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README.md

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+
+---
+
+base_model: unsloth/llama-3.2-3b-instruct-bnb-4bit
+language:
+- en
+license: apache-2.0
+tags:
+- text-generation-inference
+- transformers
+- unsloth
+- llama
+- trl
+- sft
+datasets:
+- Lyte/Reasoning-Paused
+pipeline_tag: text-generation
+
+---
+
+[![QuantFactory Banner](https://lh7-rt.googleusercontent.com/docsz/AD_4nXeiuCm7c8lEwEJuRey9kiVZsRn2W-b4pWlu3-X534V3YmVuVc2ZL-NXg2RkzSOOS2JXGHutDuyyNAUtdJI65jGTo8jT9Y99tMi4H4MqL44Uc5QKG77B0d6-JfIkZHFaUA71-RtjyYZWVIhqsNZcx8-OMaA?key=xt3VSDoCbmTY7o-cwwOFwQ)](https://hf.co/QuantFactory)
+
+
+# QuantFactory/Llama-3.2-3B-Overthinker-GGUF
+This is quantized version of [Lyte/Llama-3.2-3B-Overthinker](https://huggingface.co/Lyte/Llama-3.2-3B-Overthinker) created using llama.cpp
+
+# Original Model Card
+
+
+
+# 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)