Instructions to use Commencis/Commencis-LLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Commencis/Commencis-LLM with Transformers:

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

pipe = pipeline("text-generation", model="Commencis/Commencis-LLM")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Commencis/Commencis-LLM")
model = AutoModelForCausalLM.from_pretrained("Commencis/Commencis-LLM")
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 Commencis/Commencis-LLM with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Commencis/Commencis-LLM

SGLang

How to use Commencis/Commencis-LLM 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 "Commencis/Commencis-LLM" \
    --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": "Commencis/Commencis-LLM",
		"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 "Commencis/Commencis-LLM" \
        --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": "Commencis/Commencis-LLM",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Commencis/Commencis-LLM with Docker Model Runner:
```
docker model run hf.co/Commencis/Commencis-LLM
```

yunusonur commited on Mar 19, 2024

Commit

fc4354e

verified ·

1 Parent(s): 72eb687

Update README.md

Browse files

Files changed (1) hide show

README.md +2 -2

README.md CHANGED Viewed

@@ -62,7 +62,7 @@ class TextGenerationAssistant:
     def generate_response(self, user_query):
         prompt = self.format_prompt(user_query)
         outputs = self.pipe(prompt, **self.sampling_params)
-        return outputs[0]["generated_text"].split("[/INST]")[-1]
 assistant = TextGenerationAssistant(model_id="Commencis/Commencis-LLM")
@@ -94,7 +94,7 @@ pipeline = transformers.pipeline(
 )
 outputs = pipeline(prompt, max_new_tokens=1024, do_sample=True, temperature=0.5, top_k=50, top_p=0.9)
-print(outputs[0]["generated_text"])
 ```
 ## Bias, Risks, and Limitations

     def generate_response(self, user_query):
         prompt = self.format_prompt(user_query)
         outputs = self.pipe(prompt, **self.sampling_params)
+        return outputs[0]["generated_text"].split("[/INST]")[1].strip()
 assistant = TextGenerationAssistant(model_id="Commencis/Commencis-LLM")
 )
 outputs = pipeline(prompt, max_new_tokens=1024, do_sample=True, temperature=0.5, top_k=50, top_p=0.9)
+print (outputs[0]["generated_text"].split("[/INST]")[1].strip())
 ```
 ## Bias, Risks, and Limitations