning?"}]
prompt = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
outputs = model.generate(prompt, max_new_tokens=256, temperature=1.0, top_p=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model = "LLM360/Guru-7B"
tokenizer = AutoTokenizer.from_pretrained(model)
model = AutoModelForCausalLM.from_pretrained(model, device_map="auto", torch_dtype="auto")

messages = [{"role": "user", "content": "What is reinforcement learning?"}]
prompt = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
outputs = model.generate(prompt, max_new_tokens=256, temperature=1.0, top_p=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model = "LLM360/Guru-7B"
tokenizer = AutoTokenizer.from_pretrained(model)
model = AutoModelForCausalLM.from_pretrained(model, device_map="auto", torch_dtype="auto")

messages = [{"role": "user", "content": "What is reinforcement learning?"}]
prompt = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
outputs = model.generate(prompt, max_new_tokens=256, temperature=1.0, top_p=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model = "LLM360/Guru-7B"
tokenizer = AutoTokenizer.from_pretrained(model)
model = AutoModelForCausalLM.from_pretrained(model, device_map="auto", torch_dtype="auto")

messages = [{"role": "user", "content": "What is reinforcement learning?"}]
prompt = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
outputs = model.generate(prompt, max_new_tokens=256, temperature=1.0, top_p=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model = "LLM360/Guru-7B"
tokenizer = AutoTokenizer.from_pretrained(model)
model = AutoModelForCausalLM.from_pretrained(model, device_map="auto", torch_dtype="auto")

messages = [{"role": "user", "content": "What is reinforcement learning?"}]
prompt = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
outputs = model.generate(prompt, max_new_tokens=256, temperature=1.0, top_p=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model = "LLM360/Guru-7B"
tokenizer = AutoTokenizer.from_pretrained(model)
model = AutoModelForCausalLM.from_pretrained(model, device_map="auto", torch_dtype="auto")

messages = [{"role": "user", "content": "What is reinforcement learning?"}]
prompt = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
outputs = model.generate(prompt, max_new_tokens=256, temperature=1.0, top_p=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model = "LLM360/Guru-7B"
tokenizer = AutoTokenizer.from_pretrained(model)
model = AutoModelForCausalLM.from_pretrained(model, device_map="auto", torch_dtype="auto")

messages = [{"role": "user", "content": "What is reinforcement learning?"}]
prompt = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
outputs = model.generate(prompt, max_new_tokens=256, temperature=1.0, top_p=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model = "LLM360/Guru-7B"
tokenizer = AutoTokenizer.from_pretrained(model)
model = AutoModelForCausalLM.from_pretrained(model, device_map="auto", torch_dtype="auto")

messages = [{"role": "user", "content": "What is reinforcement learning?"}]
prompt = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
outputs = model.generate(prompt, max_new_tokens=256, temperature=1.0, top_p=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model = "LLM360/Guru-7B"
tokenizer = AutoTokenizer.from_pretrained(model)
model = AutoModelForCausalLM.from_pretrained(model, device_map="auto", torch_dtype="auto")

messages = [{"role": "user", "content": "What is reinforcement learning?"}]
prompt = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
outputs = model.generate(prompt, max_new_tokens=256, temperature=1.0, top_p=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model = "LLM360/Guru-7B"
tokenizer = AutoTokenizer.from_pretrained(model)
model = AutoModelForCausalLM.from_pretrained(model, device_map="auto", torch_dtype="auto")

messages = [{"role": "user", "content": "What is reinforcement learning?"}]
prompt = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
outputs = model.generate(prompt, max_new_tokens=256, temperature=1.0, top_p=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model = "LLM360/Guru-7B"
tokenizer = AutoTokenizer.from_pretrained(model)
model = AutoModelForCausalLM.from_pretrained(model, device_map="auto", torch_dtype="auto")

messages = [{"role": "user", "content": "What is reinforcement learning?"}]
prompt = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
outputs = model.generate(prompt, max_new_tokens=256, temperature=1.0, top_p=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model = "LLM360/Guru-7B"
tokenizer = AutoTokenizer.from_pretrained(model)
model = AutoModelForCausalLM.from_pretrained(model, device_map="auto", torch_dtype="auto")

