Instructions to use m-a-p/OpenCodeInterpreter-CL-13B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use m-a-p/OpenCodeInterpreter-CL-13B with Transformers:

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

pipe = pipeline("text-generation", model="m-a-p/OpenCodeInterpreter-CL-13B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("m-a-p/OpenCodeInterpreter-CL-13B")
model = AutoModelForCausalLM.from_pretrained("m-a-p/OpenCodeInterpreter-CL-13B")

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use m-a-p/OpenCodeInterpreter-CL-13B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "m-a-p/OpenCodeInterpreter-CL-13B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "m-a-p/OpenCodeInterpreter-CL-13B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/m-a-p/OpenCodeInterpreter-CL-13B

SGLang

How to use m-a-p/OpenCodeInterpreter-CL-13B 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 "m-a-p/OpenCodeInterpreter-CL-13B" \
    --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": "m-a-p/OpenCodeInterpreter-CL-13B",
		"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 "m-a-p/OpenCodeInterpreter-CL-13B" \
        --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": "m-a-p/OpenCodeInterpreter-CL-13B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use m-a-p/OpenCodeInterpreter-CL-13B with Docker Model Runner:
```
docker model run hf.co/m-a-p/OpenCodeInterpreter-CL-13B
```

Anitaliu98 commited on May 14, 2024

Commit

0f4e640

verified ·

1 Parent(s): e46c2aa

Update tokenizer_config.json

Browse files

Files changed (1) hide show

tokenizer_config.json +2 -1

tokenizer_config.json CHANGED Viewed

@@ -82,5 +82,6 @@
   "suffix_token": "▁<SUF>",
   "tokenizer_class": "CodeLlamaTokenizer",
   "unk_token": "<unk>",
-  "use_default_system_prompt": false
 }

   "suffix_token": "▁<SUF>",
   "tokenizer_class": "CodeLlamaTokenizer",
   "unk_token": "<unk>",
+  "use_default_system_prompt": false,
+  "chat_template": "{% for message in messages %}\n{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}\n{% endfor %}\n{% if add_generation_prompt %}\n{{ '<|im_start|>assistant\n' }}\n{% endif %}"
 }