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bio-posttrain-qwen3-1.7b-dn…/README.md
ModelHub XC 4b10f252d6 初始化项目,由ModelHub XC社区提供模型
Model: mims-harvard/bio-posttrain-qwen3-1.7b-dna-sft
Source: Original Platform
2026-09-29 10:05:16 +08:00

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---
license: apache-2.0
base_model: Qwen/Qwen3-1.7B
tags:
- biology
- bio-posttrain
- dna-sft
- dna
library_name: transformers
---
# Bio-posttrain Qwen3-1.7B DNA SFT
DNA 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`
- **DNA encoder:** Evo2 `evo2_1b_base` (frozen; not included in this repo)
- **Embedding layer:** `blocks.20.mlp.l3`
- **LoRA:** rank 64, alpha 128
- **Validation loss:** 0.4687
This repo contains the **merged text LLM** (LoRA fused into base weights) plus `dna_projection.pt`.
## Files
| File | Description |
|------|-------------|
| `model.safetensors` | Merged Qwen3-1.7B weights |
| `dna_projection.pt` | Linear map from Evo2 hidden (1920) → text hidden (2048) |
| `dna_model_config.json` | DNA encoder + projection metadata |
## Loading
Use the [BioReason](https://github.com/mims-harvard/BioReason) `DNALLMModel` loader:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
text_model = AutoModelForCausalLM.from_pretrained("mims-harvard/bio-posttrain-qwen3-1.7b-dna-sft")
tokenizer = AutoTokenizer.from_pretrained("mims-harvard/bio-posttrain-qwen3-1.7b-dna-sft")
proj = torch.load("mims-harvard/bio-posttrain-qwen3-1.7b-dna-sft/dna_projection.pt", map_location="cpu")
# Load Evo2 separately: evo2_1b_base
```
See `dna_model_config.json` for encoder settings.
## Collection
Part of the [Bio-posttrain](https://huggingface.co/collections/mims-harvard/bio-posttrain) collection.