81 lines
2.2 KiB
Markdown
81 lines
2.2 KiB
Markdown
|
---
|
||
|
|
base_model: Qwen/Qwen3-4B-Instruct-2507
|
||
|
|
datasets:
|
||
|
|
- u-10bei/dpo-dataset-qwen-cot
|
||
|
|
language:
|
||
|
|
- en
|
||
|
|
license: apache-2.0
|
||
|
|
library_name: transformers
|
||
|
|
pipeline_tag: text-generation
|
||
|
|
tags:
|
||
|
|
- dpo
|
||
|
|
- unsloth
|
||
|
|
- qwen
|
||
|
|
- alignment
|
||
|
|
- two-stage-training
|
||
|
|
---
|
||
|
|
|
||
|
|
# qwen3-4b-2507-dpo-cot-merged
|
||
|
|
|
||
|
|
This model is the result of **two-stage fine-tuning**:
|
||
|
|
|
||
|
|
1. **SFT (Supervised Fine-Tuning)**: Training on structured output datasets
|
||
|
|
2. **DPO (Direct Preference Optimization)**: Alignment using preference data
|
||
|
|
|
||
|
|
This repository contains the **full-merged 16-bit weights**. No adapter loading is required.
|
||
|
|
|
||
|
|
## Training Pipeline
|
||
|
|
|
||
|
|
### Stage 1: SFT
|
||
|
|
- **Base model**: Qwen/Qwen3-4B-Instruct-2507
|
||
|
|
- **SFT adapter**: masachika/qwen3-4b-Instruct-2507-structured-output-lora
|
||
|
|
- **Objective**: Learn to generate structured outputs (JSON, YAML, XML, TOML, CSV)
|
||
|
|
|
||
|
|
### Stage 2: DPO (This model)
|
||
|
|
- **Starting point**: SFT-trained model (merged)
|
||
|
|
- **Method**: Direct Preference Optimization
|
||
|
|
- **Dataset**: u-10bei/dpo-dataset-qwen-cot
|
||
|
|
- **Objective**: Align responses with preferred outputs, improve reasoning quality
|
||
|
|
|
||
|
|
## Training Configuration (DPO Stage)
|
||
|
|
- **Epochs**: 2
|
||
|
|
- **Learning rate**: 3e-07
|
||
|
|
- **Beta**: 0.2
|
||
|
|
- **Max sequence length**: 2048
|
||
|
|
- **LoRA Config**: r=64, alpha=128 (merged into base)
|
||
|
|
|
||
|
|
## Usage
|
||
|
|
Since this is a merged model, you can use it directly with `transformers`.
|
||
|
|
|
||
|
|
```python
|
||
|
|
from transformers import AutoModelForCausalLM, AutoTokenizer
|
||
|
|
import torch
|
||
|
|
|
||
|
|
model_id = "masachika/qwen3-4b-dpo-cot-merged"
|
||
|
|
|
||
|
|
tokenizer = AutoTokenizer.from_pretrained(model_id)
|
||
|
|
model = AutoModelForCausalLM.from_pretrained(
|
||
|
|
model_id,
|
||
|
|
torch_dtype=torch.float16,
|
||
|
|
device_map="auto"
|
||
|
|
)
|
||
|
|
|
||
|
|
# Test inference
|
||
|
|
prompt = "Your question here"
|
||
|
|
inputs = tokenizer.apply_chat_template(
|
||
|
|
[{"role": "user", "content": prompt}],
|
||
|
|
tokenize=True,
|
||
|
|
add_generation_prompt=True,
|
||
|
|
return_tensors="pt"
|
||
|
|
).to("cuda")
|
||
|
|
outputs = model.generate(**inputs, max_new_tokens=512)
|
||
|
|
print(tokenizer.decode(outputs[0]))
|
||
|
|
```
|
||
|
|
|
||
|
|
## Sources & License (IMPORTANT)
|
||
|
|
|
||
|
|
* **SFT Base**: masachika/qwen3-4b-Instruct-2507-structured-output-lora
|
||
|
|
* **DPO Training Data**: u-10bei/dpo-dataset-qwen-cot
|
||
|
|
* **License**: MIT License (as per dataset terms)
|
||
|
|
* **Compliance**: Users must follow the original base model's license terms
|