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Model: nnsohamnn/Qwen2.5-3B-ReTrace-OpenO1-Merged Source: Original Platform
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README.md
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README.md
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---
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license: apache-2.0
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base_model: Qwen/Qwen2.5-3B-Instruct
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tags:
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- reasoning
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- chain-of-thought
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- thinking
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- qwen2.5
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- merged-model
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- retrace
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- openo1
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datasets:
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- nnsohamnn/ReTrace501-v1
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- O1-OPEN/OpenO1-SFT
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language:
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- en
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pipeline_tag: text-generation
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---
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# 🧠 Qwen2.5-3B-Instruct ReTrace-OpenO1 Merged
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<div align="center">
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||||
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||||
[](https://huggingface.co/nnsohamnn/Qwen2.5-3B-ReTrace-OpenO1-Merged)
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[](https://huggingface.co/nnsohamnn/Qwen2.5-3B-ReTrace-OpenO1-5k-QLoRA)
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[](https://huggingface.co/Qwen/Qwen2.5-3B-Instruct)
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[](LICENSE)
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**A reasoning-focused model trained on 5,000 chain-of-thought examples**
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|
||||
[🚀 Try Demo](https://huggingface.co/spaces/nnsohamnn/Qwen-2.5-3b-Think-QLora) • [📊 Dataset ReTrace](https://huggingface.co/datasets/nnsohamnn/ReTrace501-v1) • [📊 Dataset OpenO1](https://huggingface.co/datasets/O1-OPEN/OpenO1-SFT)
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</div>
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---
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||||
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## 📋 Model Description
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||||
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This is a **fully merged model** of Qwen2.5-3B-Instruct fine-tuned with LoRA on 5,000 reasoning samples (500 ReTrace + 4,500 OpenO1-SFT). The model generates structured reasoning with explicit `<Thought>` and `<Output>` tags, demonstrating enhanced step-by-step problem-solving capabilities.
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### 🎯 Key Features
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||||
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- ✅ **Fully Merged**: Ready-to-use model (no adapter loading needed)
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||||
- ✅ **Structured Reasoning**: Outputs thinking in `<Thought>` tags, final answer in `<Output>` tags
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||||
- ✅ **5K Training Samples**: 500 ReTrace + 4,500 OpenO1-SFT examples
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- ✅ **Multi-Domain**: Math, logic, word problems, and general reasoning
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||||
- ✅ **Production Ready**: FP16, 6GB model size
|
||||
|
||||
---
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||||
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||||
## 📊 Training Loss
|
||||
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||||
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||||
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||||
### 📈 Training Statistics
|
||||
|
||||
| Metric | Value |
|
||||
|--------|-------|
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||||
| **Initial Loss** | 1.3374 |
|
||||
| **Final Loss** | 0.6798 |
|
||||
| **Best Loss** | 0.6662 (Step 240) |
|
||||
| **Improvement** | 49.2% ↓ |
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||||
| **Total Steps** | 310 |
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||||
|
||||
---
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||||
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||||
## ⚙️ Training Configuration
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||||
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||||
```
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||||
# Model
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BASE_MODEL = "Qwen/Qwen2.5-3B-Instruct"
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MAX_SEQ_LENGTH = 4096
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# LoRA
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LORA_R = 64
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LORA_ALPHA = 128
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LORA_DROPOUT = 0.05
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# Training
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BATCH_SIZE = 8
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GRADIENT_ACCUMULATION = 4
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LEARNING_RATE = 2e-4
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NUM_EPOCHS = 2
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WARMUP_STEPS = 50
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||||
|
||||
# Datasets
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||||
- 500 samples from ReTrace501-v1
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- 4,500 samples from OpenO1-SFT
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||||
```
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||||
|
||||
---
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||||
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## 🚀 Usage
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### Installation
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||||
|
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```
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pip install torch transformers accelerate
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```
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### Quick Inference
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||||
|
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```
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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||||
# =========================
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||||
# Load model and tokenizer
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||||
# =========================
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||||
model_name = "nnsohamnn/Qwen2.5-3B-ReTrace-OpenO1-Merged"
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tokenizer = AutoTokenizer.from_pretrained(
|
||||
model_name,
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trust_remote_code=True
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||||
)
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||||
|
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model = AutoModelForCausalLM.from_pretrained(
|
||||
model_name,
|
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torch_dtype=torch.float16,
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device_map="auto",
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||||
trust_remote_code=True
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||||
)
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||||
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||||
# =========================
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||||
# LLM question function
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||||
# =========================
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def ask_llm(question: str):
|
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messages = [
|
||||
{
|
||||
"role": "system",
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"content": (
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||||
"You are a helpful AI assistant. When solving problems, show your detailed reasoning process inside <Thought> tags, then provide your final answer inside <Output> tags and explain the final answer from reasoning in short. Break down complex problems step-by-step."
