47 lines
1.4 KiB
Markdown
47 lines
1.4 KiB
Markdown
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---
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license: apache-2.0
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base_model: Qwen/Qwen3-1.7B
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tags:
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- biology
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- bio-posttrain
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- rna-sft
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- rna
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library_name: transformers
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---
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# Bio-posttrain Qwen3-1.7B RNA SFT
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RNA supervised fine-tuning (SFT) checkpoint from [How Post-Training Shapes Biological Reasoning Models](https://huggingface.co/collections/mims-harvard/bio-posttrain).
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## Model details
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- **Base model:** `Qwen/Qwen3-1.7B`
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- **RNA embeddings:** Precomputed TranscriptFormer (2048-d), projected via `rna_projection.pt`
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- **LoRA:** rank 32, alpha 64
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- **Validation loss:** 0.5394
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## Files
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| File | Description |
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|------|-------------|
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| `model.safetensors` | Merged LLM weights |
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| `rna_projection.pt` | Linear map RNA embed (2048) → text hidden |
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| `rna_model_config.json` | Architecture metadata |
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## Loading
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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 = AutoModelForCausalLM.from_pretrained("mims-harvard/bio-posttrain-qwen3-1.7b-rna-sft", trust_remote_code=True)
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tokenizer = AutoTokenizer.from_pretrained("mims-harvard/bio-posttrain-qwen3-1.7b-rna-sft")
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proj = torch.load("rna_projection.pt", map_location="cpu")
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```
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RNA sequence embeddings are supplied offline at inference time. See the `rna_models` code in the BioReason repository.
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## Collection
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Part of the [Bio-posttrain](https://huggingface.co/collections/mims-harvard/bio-posttrain) collection.
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