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QuantFactory / Reasoning-Llama-1b-v0.1-GGUF
Model's Last Updated: October 07 2024
Introduction of Reasoning-Llama-1b-v0.1-GGUF
Model Details of Reasoning-Llama-1b-v0.1-GGUF
QuantFactory/Reasoning-Llama-1b-v0.1-GGUF
This is quantized version of KingNish/Reasoning-Llama-1b-v0.1 created using llama.cpp
Original Model Card
Model Dexcription
It's First iteration of this model. For testing purpose its just trained on 10k rows. It performed very well than expected. It do first reasoning and than generate response on based on it but it do like o1. It do reasoning separately (Just like o1), no tags (like reflection). Below is inference code.
from transformers import AutoModelForCausalLM, AutoTokenizer
MAX_REASONING_TOKENS = 1024
MAX_RESPONSE_TOKENS = 512
model_name = "KingNish/Reasoning-Llama-1b-v0.1"
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype="auto", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained(model_name)
prompt = "Which is greater 9.9 or 9.11 ??"
messages = [
{"role": "user", "content": prompt}
]
# Generate reasoning
reasoning_template = tokenizer.apply_chat_template(messages, tokenize=False, add_reasoning_prompt=True)
reasoning_inputs = tokenizer(reasoning_template, return_tensors="pt").to(model.device)
reasoning_ids = model.generate(**reasoning_inputs, max_new_tokens=MAX_REASONING_TOKENS)
reasoning_output = tokenizer.decode(reasoning_ids[0, reasoning_inputs.input_ids.shape[1]:], skip_special_tokens=True)
# print("REASONING: " + reasoning_output)
# Generate answer
messages.append({"role": "reasoning", "content": reasoning_output})
response_template = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
response_inputs = tokenizer(response_template, return_tensors="pt").to(model.device)
response_ids = model.generate(**response_inputs, max_new_tokens=MAX_RESPONSE_TOKENS)
response_output = tokenizer.decode(response_ids[0, response_inputs.input_ids.shape[1]:], skip_special_tokens=True)
print("ANSWER: " + response_output)
- Trained by: Nishith Jain
- License: llama3.2
- Finetuned from model : meta-llama/Llama-3.2-1B-Instruct
- Dataset used : KingNish/reasoning-base-20k
This llama model was trained 2x faster with Unsloth and Huggingface's TRL library.
Runs of QuantFactory Reasoning-Llama-1b-v0.1-GGUF on huggingface.co
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More Information About Reasoning-Llama-1b-v0.1-GGUF huggingface.co Model
Url of Reasoning-Llama-1b-v0.1-GGUF
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Provider of Reasoning-Llama-1b-v0.1-GGUF huggingface.co
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Updated:July 26 2024
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