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Model: GPUburnout/GPUburnout-2B-75K-Chat-DPO
Source: Original Platform

README.md

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