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Model: mims-harvard/bio-posttrain-qwen3-1.7b-rna-sft
Source: Original Platform

README.md

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