Instructions to use ammarnasr/codegen2-1B-security with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ammarnasr/codegen2-1B-security with Transformers:

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

pipe = pipeline("text-generation", model="ammarnasr/codegen2-1B-security", trust_remote_code=True)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ammarnasr/codegen2-1B-security", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("ammarnasr/codegen2-1B-security", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ammarnasr/codegen2-1B-security with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ammarnasr/codegen2-1B-security"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ammarnasr/codegen2-1B-security",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ammarnasr/codegen2-1B-security

SGLang

How to use ammarnasr/codegen2-1B-security 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 "ammarnasr/codegen2-1B-security" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ammarnasr/codegen2-1B-security",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "ammarnasr/codegen2-1B-security" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ammarnasr/codegen2-1B-security",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ammarnasr/codegen2-1B-security with Docker Model Runner:
```
docker model run hf.co/ammarnasr/codegen2-1B-security
```

ammarnasr commited on Dec 23, 2023

Commit

bbec848

1 Parent(s): 6f0551a

Update handler.py

Browse files

Files changed (1) hide show

handler.py +6 -2

handler.py CHANGED Viewed

@@ -15,9 +15,13 @@ class EndpointHandler:
     def __call__(self, data: Dict[str, Any]) -> List[str]:
         prompt = data["inputs"]
         input_ids = self.tokenizer(prompt, return_tensors="pt").input_ids.to(self.device)
-        generated_ids = self.model.generate(input_ids)
-        return [self.tokenizer.decode(generated_ids[0], skip_special_tokens=True)]

     def __call__(self, data: Dict[str, Any]) -> List[str]:
         prompt = data["inputs"]
+        if "config" in data:
+          config = data.pop("config", None)
+        else:
+          config = {'max_new_tokens':100}
         input_ids = self.tokenizer(prompt, return_tensors="pt").input_ids.to(self.device)
+        generated_ids = self.model.generate(input_ids, **config)
+        return self.tokenizer.decode(generated_ids[0], skip_special_tokens=True)