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 Mar 6

Commit

82b3ded

verified ·

1 Parent(s): ba80792

Update README.md

Browse files

Files changed (1) hide show

README.md +10 -17

README.md CHANGED Viewed

@@ -31,23 +31,17 @@ The model contains three versions:
 # Model Deployment for Private Gene Set Analysis
 ```python
-  from transformers import logging
-  logging.set_verbosity_error()
-  import re
-  import torch
-  import openai
-  import numpy as np
-  import pandas as pd
-  from scipy import stats
-  from rouge_score import rouge_scorer
-  from torch import Tensor
-  import tqdm
-  tokenizer_test = AutoTokenizer.from_pretrained(model_path, token = 'xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx') # Your access key of hugging face
   model_test = AutoModelForCausalLM.from_pretrained(
-      model_path, device_map='auto', token = 'xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx'  # Your access key of hugging face
   )
   def complete_chat(system, prompt, model, tokenizer):
@@ -73,13 +67,12 @@ The model contains three versions:
   def llama(genes):
       genes = genes.replace("/",",").replace(" ",",")
       prompt = users(genes)
       summary =complete_chat(system, prompt, model_test, tokenizer_test)
       return summary
   if __name__ == "__main__":
-    genes = "Your Gene Set Separated by comma (,)"
     result = llama(genes)
     print(result)
 ```

 # Model Deployment for Private Gene Set Analysis
 ```python
+  import transformers
+  from transformers import AutoTokenizer, AutoModelForCausalLM
+  tokenizer_test = AutoTokenizer.from_pretrained(
+      model_path,
+      token = 'xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx'  # Your access key of hugging face
+  )
   model_test = AutoModelForCausalLM.from_pretrained(
+      model_path,
+      device_map='auto',
+      token = 'xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx'  # Your access key of hugging face
   )
   def complete_chat(system, prompt, model, tokenizer):
   def llama(genes):
       genes = genes.replace("/",",").replace(" ",",")
       prompt = users(genes)
       summary =complete_chat(system, prompt, model_test, tokenizer_test)
       return summary
   if __name__ == "__main__":
+    genes = "Your Gene Set Separated by comma (,)!"
     result = llama(genes)
     print(result)
 ```