Instructions to use AIFunOver/OpenCoder-1.5B-Instruct-openvino-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AIFunOver/OpenCoder-1.5B-Instruct-openvino-4bit with Transformers:

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

pipe = pipeline("text-generation", model="AIFunOver/OpenCoder-1.5B-Instruct-openvino-4bit")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("AIFunOver/OpenCoder-1.5B-Instruct-openvino-4bit")
model = AutoModelForCausalLM.from_pretrained("AIFunOver/OpenCoder-1.5B-Instruct-openvino-4bit")
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

vLLM

How to use AIFunOver/OpenCoder-1.5B-Instruct-openvino-4bit with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AIFunOver/OpenCoder-1.5B-Instruct-openvino-4bit"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AIFunOver/OpenCoder-1.5B-Instruct-openvino-4bit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/AIFunOver/OpenCoder-1.5B-Instruct-openvino-4bit

SGLang

How to use AIFunOver/OpenCoder-1.5B-Instruct-openvino-4bit 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 "AIFunOver/OpenCoder-1.5B-Instruct-openvino-4bit" \
    --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": "AIFunOver/OpenCoder-1.5B-Instruct-openvino-4bit",
		"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 "AIFunOver/OpenCoder-1.5B-Instruct-openvino-4bit" \
        --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": "AIFunOver/OpenCoder-1.5B-Instruct-openvino-4bit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use AIFunOver/OpenCoder-1.5B-Instruct-openvino-4bit with Docker Model Runner:
```
docker model run hf.co/AIFunOver/OpenCoder-1.5B-Instruct-openvino-4bit
```

AIFunOver commited on Nov 12, 2024

Commit

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	@@ -0,0 +1,31 @@

+---
+base_model: infly/OpenCoder-1.5B-Instruct
+datasets:
+- OpenCoder-LLM/opencoder-sft-stage1
+- OpenCoder-LLM/opencoder-sft-stage2
+language:
+- en
+- zh
+library_name: transformers
+license: other
+license_name: inf
+license_link: https://huggingface.co/infly/OpenCoder-1.5B-Instruct/blob/main/LICENSE
+pipeline_tag: text-generation
+tags:
+- openvino
+- nncf
+- 4-bit
+base_model_relation: quantized
+---
+This model is a quantized version of [`infly/OpenCoder-1.5B-Instruct`](https://huggingface.co/infly/OpenCoder-1.5B-Instruct) and is converted to the OpenVINO format. This model was obtained via the [nncf-quantization](https://huggingface.co/spaces/echarlaix/nncf-quantization) space with [optimum-intel](https://github.com/huggingface/optimum-intel).
+First make sure you have `optimum-intel` installed:
+```bash
+pip install optimum[openvino]
+```
+To load your model you can do as follows:
+```python
+from optimum.intel import OVModelForCausalLM
+model_id = "AIFunOver/OpenCoder-1.5B-Instruct-openvino-4bit"
+model = OVModelForCausalLM.from_pretrained(model_id)
+```