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||||
)
|
||||
},
|
||||
{
|
||||
"role": "user",
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||||
"content": question
|
||||
}
|
||||
]
|
||||
|
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prompt = tokenizer.apply_chat_template(
|
||||
messages,
|
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tokenize=False,
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add_generation_prompt=True
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)
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inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
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outputs = model.generate(
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**inputs,
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max_new_tokens=1024,
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temperature=0.7,
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top_p=0.9
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)
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# Correct slicing
|
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prompt_len = inputs["input_ids"].shape[1]
|
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response = tokenizer.decode(
|
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outputs[0][prompt_len:],
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skip_special_tokens=True
|
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)
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return response
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||||
# =========================
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||||
# Change ONLY this block
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||||
# =========================
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||||
question = """
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||||
A machine produces items where 4% of the output is defective. A quality control test correctly identifies a defective item with probability 0.95 and incorrectly labels a good item as defective with probability 0.03. If an item is selected at random and the test reports it as defective, determine the probability that the item is actually defective.
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"""
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print(ask_llm(question))
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```
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||||
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||||
### Expected Output
|
||||
# Question
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||||
A machine produces items where 4% of the output is defective. A quality control test correctly identifies a defective item with probability 0.95 and incorrectly labels a good item as defective with probability 0.03. If an item is selected at random and the test reports it as defective, determine the probability that the item is actually defective.
|
||||
```
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<Thought>
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Let's define the events:
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- \( D \): The event that the item is defective.
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||||
- \( D^c \): The event that the item is not defective.
|
||||
- \( T \): The event that the test reports the item as defective.
|
||||
|
||||
Given probabilities:
|
||||
- \( P(D) = 0.04 \) (4% defective)
|
||||
- \( P(T|D) = 0.95 \) (Test correctly identifies defective items)
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||||
- \( P(T|D^c) = 0.03 \) (Test incorrectly labels good items as defective)
|
||||
|
||||
We need to find \( P(D|T) \), the probability that the item is defective given that the test reports it as defective.
|
||||
|
||||
Using Bayes' theorem:
|
||||
\[
|
||||
P(D|T) = \frac{P(T|D)P(D)}{P(T)}
|
||||
\]
|
||||
|
||||
First, we need to find \( P(T) \), the total probability that the test reports a defective item. This can be found using the law of total probability:
|
||||
\[
|
||||
P(T) = P(T|D)P(D) + P(T|D^c)P(D^c)
|
||||
\]
|
||||
|
||||
Calculate each term:
|
||||
\[
|
||||
P(D^c) = 1 - P(D) = 1 - 0.04 = 0.96
|
||||
\]
|
||||
\[
|
||||
P(T|D^c) = 0.03
|
||||
\]
|
||||
\[
|
||||
P(T) = (0.95)(0.04) + (0.03)(0.96) = 0.038 + 0.0288 = 0.0668
|
||||
\]
|
||||
|
||||
Now, substitute back into Bayes' theorem:
|
||||
\[
|
||||
P(D|T) = \frac{(0.95)(0.04)}{0.0668} = \frac{0.038}{0.0668} \approx 0.572
|
||||
\]
|
||||
|
||||
So, the probability that the item is actually defective given that the test reports it as defective is approximately 57.2%.