messages = [{"role": "user", "content": "What is reinforcement learning?"}]
prompt = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
outputs = model.generate(prompt, max_new_tokens=256, temperature=1.0, top_p=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model = "LLM360/Guru-7B"
tokenizer = AutoTokenizer.from_pretrained(model)
model = AutoModelForCausalLM.from_pretrained(model, device_map="auto", torch_dtype="auto")

messages = [{"role": "user", "content": "What is reinforcement learning?"}]
prompt = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
outputs = model.generate(prompt, max_new_tokens=256, temperature=1.0, top_p=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model = "LLM360/Guru-7B"
tokenizer = AutoTokenizer.from_pretrained(model)
model = AutoModelForCausalLM.from_pretrained(model, device_map="auto", torch_dtype="auto")

messages = [{"role": "user", "content": "What is reinforcement learning?"}]
prompt = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
outputs = model.generate(prompt, max_new_tokens=256, temperature=1.0, top_p=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model = "LLM360/Guru-7B"
tokenizer = AutoTokenizer.from_pretrained(model)
model = AutoModelForCausalLM.from_pretrained(model, device_map="auto", torch_dtype="auto")

messages = [{"role": "user", "content": "What is reinforcement learning?"}]
prompt = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
outputs = model.generate(prompt, max_new_tokens=256, temperature=1.0, top_p=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model = "LLM360/Guru-7B"
tokenizer = AutoTokenizer.from_pretrained(model)
model = AutoModelForCausalLM.from_pretrained(model, device_map="auto", torch_dtype="auto")

messages = [{"role": "user", "content": "What is reinforcement learning?"}]
prompt = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
outputs = model.generate(prompt, max_new_tokens=256, temperature=1.0, top_p=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model = "LLM360/Guru-7B"
tokenizer = AutoTokenizer.from_pretrained(model)
model = AutoModelForCausalLM.from_pretrained(model, device_map="auto", torch_dtype="auto")

messages = [{"role": "user", "content": "What is reinforcement learning?"}]
prompt = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
outputs = model.generate(prompt, max_new_tokens=256, temperature=1.0, top_p=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model = "LLM360/Guru-7B"
tokenizer = AutoTokenizer.from_pretrained(model)
model = AutoModelForCausalLM.from_pretrained(model, device_map="auto", torch_dtype="auto")

messages = [{"role": "user", "content": "What is reinforcement learning?"}]
prompt = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
outputs = model.generate(prompt, max_new_tokens=256, temperature=1.0, top_p=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model = "LLM360/Guru-7B"
tokenizer = AutoTokenizer.from_pretrained(model)
model = AutoModelForCausalLM.from_pretrained(model, device_map="auto", torch_dtype="auto")

messages = [{"role": "user", "content": "What is reinforcement learning?"}]
prompt = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
outputs = model.generate(prompt, max_new_tokens=256, temperature=1.0, top_p=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model = "LLM360/Guru-7B"
tokenizer = AutoTokenizer.from_pretrained(model)
model = AutoModelForCausalLM.from_pretrained(model, device_map="auto", torch_dtype="auto")

messages = [{"role": "user", "content": "What is reinforcement learning?"}]
prompt = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
outputs = model.generate(prompt, max_new_tokens=256, temperature=1.0, top_p=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model = "LLM360/Guru-7B"
tokenizer = AutoTokenizer.from_pretrained(model)
model = AutoModelForCausalLM.from_pretrained(model, device_map="auto", torch_dtype="auto")

messages = [{"role": "user", "content": "What is reinforcement learning?"}]
prompt = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
outputs = model.generate(prompt, max_new_tokens=256, temperature=1.0, top_p=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model = "LLM360/Guru-7B"
tokenizer = AutoTokenizer.from_pretrained(model)
model = AutoModelForCausalLM.from_pretrained(model, device_map="auto", torch_dtype="auto")

