README.md

---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- sft
base_model: unsloth/llama-3.2-3b-instruct-bnb-4bit
datasets:
- Lyte/Reasoning-Paused
pipeline_tag: text-generation
model-index:
- name: Llama-3.2-3B-Overthinker
  results:
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: IFEval (0-Shot)
      type: HuggingFaceH4/ifeval
      args:
        num_few_shot: 0
    metrics:
    - type: inst_level_strict_acc and prompt_level_strict_acc
      value: 64.08
      name: strict accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Lyte/Llama-3.2-3B-Overthinker
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: BBH (3-Shot)
      type: BBH
      args:
        num_few_shot: 3
    metrics:
    - type: acc_norm
      value: 20.1
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Lyte/Llama-3.2-3B-Overthinker
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MATH Lvl 5 (4-Shot)
      type: hendrycks/competition_math
      args:
        num_few_shot: 4
    metrics:
    - type: exact_match
      value: 2.64
      name: exact match
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Lyte/Llama-3.2-3B-Overthinker
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: GPQA (0-shot)
      type: Idavidrein/gpqa
      args:
        num_few_shot: 0
    metrics:
    - type: acc_norm
      value: 1.23
      name: acc_norm
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Lyte/Llama-3.2-3B-Overthinker
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MuSR (0-shot)
      type: TAUR-Lab/MuSR
      args:
        num_few_shot: 0
    metrics:
    - type: acc_norm
      value: 3.9
      name: acc_norm
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Lyte/Llama-3.2-3B-Overthinker
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MMLU-PRO (5-shot)
      type: TIGER-Lab/MMLU-Pro
      config: main
      split: test
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 22.06
      name: accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Lyte/Llama-3.2-3B-Overthinker
      name: Open LLM Leaderboard
---


# Model Overview:

- **Training Data**: This model was trained on a dataset with columns for initial reasoning, step-by-step thinking, verifications after each step, and final answers based on full context. Is it better than the original base model? Hard to say without proper evaluations, and I don’t have the resources to run them manually.
  
- **Context Handling**: The model benefits from larger contexts (minimum 4k up to 16k tokens, though it was trained on 32k tokens). It tends to "overthink," so providing a longer context helps it perform better.

- **Performance**: Based on my very few manual tests, the model seems to excel in conversational settings—especially for mental health, creative tasks and explaining stuff. However, I encourage you to try it out yourself using this [Colab Notebook](https://colab.research.google.com/drive/1dcBbHAwYJuQJKqdPU570Hddv_F9wzjPO?usp=sharing).

- **Dataset Note**: The publicly available dataset is only a partial version. The full dataset was originally designed for a custom Mixture of Experts (MoE) architecture, but I couldn't afford to run the full experiment.

- **Acknowledgment**: Special thanks to KingNish for reigniting my passion to revisit this project. I almost abandoned it after my first attempt a month ago. Enjoy this experimental model!

# Inference Code:

- Feel free to make the steps and verifications collapsable and the initial reasoning too, you can show only the final answer to get an o1 feel(i don't know)
- **Note:** A feature we have here is the ability to control how many steps and verifications you want.

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "Lyte/Llama-3.2-3B-Overthinker"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype="auto", device_map="auto")

def generate_response(prompt, max_tokens=16384, temperature=0.8, top_p=0.95, repeat_penalty=1.1, num_steps=3):
    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_tokens // 3,
        temperature=temperature,
        top_p=top_p,
        repetition_penalty=repeat_penalty
    )
    reasoning_output = tokenizer.decode(reasoning_ids[0, reasoning_inputs.input_ids.shape[1]:], skip_special_tokens=True)
    
    # Generate thinking (step-by-step and verifications)
    messages.append({"role": "reasoning", "content": reasoning_output})
    thinking_template = tokenizer.apply_chat_template(messages, tokenize=False, add_thinking_prompt=True, num_steps=num_steps)
    thinking_inputs = tokenizer(thinking_template, return_tensors="pt").to(model.device)

    thinking_ids = model.generate(
        **thinking_inputs,
        max_new_tokens=max_tokens // 3,
        temperature=temperature,
        top_p=top_p,
        repetition_penalty=repeat_penalty
    )
    thinking_output = tokenizer.decode(thinking_ids[0, thinking_inputs.input_ids.shape[1]:], skip_special_tokens=True)
    
    # Generate final answer
    messages.append({"role": "thinking", "content": thinking_output})
    answer_template = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
    answer_inputs = tokenizer(answer_template, return_tensors="pt").to(model.device)

    answer_ids = model.generate(
        **answer_inputs,
        max_new_tokens=max_tokens // 3,
        temperature=temperature,
        top_p=top_p,
        repetition_penalty=repeat_penalty
    )
    answer_output = tokenizer.decode(answer_ids[0, answer_inputs.input_ids.shape[1]:], skip_special_tokens=True)
    return reasoning_output, thinking_output, answer_output

# Example usage:
prompt = "Explain the process of photosynthesis."
response = generate_response(prompt, num_steps=5)

print("Response:", response)
```

# Uploaded  model

- **Developed by:** Lyte
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3.2-3b-instruct-bnb-4bit

This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.

[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

# Notice:

- **The problem with runnning evals is that they won't make use of the correct template and it won't be a true eval then would it? so these barely test the model.**

# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_Lyte__Llama-3.2-3B-Overthinker)

|      Metric       |Value|
|-------------------|----:|
|Avg.               |19.00|
|IFEval (0-Shot)    |64.08|
|BBH (3-Shot)       |20.10|
|MATH Lvl 5 (4-Shot)| 2.64|
|GPQA (0-shot)      | 1.23|
|MuSR (0-shot)      | 3.90|
|MMLU-PRO (5-shot)  |22.06|