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---
base_model: chuanli11/Llama-3.2-3B-Instruct-uncensored
datasets:
- KingNish/reasoning-base-20k
language:
- en
license: llama3.2
tags:
- text-generation-inference
- transformers
- llama
- trl
- sft
- reasoning
- llama-3
---
# Model Description
A work in progress uncensored reasoning Llama 3.2 3B model trained on reasoning data.
Since I used different training code, it is unknown whether it generates the same kind of reasoning.
Here is what inference code you should use:
```py
from transformers import AutoModelForCausalLM, AutoTokenizer
MAX_REASONING_TOKENS = 1024
MAX_RESPONSE_TOKENS = 512
model_name = "piotr25691/thea-3b-25r"
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:** [Piotr Zalewski](https://huggingface.co/piotr25691)
- **License:** llama3.2
- **Finetuned from model:** [chuanli11/Llama-3.2-3B-Instruct-uncensored](https://huggingface.co/chuanli11/Llama-3.2-3B-Instruct-uncensored)
- **Dataset used:** [KingNish/reasoning-base-20k](https://huggingface.co/datasets/KingNish/reasoning-base-20k)
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).
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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