Instructions to use wanglab/sft_gene_pathway_v7_6_100k_relabel_9b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use wanglab/sft_gene_pathway_v7_6_100k_relabel_9b with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="wanglab/sft_gene_pathway_v7_6_100k_relabel_9b")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("wanglab/sft_gene_pathway_v7_6_100k_relabel_9b")
model = AutoModelForMultimodalLM.from_pretrained("wanglab/sft_gene_pathway_v7_6_100k_relabel_9b", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use wanglab/sft_gene_pathway_v7_6_100k_relabel_9b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "wanglab/sft_gene_pathway_v7_6_100k_relabel_9b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "wanglab/sft_gene_pathway_v7_6_100k_relabel_9b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/wanglab/sft_gene_pathway_v7_6_100k_relabel_9b

SGLang

How to use wanglab/sft_gene_pathway_v7_6_100k_relabel_9b with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "wanglab/sft_gene_pathway_v7_6_100k_relabel_9b" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "wanglab/sft_gene_pathway_v7_6_100k_relabel_9b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "wanglab/sft_gene_pathway_v7_6_100k_relabel_9b" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "wanglab/sft_gene_pathway_v7_6_100k_relabel_9b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use wanglab/sft_gene_pathway_v7_6_100k_relabel_9b with Docker Model Runner:
```
docker model run hf.co/wanglab/sft_gene_pathway_v7_6_100k_relabel_9b
```

You need to agree to share your Hugging Face information to access this model

This repository is publicly accessible, but you have to accept the conditions to access its files and content.

Access to these weights is reviewed manually. Please state your name, affiliation, and how you intend to use the model.

sft_gene_pathway_v7_6_100k_relabel_9b

Qwen3.5-9B fine-tuned to reason mechanistically about genetic (CRISPR) perturbations and predict how a queried pathway responds — upregulated, downregulated, or unchanged — conditioned on a single-cell expression profile.

This is the genetic arm of BioReasonCell. It is not a plain text model: it consumes a 2058-d STATE cell embedding alongside the prompt (see Loading).

Where this sits in the project

The repo name is the run name, so it maps 1:1 onto the source tree:


experiment leaf	`experiments/sft_gene_pathway_v7_6_100k/9b_full_relabel/`
family	`sft_gene_pathway_v7_6_100k` — v7.6 pooled with v7.5, scale arm + row-composition A/Bs + the relabel repair
training run	`sft_gene_pathway_v7_6_100k_relabel_9b_stage2` (stage 2; stage 1 is `..._9b_stage1`)
checkpoint	`sft_gene_pathway_v7_6_100k_relabel_9b_converted` — these weights
dataset config	`sft_gene_pathway_v7_6_full100k_relabel`
scoring set	`benchmark_genetic_v1`

Results

Scored on benchmark_genetic_v1 — 27,067 rows / 1,319 perturbations, held out, with (cell, perturbation) leakage asserted at build time. The metric is committed accuracy (accuracy on rows where the model commits to a direction), reported as the per-perturbation macro-average, never row-pooled.

dataset	k	greedy	maj@10	constant-direction control
xaira_orion	598	69.7	74.0	61.0
nadig	292	55.6	59.7	58.6
marson	240	54.3	54.0	51.7
xaira_pisces	110	70.4	71.9	65.5
kaufman	77	61.9	66.9	51.3
pooled	1,317	63.4	66.6	58.6

The constant-direction control is the score from always answering the majority direction for that dataset. It is the number any model on this task has to clear, and it is high — 58.6 pooled — so read every result against it rather than against 50.

