89 lines
3.3 KiB
Markdown
89 lines
3.3 KiB
Markdown
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---
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license: apache-2.0
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language:
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- en
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tags:
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- llama
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- dpo
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- chat
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- from-scratch
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- gpuburnout
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pipeline_tag: text-generation
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---
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# GPUburnout-2B-75K-Chat-DPO
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A 1.92 billion parameter Llama-style chat model with DPO alignment. Trained from scratch, expanded from 1B, SFT'd on SlimOrca 50K, then DPO-aligned with 1,078 preference pairs.
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## Model Details
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- **Architecture:** Llama-style decoder-only transformer
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- **Parameters:** 1.92B
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- **Hidden dim:** 2304
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- **Layers:** 24
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- **Attention:** GQA (36 query heads, 9 KV heads)
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- **FFN:** SwiGLU (intermediate 9216)
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- **Position encoding:** RoPE (theta=500000)
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- **Context length:** 2048 tokens
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- **Vocabulary:** 32,005 tokens (BPE + 5 special tokens)
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## Training Pipeline
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1. **Pretraining:** 1.04B model trained to Chinchilla-optimal (160K steps, 20.97B tokens)
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2. **Growth:** Expanded 1B -> 1.92B via weight copying + new layer insertion
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3. **Continued pretraining:** 75K steps on clean data (contaminated Python-Edu + FineMath replaced)
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4. **SFT:** SlimOrca 50K, LoRA r=16/alpha=32, 1 epoch
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5. **DPO:** 1,078 preference pairs, beta=0.1, lr=5e-7, LoRA r=16/alpha=32, 1 epoch
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## DPO Details
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- **Preference data:** 1,200 prompts across 10 categories, 5 responses per prompt at graduated temperatures (0.5-1.3)
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- **Judge:** Claude (via Claude.ai Max subscription) — evaluation only, no distillation
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- **Result:** 7/8 clean on garbage token check (vs 4/8 on 1B DPO)
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- **Key insight:** Clean pretraining data was the prerequisite — 1B DPO failed because garbage tokens were baked in from contaminated pretraining data
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## Garbage Token Check (8 standard prompts)
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| Prompt | Status |
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|---|---|
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| Explain how photosynthesis works | CLEAN |
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| What is the theory of relativity? | CLEAN |
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| Write a Python function to reverse a string | GARBAGE |
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| Tell me a creative story about a robot learning to paint | CLEAN |
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| Solve: If a train travels 60 mph for 2.5 hours, how far does it go? | CLEAN |
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| What are the ethical implications of AI in healthcare? | CLEAN |
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| Explain the water cycle to a 10-year-old | CLEAN |
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| What is the difference between a virus and a bacterium? | CLEAN |
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## Usage
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model = AutoModelForCausalLM.from_pretrained("GPUburnout/GPUburnout-2B-75K-Chat-DPO", torch_dtype="float16")
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tokenizer = AutoTokenizer.from_pretrained("GPUburnout/GPUburnout-2B-75K-Chat-DPO")
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messages = [
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{"role": "system", "content": "You are a helpful assistant."},
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{"role": "user", "content": "Explain how photosynthesis works."},
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]
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text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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inputs = tokenizer(text, return_tensors="pt").to(model.device)
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outputs = model.generate(**inputs, max_new_tokens=200, temperature=0.7, top_p=0.9)
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print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))
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```
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## Related Models
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- [GPUburnout-2B-75K](https://huggingface.co/GPUburnout/GPUburnout-2B-75K) — Base pretrained
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- [GPUburnout-2B-75K-Chat](https://huggingface.co/GPUburnout/GPUburnout-2B-75K-Chat) — SFT only
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- [GPUburnout-1B-160K](https://huggingface.co/GPUburnout/GPUburnout-1B-160K) — 1B base (Chinchilla-optimal)
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## Blog
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Full training journey documented at [gpuburnout.com](https://gpuburnout.com)
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## Author
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Jun Park ([@GPUburnout](https://github.com/GPUburnout))
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