messages = [{"role": "user", "content": "What is reinforcement learning?"}]
prompt = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
outputs = model.generate(prompt, max_new_tokens=256, temperature=1.0, top_p=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model = "LLM360/Guru-7B"
tokenizer = AutoTokenizer.from_pretrained(model)
model = AutoModelForCausalLM.from_pretrained(model, device_map="auto", torch_dtype="auto")

messages = [{"role": "user", "content": "What is reinforcement learning?"}]
prompt = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
outputs = model.generate(prompt, max_new_tokens=256, temperature=1.0, top_p=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model = "LLM360/Guru-7B"
tokenizer = AutoTokenizer.from_pretrained(model)
model = AutoModelForCausalLM.from_pretrained(model, device_map="auto", torch_dtype="auto")

messages = [{"role": "user", "content": "What is reinforcement learning?"}]
prompt = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
outputs = model.generate(prompt, max_new_tokens=256, temperature=1.0, top_p=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model = "LLM360/Guru-7B"
tokenizer = AutoTokenizer.from_pretrained(model)
model = AutoModelForCausalLM.from_pretrained(model, device_map="auto", torch_dtype="auto")

messages = [{"role": "user", "content": "What is reinforcement learning?"}]
prompt = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
outputs = model.generate(prompt, max_new_tokens=256, temperature=1.0, top_p=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model = "LLM360/Guru-7B"
tokenizer = AutoTokenizer.from_pretrained(model)
model = AutoModelForCausalLM.from_pretrained(model, device_map="auto", torch_dtype="auto")

messages = [{"role": "user", "content": "What is reinforcement learning?"}]
prompt = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
outputs = model.generate(prompt, max_new_tokens=256, temperature=1.0, top_p=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model = "LLM360/Guru-7B"
tokenizer = AutoTokenizer.from_pretrained(model)
model = AutoModelForCausalLM.from_pretrained(model, device_map="auto", torch_dtype="auto")

messages = [{"role": "user", "content": "What is reinforcement learning?"}]
prompt = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
outputs = model.generate(prompt, max_new_tokens=256, temperature=1.0, top_p=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model = "LLM360/Guru-7B"
tokenizer = AutoTokenizer.from_pretrained(model)
model = AutoModelForCausalLM.from_pretrained(model, device_map="auto", torch_dtype="auto")

messages = [{"role": "user", "content": "What is reinforcement learning?"}]
prompt = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
outputs = model.generate(prompt, max_new_tokens=256, temperature=1.0, top_p=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model = "LLM360/Guru-7B"
tokenizer = AutoTokenizer.from_pretrained(model)
model = AutoModelForCausalLM.from_pretrained(model, device_map="auto", torch_dtype="auto")

messages = [{"role": "user", "content": "What is reinforcement learning?"}]
prompt = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
outputs = model.generate(prompt, max_new_tokens=256, temperature=1.0, top_p=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model = "LLM360/Guru-7B"
tokenizer = AutoTokenizer.from_pretrained(model)
model = AutoModelForCausalLM.from_pretrained(model, device_map="auto", torch_dtype="auto")

messages = [{"role": "user", "content": "What is reinforcement learning?"}]
prompt = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
outputs = model.generate(prompt, max_new_tokens=256, temperature=1.0, top_p=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model = "LLM360/Guru-7B"
tokenizer = AutoTokenizer.from_pretrained(model)
model = AutoModelForCausalLM.from_pretrained(model, device_map="auto", torch_dtype="auto")

messages = [{"role": "user", "content": "What is reinforcement learning?"}]
prompt = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
outputs = model.generate(prompt, max_new_tokens=256, temperature=1.0, top_p=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model = "LLM360/Guru-7B"
tokenizer = AutoTokenizer.from_pretrained(model)
model = AutoModelForCausalLM.from_pretrained(model, device_map="auto", torch_dtype="auto")

messages = [{"role": "user", "content": "What is reinforcement learning?"}]
prompt = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
outputs = model.generate(prompt, max_new_tokens=256, temperature=1.0, top_p=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model = "LLM360/Guru-7B"
tokenizer = AutoTokenizer.from_pretrained(model)
model = AutoModelForCausalLM.from_pretrained(model, device_map="auto", torch_dtype="auto")

