86 lines
2.2 KiB
Markdown
86 lines
2.2 KiB
Markdown
---
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base_model: kenzrx/qwen3-4b-sft-merged
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datasets:
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- structured_data_with_cot_dataset_v2
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- structured_data_with_cot_dataset_v2
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language:
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- en
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license: apache-2.0
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library_name: transformers
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pipeline_tag: text-generation
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tags:
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- qwen
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- unsloth
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- transformers
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- text-generation
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- lora
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- merged
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- dpo
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- alignment
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- sft
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---
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# qwen3-4b-instruct-2507-sft-dpo-qwen-cot-merged
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This repository provides **full-merged 16-bit weights** (no adapter loading required).
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## What this model is
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This model was trained in **two stages**:
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1) **SFT (Supervised Fine-Tuning)** to learn high-quality reference answers / formatting
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2) **DPO (Direct Preference Optimization)** to align outputs toward preferred responses
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### Lineage
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- **Original base**: Qwen/Qwen3-4B-Instruct-2507
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- **Stage 1 (SFT) output (merged)**: kenzrx/qwen3-4b-sft-merged
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- **Stage 2 (DPO) output (this repo)**: merged 16-bit weights
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## Training Objective (DPO)
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The DPO stage optimizes the model to prefer **chosen** outputs over **rejected** outputs
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given the same prompt, improving response alignment and structured quality.
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## Training Configuration (DPO)
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- **Start model (SFT merged)**: kenzrx/qwen3-4b-sft-merged
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- **Method**: DPO (Direct Preference Optimization)
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- **Epochs**: 1
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- **Learning rate**: 1e-07
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- **Beta**: 0.1
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- **Max sequence length**: 1024
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- **LoRA Config (during training)**: r=8, alpha=16 (merged into final 16-bit weights)
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## Datasets
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- **SFT dataset**: structured_data_with_cot_dataset_v2
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- **DPO preference dataset**: structured_data_with_cot_dataset_v2
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## Usage
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You can use this model directly with `transformers`.
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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model_id = "your_id/your-repo-name"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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torch_dtype=torch.float16,
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device_map="auto",
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)
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prompt = "Your question here"
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messages = [
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{"role": "user", "content": prompt},
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]
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inputs = tokenizer.apply_chat_template(
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messages,
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tokenize=True,
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add_generation_prompt=True,
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return_tensors="pt",
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).to(model.device)
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outputs = model.generate(**inputs, max_new_tokens=512)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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