|
||||
|
||||
</Thought>
|
||||
<Output>
|
||||
The probability that the item is actually defective given that the test reports it as defective is approximately 57.2%.
|
||||
</Output>
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 📚 Training Datasets
|
||||
|
||||
### ReTrace501-v1 (500 samples)
|
||||
High-quality chain-of-thought reasoning examples focusing on mathematical problem-solving with explicit reasoning steps.
|
||||
|
||||
**Source:** [nnsohamnn/ReTrace501-v1](https://huggingface.co/datasets/nnsohamnn/ReTrace501-v1)
|
||||
|
||||
### OpenO1-SFT (4,500 samples)
|
||||
Diverse reasoning dataset covering multiple domains including logic, math, science, and general problem-solving.
|
||||
|
||||
**Source:** [O1-OPEN/OpenO1-SFT](https://huggingface.co/datasets/O1-OPEN/OpenO1-SFT)
|
||||
|
||||
---
|
||||
|
||||
## 🔧 Technical Details
|
||||
|
||||
| Component | Specification |
|
||||
|-----------|---------------|
|
||||
| **Architecture** | Qwen2.5 Transformer |
|
||||
| **Parameters** | 3.09 Billion |
|
||||
| **Context Length** | 4096 tokens |
|
||||
| **Precision** | FP16 |
|
||||
| **Training Framework** | Unsloth + HuggingFace Transformers |
|
||||
|
||||
---
|
||||
|
||||
## 📖 Citation
|
||||
|
||||
```
|
||||
@misc{qwen25-retrace-openo1-merged,
|
||||
author = {nnsohamnn},
|
||||
title = {Qwen2.5-3B ReTrace-OpenO1 Merged},
|
||||
year = {2025},
|
||||
publisher = {HuggingFace},
|
||||
url = {https://huggingface.co/nnsohamnn/Qwen2.5-3B-ReTrace-OpenO1-Merged}
|
||||
}
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 🔗 Related Resources
|
||||
|
||||
- **LoRA Adapters:** [nnsohamnn/Qwen2.5-3B-ReTrace-OpenO1-5k-QLoRA](https://huggingface.co/nnsohamnn/Qwen2.5-3B-ReTrace-OpenO1-5k-QLoRA)
|
||||
- **Base Model:** [Qwen/Qwen2.5-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-3B-Instruct)
|
||||
- **Demo Space:** [Try it live!](https://huggingface.co/spaces/nnsohamnn/Qwen-2.5-3b-Think-QLora)
|
||||
|
||||
---
|
||||
|
||||
## 🙏 Acknowledgments
|
||||
|
||||
- **Qwen Team** for the excellent base model
|
||||
- **Unsloth AI** for efficient training tools
|
||||
- **OpenO1** communities for high-quality datasets
|
||||
|
||||
---
|
||||
|
||||
## 📝 License
|
||||
|
||||
Apache 2.0 - See [LICENSE](LICENSE) for details.
|
||||
|
||||
---
|
||||
|
||||
<div align="center">
|
||||
|
||||
**Made with ❤️ by [nnsohamnn](https://huggingface.co/nnsohamnn)**
|
||||
|
||||
⭐ Star this repo if you find it useful!