messages = [{"role": "user", "content": "What is reinforcement learning?"}]
prompt = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
outputs = model.generate(prompt, max_new_tokens=256, temperature=1.0, top_p=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model = "LLM360/Guru-7B"
tokenizer = AutoTokenizer.from_pretrained(model)
model = AutoModelForCausalLM.from_pretrained(model, device_map="auto", torch_dtype="auto")

messages = [{"role": "user", "content": "What is reinforcement learning?"}]
prompt = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
outputs = model.generate(prompt, max_new_tokens=256, temperature=1.0, top_p=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model = "LLM360/Guru-7B"
tokenizer = AutoTokenizer.from_pretrained(model)
model = AutoModelForCausalLM.from_pretrained(model, device_map="auto", torch_dtype="auto")

messages = [{"role": "user", "content": "What is reinforcement learning?"}]
prompt = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
outputs = model.generate(prompt, max_new_tokens=256, temperature=1.0, top_p=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model = "LLM360/Guru-7B"
tokenizer = AutoTokenizer.from_pretrained(model)
model = AutoModelForCausalLM.from_pretrained(model, device_map="auto", torch_dtype="auto")

messages = [{"role": "user", "content": "What is reinforcement learning?"}]
prompt = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
outputs = model.generate(prompt, max_new_tokens=256, temperature=1.0, top_p=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model = "LLM360/Guru-7B"
tokenizer = AutoTokenizer.from_pretrained(model)
model = AutoModelForCausalLM.from_pretrained(model, device_map="auto", torch_dtype="auto")

messages = [{"role": "user", "content": "What is reinforcement learning?"}]
prompt = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
outputs = model.generate(prompt, max_new_tokens=256, temperature=1.0, top_p=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model = "LLM360/Guru-7B"
tokenizer = AutoTokenizer.from_pretrained(model)
model = AutoModelForCausalLM.from_pretrained(model, device_map="auto", torch_dtype="auto")

messages = [{"role": "user", "content": "What is reinforcement learning?"}]
prompt = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
outputs = model.generate(prompt, max_new_tokens=256, temperature=1.0, top_p=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model = "LLM360/Guru-7B"
tokenizer = AutoTokenizer.from_pretrained(model)
model = AutoModelForCausalLM.from_pretrained(model, device_map="auto", torch_dtype="auto")

messages = [{"role": "user", "content": "What is reinforcement learning?"}]
prompt = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
outputs = model.generate(prompt, max_new_tokens=256, temperature=1.0, top_p=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model = "LLM360/Guru-7B"
tokenizer = AutoTokenizer.from_pretrained(model)
model = AutoModelForCausalLM.from_pretrained(model, device_map="auto", torch_dtype="auto")

messages = [{"role": "user", "content": "What is reinforcement learning?"}]
prompt = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
outputs = model.generate(prompt, max_new_tokens=256, temperature=1.0, top_p=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model = "LLM360/Guru-7B"
tokenizer = AutoTokenizer.from_pretrained(model)
model = AutoModelForCausalLM.from_pretrained(model, device_map="auto", torch_dtype="auto")

messages = [{"role": "user", "content": "What is reinforcement learning?"}]
prompt = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
outputs = model.generate(prompt, max_new_tokens=256, temperature=1.0, top_p=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model = "LLM360/Guru-7B"
tokenizer = AutoTokenizer.from_pretrained(model)
model = AutoModelForCausalLM.from_pretrained(model, device_map="auto", torch_dtype="auto")

messages = [{"role": "user", "content": "What is reinforcement learning?"}]
prompt = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
outputs = model.generate(prompt, max_new_tokens=256, temperature=1.0, top_p=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model = "LLM360/Guru-7B"
tokenizer = AutoTokenizer.from_pretrained(model)
model = AutoModelForCausalLM.from_pretrained(model, device_map="auto", torch_dtype="auto")

messages = [{"role": "user", "content": "What is reinforcement learning?"}]
prompt = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
outputs = model.generate(prompt, max_new_tokens=256, temperature=1.0, top_p=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model = "LLM360/Guru-7B"
tokenizer = AutoTokenizer.from_pretrained(model)
model = AutoModelForCausalLM.from_pretrained(model, device_map="auto", torch_dtype="auto")

