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Update README for unified model repository structure

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All models (0.6B, 1.7B, 4B) with SFT and GRPO variants now go into
a single HuggingFace repo (tobil/qmd-query-expansion) with subfolders
for each size and training method.

Updated loading examples to show subfolder-based model loading.

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
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Tobi Lutke 2026-01-25 01:00:17 -05:00
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@ -26,24 +26,33 @@ hyde: To configure authentication, set the AUTH_SECRET environment variable and
## Trained Models
| Model | HuggingFace | Score | Status |
|-------|-------------|-------|--------|
| **Qwen3-0.6B v4 (SFT)** | [tobil/qmd-query-expansion-0.6B-v4](https://huggingface.co/tobil/qmd-query-expansion-0.6B-v4) | **98.8%** | Recommended |
| Qwen3-0.6B v4 (GRPO) | [tobil/qmd-query-expansion-0.6B-v4-grpo](https://huggingface.co/tobil/qmd-query-expansion-0.6B-v4-grpo) | 89.7% | Requires SFT base (see note) |
All models are in a single HuggingFace repo: **[tobil/qmd-query-expansion](https://huggingface.co/tobil/qmd-query-expansion)**
**Note on GRPO model**: The GRPO adapter was trained on top of the merged SFT model, so you must load SFT first:
| Size | SFT Adapter | GRPO Adapter | Base Model |
|------|-------------|--------------|------------|
| **0.6B** | `0.6B-sft` | `0.6B-grpo` | `Qwen/Qwen3-0.6B` |
| **1.7B** | `1.7B-sft` | `1.7B-grpo` | `Qwen/Qwen3-1.7B` |
| **4B** | `4B-sft` | `4B-grpo` | `Qwen/Qwen3-4B` |
### Loading Models
```python
from peft import PeftModel
from transformers import AutoModelForCausalLM
# Load base → merge SFT → apply GRPO
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-0.6B")
model = PeftModel.from_pretrained(model, "tobil/qmd-query-expansion-0.6B-v4")
# Load SFT model (recommended)
base = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-1.7B", torch_dtype="bfloat16")
model = PeftModel.from_pretrained(base, "tobil/qmd-query-expansion", subfolder="1.7B-sft")
# Load GRPO model (requires SFT first)
base = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-1.7B", torch_dtype="bfloat16")
model = PeftModel.from_pretrained(base, "tobil/qmd-query-expansion", subfolder="1.7B-sft")
model = model.merge_and_unload()
model = PeftModel.from_pretrained(model, "tobil/qmd-query-expansion-0.6B-v4-grpo")
model = PeftModel.from_pretrained(model, "tobil/qmd-query-expansion", subfolder="1.7B-grpo")
```
**Note on GRPO models**: GRPO adapters were trained on top of merged SFT weights, so you must load and merge SFT first before applying GRPO.
## Prompt Format
The models use **Qwen3 chat template** with `/no_think` to disable thinking mode.