|
||||
|
||||
[Report Issues](https://huggingface.co/nnsohamnn/Qwen2.5-3B-ReTrace-OpenO1-Merged/discussions) • [Discussions](https://huggingface.co/nnsohamnn/Qwen2.5-3B-ReTrace-OpenO1-Merged/discussions)
|
||||
|
||||
</div>
|
||||
|
||||
24
added_tokens.json
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added_tokens.json
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{
|
||||
"</tool_call>": 151658,
|
||||
"<tool_call>": 151657,
|
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"<|box_end|>": 151649,
|
||||
"<|box_start|>": 151648,
|
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"<|endoftext|>": 151643,
|
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"<|file_sep|>": 151664,
|
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"<|fim_middle|>": 151660,
|
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"<|fim_pad|>": 151662,
|
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"<|fim_prefix|>": 151659,
|
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"<|fim_suffix|>": 151661,
|
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"<|im_end|>": 151645,
|
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"<|im_start|>": 151644,
|
||||
"<|image_pad|>": 151655,
|
||||
"<|object_ref_end|>": 151647,
|
||||
"<|object_ref_start|>": 151646,
|
||||
"<|quad_end|>": 151651,
|
||||
"<|quad_start|>": 151650,
|
||||
"<|repo_name|>": 151663,
|
||||
"<|video_pad|>": 151656,
|
||||
"<|vision_end|>": 151653,
|
||||
"<|vision_pad|>": 151654,
|
||||
"<|vision_start|>": 151652
|
||||
}
|
||||
54
chat_template.jinja
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54
chat_template.jinja
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|
||||
{%- if tools %}
|
||||
{{- '<|im_start|>system\n' }}
|
||||
{%- if messages[0]['role'] == 'system' %}
|
||||
{{- messages[0]['content'] }}
|
||||
{%- else %}
|
||||
{{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
|
||||
{%- endif %}
|
||||
{{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
|
||||
{%- for tool in tools %}
|
||||
{{- "\n" }}
|
||||
{{- tool | tojson }}
|
||||
{%- endfor %}
|
||||
{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
|
||||
{%- else %}
|
||||
{%- if messages[0]['role'] == 'system' %}
|
||||
{{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
|
||||
{%- else %}
|
||||
{{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
|
||||
{%- endif %}
|
||||
{%- endif %}
|
||||
{%- for message in messages %}
|
||||
{%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
|
||||
{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
|
||||
{%- elif message.role == "assistant" %}
|
||||
{{- '<|im_start|>' + message.role }}
|
||||
{%- if message.content %}
|
||||
{{- '\n' + message.content }}
|
||||
{%- endif %}
|
||||
{%- for tool_call in message.tool_calls %}
|
||||
{%- if tool_call.function is defined %}
|
||||
{%- set tool_call = tool_call.function %}
|
||||
{%- endif %}
|
||||
{{- '\n<tool_call>\n{"name": "' }}
|
||||
{{- tool_call.name }}
|
||||
{{- '", "arguments": ' }}
|
||||
{{- tool_call.arguments | tojson }}
|
||||
{{- '}\n</tool_call>' }}
|
||||
{%- endfor %}
|
||||
{{- '<|im_end|>\n' }}
|
||||
{%- elif message.role == "tool" %}
|
||||
{%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
|
||||
{{- '<|im_start|>user' }}
|
||||
{%- endif %}
|
||||
{{- '\n<tool_response>\n' }}
|
||||
{{- message.content }}
|
||||
{{- '\n</tool_response>' }}
|
||||
{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
|
||||
{{- '<|im_end|>\n' }}
|
||||
{%- endif %}
|
||||
{%- endif %}
|
||||
{%- endfor %}
|
||||
{%- if add_generation_prompt %}
|
||||
{{- '<|im_start|>assistant\n' }}
|
||||
{%- endif %}
|
||||
66
config.json
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66
config.json
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|
||||
{
|
||||
"architectures": [
|
||||
"Qwen2ForCausalLM"
|
||||
],
|
||||
"attention_dropout": 0.0,
|
||||
"bos_token_id": 151643,
|
||||
"dtype": "float16",
|
||||
"eos_token_id": 151645,
|
||||
"hidden_act": "silu",
|
||||
"hidden_size": 2048,
|
||||
"initializer_range": 0.02,
|
||||
"intermediate_size": 11008,
|
||||
"layer_types": [
|
||||
"full_attention",
|
||||
"full_attention",
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31
special_tokens_map.json
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Normal file
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3
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3
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207
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BIN
training_plot.png
Normal file
BIN
training_plot.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 34 KiB |
1
vocab.json
Normal file
1
vocab.json
Normal file
File diff suppressed because one or more lines are too long
Reference in New Issue
Block a user