Instructions to use jsoul/geo-perturbation-grouping-qwen3-4b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jsoul/geo-perturbation-grouping-qwen3-4b with Transformers:

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

pipe = pipeline("text-generation", model="jsoul/geo-perturbation-grouping-qwen3-4b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("jsoul/geo-perturbation-grouping-qwen3-4b")
model = AutoModelForCausalLM.from_pretrained("jsoul/geo-perturbation-grouping-qwen3-4b", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use jsoul/geo-perturbation-grouping-qwen3-4b with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf jsoul/geo-perturbation-grouping-qwen3-4b:Q8_0
# Run inference directly in the terminal:
llama cli -hf jsoul/geo-perturbation-grouping-qwen3-4b:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf jsoul/geo-perturbation-grouping-qwen3-4b:Q8_0
# Run inference directly in the terminal:
llama cli -hf jsoul/geo-perturbation-grouping-qwen3-4b:Q8_0

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf jsoul/geo-perturbation-grouping-qwen3-4b:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf jsoul/geo-perturbation-grouping-qwen3-4b:Q8_0

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf jsoul/geo-perturbation-grouping-qwen3-4b:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf jsoul/geo-perturbation-grouping-qwen3-4b:Q8_0

Use Docker

docker model run hf.co/jsoul/geo-perturbation-grouping-qwen3-4b:Q8_0

LM Studio
Jan

vLLM

How to use jsoul/geo-perturbation-grouping-qwen3-4b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jsoul/geo-perturbation-grouping-qwen3-4b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jsoul/geo-perturbation-grouping-qwen3-4b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/jsoul/geo-perturbation-grouping-qwen3-4b:Q8_0

SGLang

How to use jsoul/geo-perturbation-grouping-qwen3-4b 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 "jsoul/geo-perturbation-grouping-qwen3-4b" \
    --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": "jsoul/geo-perturbation-grouping-qwen3-4b",
		"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 "jsoul/geo-perturbation-grouping-qwen3-4b" \
        --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": "jsoul/geo-perturbation-grouping-qwen3-4b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use jsoul/geo-perturbation-grouping-qwen3-4b with Ollama:
```
ollama run hf.co/jsoul/geo-perturbation-grouping-qwen3-4b:Q8_0
```
Unsloth Desktop

How to use jsoul/geo-perturbation-grouping-qwen3-4b with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf jsoul/geo-perturbation-grouping-qwen3-4b:Q8_0

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "jsoul/geo-perturbation-grouping-qwen3-4b:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use jsoul/geo-perturbation-grouping-qwen3-4b with Docker Model Runner:
```
docker model run hf.co/jsoul/geo-perturbation-grouping-qwen3-4b:Q8_0
```

Lemonade

How to use jsoul/geo-perturbation-grouping-qwen3-4b with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull jsoul/geo-perturbation-grouping-qwen3-4b:Q8_0

Run and chat with the model

lemonade run user.geo-perturbation-grouping-qwen3-4b-Q8_0

List all available models

lemonade list

Hermes Agent

How to use jsoul/geo-perturbation-grouping-qwen3-4b with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf jsoul/geo-perturbation-grouping-qwen3-4b:Q8_0

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default jsoul/geo-perturbation-grouping-qwen3-4b:Q8_0

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use jsoul/geo-perturbation-grouping-qwen3-4b with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf jsoul/geo-perturbation-grouping-qwen3-4b:Q8_0

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "jsoul/geo-perturbation-grouping-qwen3-4b:Q8_0" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

GEO Gene-Perturbation Grouping — Qwen3-4B (merged)

A fine-tune of Qwen/Qwen3-4B-Instruct-2507 that reads the sample metadata of an NCBI GEO gene-expression series and organizes its samples into valid genetic-perturbation case/control experimental groups (methods KD / KO / OE, ≥2 controls and ≥2 cases, single wild-type HGNC target gene, one cell line, matched time points), or reports No valid groups found.

The LoRA adapter has been merged into the base weights, so this is a standalone model (bfloat16, safetensors) usable directly with transformers, vLLM, TGI, etc.

Model details


Base model	`Qwen/Qwen3-4B-Instruct-2507` (Qwen3, 36 layers, hidden 2560, GQA 32/8 heads, 262k context)
Parameters	~4B
Precision	bfloat16
Format	merged full weights, `safetensors` (2 shards, ~8 GB)
Architecture	`Qwen3ForCausalLM`

Training


Method	LoRA (merged), via Unsloth 2025.8.5 (`transformers` 4.55.1)
LoRA rank / alpha	32 / 32
Learning rate	2e-4
Epochs	3
Seed	3407
Training data	`jsoul/geo-perturbation-grouping-train` — `train` split only (2,400 examples)

The companion validation split (600 examples) and the held-out jsoul/geo-perturbation-grouping-test set (300 examples) were not used for training. GEO accessions are disjoint across train / validation / test, so the test set is a clean leakage-free benchmark.

Prompt format

The model was trained on single-turn chats: a fixed system rulebook, a user message containing the study metadata, and an assistant target. For best results, use the exact system prompt from the training dataset (ds["train"][0]["messages"][0]["content"]) and supply the study metadata as the user turn.

Target output format:

Group <n>:
  Cell line: <cell line>
  Perturbation method: <KD | KO | OE>
  Target gene: <HGNC symbol>
  Control: <comma-separated GSM IDs>
  Case: <comma-separated GSM IDs>

Usage

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "jsoul/geo-perturbation-grouping-qwen3-4b"  # private; requires a token
tok = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")

system = "<the fixed rulebook — see the training dataset's messages[0]>"
user   = "<GEO study title/summary/design + the per-sample GSM table>"

messages = [{"role": "system", "content": system},
            {"role": "user", "content": user}]
inputs = tok.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
out = model.generate(inputs, max_new_tokens=1024, do_sample=False)
print(tok.decode(out[0, inputs.shape[1]:], skip_special_tokens=True))

Greedy decoding (do_sample=False) is recommended for this structured extraction task; the packaged generation_config.json otherwise defaults to sampling (temperature=0.7, top_p=0.8, top_k=20), inherited from the base model.

Intended use & limitations

Intended: curation assistance for identifying perturbation case/control groups from GEO transcriptomic studies (RNA-seq / microarray). Outputs should be reviewed by a human.
Out of scope: non-transcriptomic assays, non-human samples, multi-gene or mutant constructs, and drug/stimulus-confounded designs — the rulebook instructs the model to skip these, but errors are possible.
Limitations: may hallucinate GSM IDs or gene symbols, mis-assign controls, or mishandle very long sample tables. Always validate GSM membership and HGNC symbols against the source GEO record before use.

License

Inherits the base model's license (Apache 2.0, from Qwen/Qwen3-4B-Instruct-2507). Training data derives from public NCBI GEO metadata; see the dataset cards for provenance.

Citation

Please cite this repository, the base model (Qwen/Qwen3-4B-Instruct-2507), and acknowledge NCBI GEO as the metadata source.

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Safetensors

Model size

4B params

Tensor type

BF16

Model tree for jsoul/geo-perturbation-grouping-qwen3-4b

Base model

Qwen/Qwen3-4B-Instruct-2507

Adapter

(5665)

this model

jsoul
/

geo-perturbation-grouping-qwen3-4b

GEO Gene-Perturbation Grouping — Qwen3-4B (merged)

Model details

Training

Prompt format

Usage

Intended use & limitations

License

Citation

Model tree for jsoul/geo-perturbation-grouping-qwen3-4b

Dataset used to train jsoul/geo-perturbation-grouping-qwen3-4b