messages = [{"role": "user", "content": "What is reinforcement learning?"}]
prompt = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
outputs = model.generate(prompt, max_new_tokens=256, temperature=1.0, top_p=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model = "LLM360/Guru-7B"
tokenizer = AutoTokenizer.from_pretrained(model)
model = AutoModelForCausalLM.from_pretrained(model, device_map="auto", torch_dtype="auto")

messages = [{"role": "user", "content": "What is reinforcement learning?"}]
prompt = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
outputs = model.generate(prompt, max_new_tokens=256, temperature=1.0, top_p=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model = "LLM360/Guru-7B"
tokenizer = AutoTokenizer.from_pretrained(model)
model = AutoModelForCausalLM.from_pretrained(model, device_map="auto", torch_dtype="auto")

messages = [{"role": "user", "content": "What is reinforcement learning?"}]
prompt = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
outputs = model.generate(prompt, max_new_tokens=256, temperature=1.0, top_p=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model = "LLM360/Guru-7B"
tokenizer = AutoTokenizer.from_pretrained(model)
model = AutoModelForCausalLM.from_pretrained(model, device_map="auto", torch_dtype="auto")

messages = [{"role": "user", "content": "What is reinforcement learning?"}]
prompt = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
outputs = model.generate(prompt, max_new_tokens=256, temperature=1.0, top_p=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model = "LLM360/Guru-7B"
tokenizer = AutoTokenizer.from_pretrained(model)
model = AutoModelForCausalLM.from_pretrained(model, device_map="auto", torch_dtype="auto")

messages = [{"role": "user", "content": "What is reinforcement learning?"}]
prompt = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
outputs = model.generate(prompt, max_new_tokens=256, temperature=1.0, top_p=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model = "LLM360/Guru-7B"
tokenizer = AutoTokenizer.from_pretrained(model)
model = AutoModelForCausalLM.from_pretrained(model, device_map="auto", torch_dtype="auto")

messages = [{"role": "user", "content": "What is reinforcement learning?"}]
prompt = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
outputs = model.generate(prompt, max_new_tokens=256, temperature=1.0, top_p=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model = "LLM360/Guru-7B"
tokenizer = AutoTokenizer.from_pretrained(model)
model = AutoModelForCausalLM.from_pretrained(model, device_map="auto", torch_dtype="auto")

messages = [{"role": "user", "content": "What is reinforcement learning?"}]
prompt = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
outputs = model.generate(prompt, max_new_tokens=256, temperature=1.0, top_p=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model = "LLM360/Guru-7B"
tokenizer = AutoTokenizer.from_pretrained(model)
model = AutoModelForCausalLM.from_pretrained(model, device_map="auto", torch_dtype="auto")

messages = [{"role": "user", "content": "What is reinforcement learning?"}]
prompt = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
outputs = model.generate(prompt, max_new_tokens=256, temperature=1.0, top_p=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model = "LLM360/Guru-7B"
tokenizer = AutoTokenizer.from_pretrained(model)
model = AutoModelForCausalLM.from_pretrained(model, device_map="auto", torch_dtype="auto")

messages = [{"role": "user", "content": "What is reinforcement learning?"}]
prompt = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
outputs = model.generate(prompt, max_new_tokens=256, temperature=1.0, top_p=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model = "LLM360/Guru-7B"
tokenizer = AutoTokenizer.from_pretrained(model)
model = AutoModelForCausalLM.from_pretrained(model, device_map="auto", torch_dtype="auto")

messages = [{"role": "user", "content": "What is reinforcement learning?"}]
prompt = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
outputs = model.generate(prompt, max_new_tokens=256, temperature=1.0, top_p=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model = "LLM360/Guru-7B"
tokenizer = AutoTokenizer.from_pretrained(model)
model = AutoModelForCausalLM.from_pretrained(model, device_map="auto", torch_dtype="auto")

