Instructions to use cllm/consistency-llm-7b-codesearchnet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use cllm/consistency-llm-7b-codesearchnet with Transformers:

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

pipe = pipeline("text-generation", model="cllm/consistency-llm-7b-codesearchnet")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("cllm/consistency-llm-7b-codesearchnet")
model = AutoModelForCausalLM.from_pretrained("cllm/consistency-llm-7b-codesearchnet", 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

vLLM

How to use cllm/consistency-llm-7b-codesearchnet with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "cllm/consistency-llm-7b-codesearchnet"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "cllm/consistency-llm-7b-codesearchnet",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/cllm/consistency-llm-7b-codesearchnet

SGLang

How to use cllm/consistency-llm-7b-codesearchnet 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 "cllm/consistency-llm-7b-codesearchnet" \
    --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": "cllm/consistency-llm-7b-codesearchnet",
		"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 "cllm/consistency-llm-7b-codesearchnet" \
        --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": "cllm/consistency-llm-7b-codesearchnet",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use cllm/consistency-llm-7b-codesearchnet with Docker Model Runner:
```
docker model run hf.co/cllm/consistency-llm-7b-codesearchnet
```

consistency-llm-7b-codesearchnet

Commit History

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Commit History

Update README.md 8463ad9 verified

Create README.md 280f905 verified

upload models ccc2c11

initial commit 3938ad2 verified

Update README.md

8463ad9
verified

Create README.md

280f905
verified

upload models

ccc2c11

initial commit

3938ad2
verified