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CreitinGameplays/Llama-3.2-3B-Instruct-R1-v1
Llama-3.2-3B-Instruct-R1-v1 is a text generation model from CreitinGameplays. Use it when you need the model to write or continue text. It is set up for transformers. The card lists the license as mit.
Downloads · 30 days
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.safetensors6.4 GB · 100%
From the Hugging Face model README
Chat template format:
<|start_header_id|>system<|end_header_id|>
You are focused on providing systematic, well-reasoned responses. Response Structure: - Format: <think>{{reasoning}}</think>{{answer}} - Reasoning: Minimum 6 logical steps only when it required in <think> block - Process: Think first, then answer.
You are a helpful AI assistant named Llama, made by Meta AI.
<|eot_id|><|start_header_id|>user<|end_header_id|>
How many r's are in strawberry?<|eot_id|><|start_header_id|>assistant<|end_header_id|><think>
Run this model:
# test the model
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, TextStreamer
def main():
model_id = "CreitinGameplays/Llama-3.2-3B-Instruct-R1-v1"
# Load the tokenizer.
tokenizer = AutoTokenizer.from_pretrained(model_id, add_eos_token=True)
# Load the model using bitsandbytes 8-bit quantization if CUDA is available.
if torch.cuda.is_available():
model = AutoModelForCausalLM.from_pretrained(
model_id,
load_in_8bit=True,
device_map="auto"
)
device = torch.device("cuda")
else:
model = AutoModelForCausalLM.from_pretrained(model_id)
device = torch.device("cpu")
# Define the generation parameters.
generation_kwargs = {
"max_new_tokens": 2048,
"do_sample": True,
"temperature": 0.5,
"top_p": 0.9,
"repetition_penalty": 1.1,
"num_return_sequences": 1,
"forced_eos_token_id": tokenizer.eos_token_id,
"pad_token_id": tokenizer.eos_token_id
}
print("Enter your prompt (type 'exit' to quit):")
while True:
# Get user input.
user_input = input("Input> ")
if user_input.lower().strip() in ("exit", "quit"):
break
# Construct the prompt in your desired format.
prompt = f"""
<|start_header_id|>system<|end_header_id|>
You are focused on providing systematic, well-reasoned responses. Response Structure: - Format: <think>{{reasoning}}</think>{{answer}} - Reasoning: Minimum 6 logical steps only when it required in <think> block - Process: Think first, then answer.
You are a helpful AI assistant named Llama, made by Meta AI.
<|eot_id|><|start_header_id|>user<|end_header_id|>
How many r's are in strawberry?<|eot_id|><|start_header_id|>assistant<|end_header_id|><think>
"""
# Tokenize the prompt and send to the selected device.
input_ids = tokenizer.encode(prompt, return_tensors="pt", add_special_tokens=True).to(device)
# Create a new TextStreamer instance for streaming responses.
streamer = TextStreamer(tokenizer)
generation_kwargs["streamer"] = streamer
print("\nAssistant Response:")
# Generate the text (tokens will stream to stdout via the streamer).
outputs = model.generate(input_ids, **generation_kwargs)
if __name__ == "__main__":
main()
Or alternatively:
import torch
from transformers import pipeline
model_id = "CreitinGameplays/Llama-3.2-3B-Instruct-R1-v1"
pipe = pipeline(
"text-generation",
model=model_id,
torch_dtype=torch.bfloat16,
device_map="auto"
)
messages = [{"role": "user", "content": "hello there!"}]
outputs = pipe(
messages,
temperature=0.5,
repetition_penalty=1.1,
max_new_tokens=2048
)
print(outputs[0]["generated_text"][-1])