piotr25691
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README.md
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---
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base_model: chuanli11/Llama-3.2-3B-Instruct-uncensored
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datasets:
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- KingNish/reasoning-base-20k
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language:
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- en
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license: llama3.2
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tags:
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- text-generation-inference
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- transformers
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- llama
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- trl
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- sft
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- reasoning
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- llama-3
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---
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# Model Description
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A work in progress uncensored reasoning Llama 3.2 3B model trained on reasoning data.
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Since I used different training code, it is unknown whether it generates the same kind of reasoning.
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Here is what inference code you should use:
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```py
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from transformers import AutoModelForCausalLM, AutoTokenizer
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MAX_REASONING_TOKENS = 1024
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MAX_RESPONSE_TOKENS = 512
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model_name = "piotr25691/thea-3b-25r"
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model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype="auto", device_map="auto")
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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prompt = "Which is greater 9.9 or 9.11 ??"
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messages = [
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{"role": "user", "content": prompt}
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]
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# Generate reasoning
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reasoning_template = tokenizer.apply_chat_template(messages, tokenize=False, add_reasoning_prompt=True)
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reasoning_inputs = tokenizer(reasoning_template, return_tensors="pt").to(model.device)
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reasoning_ids = model.generate(**reasoning_inputs, max_new_tokens=MAX_REASONING_TOKENS)
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reasoning_output = tokenizer.decode(reasoning_ids[0, reasoning_inputs.input_ids.shape[1]:], skip_special_tokens=True)
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# print("REASONING: " + reasoning_output)
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# Generate answer
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messages.append({"role": "reasoning", "content": reasoning_output})
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response_template = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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response_inputs = tokenizer(response_template, return_tensors="pt").to(model.device)
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response_ids = model.generate(**response_inputs, max_new_tokens=MAX_RESPONSE_TOKENS)
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response_output = tokenizer.decode(response_ids[0, response_inputs.input_ids.shape[1]:], skip_special_tokens=True)
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print("ANSWER: " + response_output)
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```
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- **Trained by:** [Piotr Zalewski](https://huggingface.co/piotr25691)
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- **License:** llama3.2
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- **Finetuned from model:** [chuanli11/Llama-3.2-3B-Instruct-uncensored](https://huggingface.co/chuanli11/Llama-3.2-3B-Instruct-uncensored)
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- **Dataset used:** [KingNish/reasoning-base-20k](https://huggingface.co/datasets/KingNish/reasoning-base-20k)
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This Llama model was trained faster than [Unsloth](https://github.com/unslothai/unsloth) using [custom training code](https://www.kaggle.com/code/piotr25691/distributed-llama-training-with-2xt4?scriptVersionId=200492023).
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Visit https://www.kaggle.com/code/piotr25691/distributed-llama-training-with-2xt4?scriptVersionId=200492023 to find out how you can finetune your models using BOTH of the Kaggle provided GPUs.
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