b5d8a32002a7d2bd946d5db0849f95dc698ddd4d
Model: mims-harvard/bio-posttrain-qwen3-1.7b-rna-sft Source: Original Platform
license, base_model, tags, library_name
| license | base_model | tags | library_name | ||||
|---|---|---|---|---|---|---|---|
| apache-2.0 | Qwen/Qwen3-1.7B |
|
transformers |
Bio-posttrain Qwen3-1.7B RNA SFT
RNA supervised fine-tuning (SFT) checkpoint from How Post-Training Shapes Biological Reasoning Models.
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 |
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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 collection.
Description
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