Instructions to use tim1900/cvx-coder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tim1900/cvx-coder with Transformers:

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

pipe = pipeline("text-generation", model="tim1900/cvx-coder", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("tim1900/cvx-coder", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("tim1900/cvx-coder", trust_remote_code=True)
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 tim1900/cvx-coder with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tim1900/cvx-coder"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tim1900/cvx-coder",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/tim1900/cvx-coder

SGLang

How to use tim1900/cvx-coder 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 "tim1900/cvx-coder" \
    --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": "tim1900/cvx-coder",
		"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 "tim1900/cvx-coder" \
        --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": "tim1900/cvx-coder",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use tim1900/cvx-coder with Docker Model Runner:
```
docker model run hf.co/tim1900/cvx-coder
```

tim1900 commited on Sep 18, 2024

Commit

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 ## Introduction
-cvx-coder aims to improve the [Matlab CVX](https://cvxr.com/cvx) code ability and QA ability of LLMs. It is a [phi-3 model](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) finetuned on a dataset consisting of CVX docs, codes, [forum conversations](https://ask.cvxr.com/).
 ## Quickstart
 For one quick test, run the following:

 ## Introduction
+cvx-coder aims to improve the [Matlab CVX](https://cvxr.com/cvx) code ability and QA ability of LLMs. It is a [phi-3 model](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) finetuned on a dataset consisting of CVX docs, codes, [forum conversations](https://ask.cvxr.com/) ( my cleaned version of them is at [CVX-forum-conversations](https://huggingface.co/datasets/tim1900/CVX-forum-conversations)).
 ## Quickstart
 For one quick test, run the following: