Instructions to use ncbi/Gene-R1-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ncbi/Gene-R1-8B with Transformers:

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

pipe = pipeline("text-generation", model="ncbi/Gene-R1-8B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ncbi/Gene-R1-8B")
model = AutoModelForCausalLM.from_pretrained("ncbi/Gene-R1-8B")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use ncbi/Gene-R1-8B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ncbi/Gene-R1-8B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ncbi/Gene-R1-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ncbi/Gene-R1-8B

SGLang

How to use ncbi/Gene-R1-8B 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 "ncbi/Gene-R1-8B" \
    --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": "ncbi/Gene-R1-8B",
		"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 "ncbi/Gene-R1-8B" \
        --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": "ncbi/Gene-R1-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ncbi/Gene-R1-8B with Docker Model Runner:
```
docker model run hf.co/ncbi/Gene-R1-8B
```

zz07 commited on 27 days ago

Commit

0aedc37

verified ·

1 Parent(s): 9f39d51

Update README.md

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README.md +2 -18

README.md CHANGED Viewed

@@ -26,9 +26,9 @@ base_model:
 **Gene-R1 helps for gene set analysis through fine-tuned small language models (SLMs) that can be locally deployed.**.
 The model contains three versions:
 - [Gene-R1-1B](https://huggingface.co/ncbi/Gene-R1/tree/main/1B): A version fine-tuned based on the Llama-3.2-1B-Instruct.
 - [Gene-R1-3B](https://huggingface.co/ncbi/Gene-R1/tree/main/3B): A version fine-tuned based on the Llama-3.2-3B-Instruct.
-- [Gene-R1-8B](https://huggingface.co/ncbi/Gene-R1/tree/main/8B): A version fine-tuned based on the Llama-3.1-8B-Instruct.
 # Model Deployment for Private Gene Set Analysis
@@ -37,7 +37,7 @@ The model contains three versions:
   import transformers
   from transformers import AutoTokenizer, AutoModelForCausalLM
-  model_id = "ncbi/Gene-R1"
   tokenizer_test = AutoTokenizer.from_pretrained(
       model_id,
       token = "xxxxxxxxx" # Your access key of hugging face
@@ -47,22 +47,6 @@ The model contains three versions:
       device_map = "auto",
       token = "xxxxxxxxx" # Your access key of hugging face
   )
-  # If you want to download a specific model of Gene-R1, using below code:
-  <!-- model_id = "ncbi/Gene-R1"
-  subfolder = "3B"
-  tokenizer_test = AutoTokenizer.from_pretrained(
-      model_id,
-      subfolder=subfolder,
-      token = "xxxxxxxxx" # Your access key of hugging face
-  )
-  model_test = AutoModelForCausalLM.from_pretrained(
-      model_id,
-      subfolder=subfolder,
-      device_map = "auto",
-      token = "xxxxxxxxx" # Your access key of hugging face
-  )-->
   def complete_chat(system, prompt, model, tokenizer):
       model.generation_config.do_sample=False

 **Gene-R1 helps for gene set analysis through fine-tuned small language models (SLMs) that can be locally deployed.**.
 The model contains three versions:
+- [Gene-R1-8B](https://huggingface.co/ncbi/Gene-R1/tree/main/8B): A version fine-tuned based on the Llama-3.1-8B-Instruct.
 - [Gene-R1-1B](https://huggingface.co/ncbi/Gene-R1/tree/main/1B): A version fine-tuned based on the Llama-3.2-1B-Instruct.
 - [Gene-R1-3B](https://huggingface.co/ncbi/Gene-R1/tree/main/3B): A version fine-tuned based on the Llama-3.2-3B-Instruct.
 # Model Deployment for Private Gene Set Analysis
   import transformers
   from transformers import AutoTokenizer, AutoModelForCausalLM
+  model_id = "ncbi/Gene-R1-8B"
   tokenizer_test = AutoTokenizer.from_pretrained(
       model_id,
       token = "xxxxxxxxx" # Your access key of hugging face
       device_map = "auto",
       token = "xxxxxxxxx" # Your access key of hugging face
   )
   def complete_chat(system, prompt, model, tokenizer):
       model.generation_config.do_sample=False