messages = [{"role": "user", "content": "What is reinforcement learning?"}]
prompt = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
outputs = model.generate(prompt, max_new_tokens=256, temperature=1.0, top_p=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model = "LLM360/Guru-7B"
tokenizer = AutoTokenizer.from_pretrained(model)
model = AutoModelForCausalLM.from_pretrained(model, device_map="auto", torch_dtype="auto")

messages = [{"role": "user", "content": "What is reinforcement learning?"}]
prompt = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
outputs = model.generate(prompt, max_new_tokens=256, temperature=1.0, top_p=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model = "LLM360/Guru-7B"
tokenizer = AutoTokenizer.from_pretrained(model)
model = AutoModelForCausalLM.from_pretrained(model, device_map="auto", torch_dtype="auto")

messages = [{"role": "user", "content": "What is reinforcement learning?"}]
prompt = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
outputs = model.generate(prompt, max_new_tokens=256, temperature=1.0, top_p=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model = "LLM360/Guru-7B"
tokenizer = AutoTokenizer.from_pretrained(model)
model = AutoModelForCausalLM.from_pretrained(model, device_map="auto", torch_dtype="auto")

messages = [{"role": "user", "content": "What is reinforcement learning?"}]
prompt = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
outputs = model.generate(prompt, max_new_tokens=256, temperature=1.0, top_p=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model = "LLM360/Guru-7B"
tokenizer = AutoTokenizer.from_pretrained(model)
model = AutoModelForCausalLM.from_pretrained(model, device_map="auto", torch_dtype="auto")

messages = [{"role": "user", "content": "What is reinforcement learning?"}]
prompt = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
outputs = model.generate(prompt, max_new_tokens=256, temperature=1.0, top_p=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model = "LLM360/Guru-7B"
tokenizer = AutoTokenizer.from_pretrained(model)
model = AutoModelForCausalLM.from_pretrained(model, device_map="auto", torch_dtype="auto")

messages = [{"role": "user", "content": "What is reinforcement learning?"}]
prompt = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
outputs = model.generate(prompt, max_new_tokens=256, temperature=1.0, top_p=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model = "LLM360/Guru-7B"
tokenizer = AutoTokenizer.from_pretrained(model)
model = AutoModelForCausalLM.from_pretrained(model, device_map="auto", torch_dtype="auto")

messages = [{"role": "user", "content": "What is reinforcement learning?"}]
prompt = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
outputs = model.generate(prompt, max_new_tokens=256, temperature=1.0, top_p=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model = "LLM360/Guru-7B"
tokenizer = AutoTokenizer.from_pretrained(model)
model = AutoModelForCausalLM.from_pretrained(model, device_map="auto", torch_dtype="auto")

messages = [{"role": "user", "content": "What is reinforcement learning?"}]
prompt = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
outputs = model.generate(prompt, max_new_tokens=256, temperature=1.0, top_p=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model = "LLM360/Guru-7B"
tokenizer = AutoTokenizer.from_pretrained(model)
model = AutoModelForCausalLM.from_pretrained(model, device_map="auto", torch_dtype="auto")

messages = [{"role": "user", "content": "What is reinforcement learning?"}]
prompt = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
outputs = model.generate(prompt, max_new_tokens=256, temperature=1.0, top_p=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model = "LLM360/Guru-7B"
tokenizer = AutoTokenizer.from_pretrained(model)
model = AutoModelForCausalLM.from_pretrained(model, device_map="auto", torch_dtype="auto")

messages = [{"role": "user", "content": "What is reinforcement learning?"}]
prompt = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
outputs = model.generate(prompt, max_new_tokens=256, temperature=1.0, top_p=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model = "LLM360/Guru-7B"
tokenizer = AutoTokenizer.from_pretrained(model)
model = AutoModelForCausalLM.from_pretrained(model, device_map="auto", torch_dtype="auto")

messages = [{"role": "user", "content": "What is reinforcement learning?"}]
prompt = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
outputs = model.generate(prompt, max_new_tokens=256, temperature=1.0, top_p=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model = "LLM360/Guru-7B"
tokenizer = AutoTokenizer.from_pretrained(model)
model = AutoModelForCausalLM.from_pretrained(model, device_map="auto", torch_dtype="auto")

