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.
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 and Huggingface's TRL library.

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

Detailed results can be found here

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