Against mechanistic baselines

Per-perturbation committed accuracy, greedy, with each baseline rate-matched onto the same footing:

dataset	9B (this model)	STATE few-shot(ii)	STATE zero-shot	Rhaister few-shot(ii)
xaira_orion	69.7	58.6	52.3 ⁽¹⁷%⁾	—
nadig	55.6	35.0	47.0 ⁽⁹⁹%⁾	51.9 ⁽⁹⁴%⁾
marson	54.3	50.6	—	54.3 ⁽⁸⁴%⁾
xaira_pisces	70.4	71.4	60.2 ⁽³⁸%⁾	68.3 ⁽⁸⁴%⁾
kaufman	61.9	51.4	45.4 ⁽¹⁰%⁾	49.2 ⁽⁷¹%⁾
pooled	63.4

Superscripts are the share of the dataset each baseline covers; cells under 10% coverage are shown as — because 8–14 perturbations is a number, not a measurement. Both zero-shot baselines are capped at their released checkpoint's 2,024-gene perturbation vocabulary.

Things worth knowing before you quote a number

maj@10 is worth more than doubling the base model. Voting over ten samples at temperature 1 buys +3.2 here, against +2.5 for 9B over 4B at greedy. The two stack.
nadig is the one dataset almost nothing clears. Only this model at maj@10 (59.7) beats its 58.6 constant call, and by about a point. The observed-data ceiling there is 84.0, so that is a real deficit, not a noise floor.
kaufman is the informative case for self-consistency: commit rate falls 37.0 → 31.5 while accuracy rises to 66.9 — voting makes the model abstain more and be right more often when it does answer.
Greedy forced accuracy is degenerate at n=1 (an abstention cannot be pushed to a direction) and only becomes meaningful at maj@10.

Loading

The checkpoint carries a cell_projection.pt alongside the transformer weights. The model expects a STATE 2058-d per-perturbation cell embedding projected into the token stream between <|CELL_START|> and <|CELL_END|>; loading the weights with AutoModel.from_pretrained alone will run, but will not reproduce the results above, because the cell channel will be empty.

from huggingface_hub import snapshot_download

path = snapshot_download("wanglab/sft_gene_pathway_v7_6_100k_relabel_9b")
# path/model.safetensors      transformer weights
# path/cell_projection.pt     the cell-embedding projector (required)

The full inference path — embedding construction, projection, prompt rendering and scoring — lives in the BioReasonCell repository. Rendering matters: a prompt belongs to a corpus version, and scoring this model under a different template measures something else.

Training


base model	Qwen3.5-9B
method	two-stage SFT, LoRA r128 / α128
stage 1	cell-projector alignment, 1 epoch
stage 2	full reasoning SFT, 10 epochs, warm-started from stage 1's projector
corpus	`sft_gene_pathway_v7_6_full100k_relabel` — 102,957 rows
context	`max_length_text` 16384
hardware	2 nodes × 4 GPUs, effective batch 64
wall clock	2:56:19 (stage 1) + 27:35 (stage 2)

Training traces were generated by Claude Opus 5 at effort: medium over CRISPR perturbation data, then repaired: 25,531 traces invalidated by a labelling error were regenerated. This checkpoint is the arm trained on the repaired corpus, and the repair is worth +2.5 greedy / +2.8 maj@10 over the 4B on identical perturbations.

Intended use and limitations

Research use. This model predicts a direction of pathway response, not an effect size, and its outputs are hypotheses to be tested — not experimental evidence.

The constant-direction control is 58.6 pooled. A model at 63.4 is meaningfully but not dramatically above "always say up," and on marson it does not clear its control at all.
Performance is not uniform across datasets; quote the per-dataset row, not just the pooled number.
The model was trained and scored on one template. Scoring under a different prompt measures a different thing.
Reasoning traces are model-generated and can be fluent while wrong. Do not treat a trace's intermediate claims about individual genes as validated.

Generations: wanglab/sft_gene_pathway_v7_6_100k_relabel_9b_generations
Reasoning corpus: wanglab/BioReasonCell-ReasoningData

License

Inherits the license of the Qwen3.5-9B base model. Training data derives from Claude Opus 5 generations; downstream use is subject to the terms governing those outputs.

Downloads last month: 4

Safetensors

Model size

9B params

Tensor type

BF16

Model tree for wanglab/sft_gene_pathway_v7_6_100k_relabel_9b

Base model

Qwen/Qwen3.5-9B-Base

Finetuned

Qwen/Qwen3.5-9B