messages = [{"role": "user", "content": "What is reinforcement learning?"}]
prompt = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
outputs = model.generate(prompt, max_new_tokens=256, temperature=1.0, top_p=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model = "LLM360/Guru-7B"
tokenizer = AutoTokenizer.from_pretrained(model)
model = AutoModelForCausalLM.from_pretrained(model, device_map="auto", torch_dtype="auto")

messages = [{"role": "user", "content": "What is reinforcement learning?"}]
prompt = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
outputs = model.generate(prompt, max_new_tokens=256, temperature=1.0, top_p=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model = "LLM360/Guru-7B"
tokenizer = AutoTokenizer.from_pretrained(model)
model = AutoModelForCausalLM.from_pretrained(model, device_map="auto", torch_dtype="auto")

messages = [{"role": "user", "content": "What is reinforcement learning?"}]
prompt = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
outputs = model.generate(prompt, max_new_tokens=256, temperature=1.0, top_p=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model = "LLM360/Guru-7B"
tokenizer = AutoTokenizer.from_pretrained(model)
model = AutoModelForCausalLM.from_pretrained(model, device_map="auto", torch_dtype="auto")

messages = [{"role": "user", "content": "What is reinforcement learning?"}]
prompt = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
outputs = model.generate(prompt, max_new_tokens=256, temperature=1.0, top_p=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model = "LLM360/Guru-7B"
tokenizer = AutoTokenizer.from_pretrained(model)
model = AutoModelForCausalLM.from_pretrained(model, device_map="auto", torch_dtype="auto")

messages = [{"role": "user", "content": "What is reinforcement learning?"}]
prompt = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
outputs = model.generate(prompt, max_new_tokens=256, temperature=1.0, top_p=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model = "LLM360/Guru-7B"
tokenizer = AutoTokenizer.from_pretrained(model)
model = AutoModelForCausalLM.from_pretrained(model, device_map="auto", torch_dtype="auto")

messages = [{"role": "user", "content": "What is reinforcement learning?"}]
prompt = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
outputs = model.generate(prompt, max_new_tokens=256, temperature=1.0, top_p=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model = "LLM360/Guru-7B"
tokenizer = AutoTokenizer.from_pretrained(model)
model = AutoModelForCausalLM.from_pretrained(model, device_map="auto", torch_dtype="auto")

messages = [{"role": "user", "content": "What is reinforcement learning?"}]
prompt = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
outputs = model.generate(prompt, max_new_tokens=256, temperature=1.0, top_p=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model = "LLM360/Guru-7B"
tokenizer = AutoTokenizer.from_pretrained(model)
model = AutoModelForCausalLM.from_pretrained(model, device_map="auto", torch_dtype="auto")

messages = [{"role": "user", "content": "What is reinforcement learning?"}]
prompt = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
outputs = model.generate(prompt, max_new_tokens=256, temperature=1.0, top_p=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model = "LLM360/Guru-7B"
tokenizer = AutoTokenizer.from_pretrained(model)
model = AutoModelForCausalLM.from_pretrained(model, device_map="auto", torch_dtype="auto")

messages = [{"role": "user", "content": "What is reinforcement learning?"}]
prompt = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
outputs = model.generate(prompt, max_new_tokens=256, temperature=1.0, top_p=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model = "LLM360/Guru-7B"
tokenizer = AutoTokenizer.from_pretrained(model)
model = AutoModelForCausalLM.from_pretrained(model, device_map="auto", torch_dtype="auto")

messages = [{"role": "user", "content": "What is reinforcement learning?"}]
prompt = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
outputs = model.generate(prompt, max_new_tokens=256, temperature=1.0, top_p=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
pythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model = "LLM360/Guru-7B"
tokenizer = AutoTokenizer.from_pretrained(model)
model = AutoModelForCausalLM.from_pretrained(model, device_map="auto", torch_dtype="auto")

messages = [{"role": "user", "content": "What is reinforcement learning?"}]
prompt = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
outputs = model.generate(prompt, max_new_tokens=256, temperature=1.0, top_p=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

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