Instructions to use infly/OpenCoder-8B-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use infly/OpenCoder-8B-Base with Transformers:

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

pipe = pipeline("text-generation", model="infly/OpenCoder-8B-Base")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("infly/OpenCoder-8B-Base")
model = AutoModelForMultimodalLM.from_pretrained("infly/OpenCoder-8B-Base")
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 Settings

vLLM

How to use infly/OpenCoder-8B-Base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "infly/OpenCoder-8B-Base"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "infly/OpenCoder-8B-Base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/infly/OpenCoder-8B-Base

SGLang

How to use infly/OpenCoder-8B-Base 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 "infly/OpenCoder-8B-Base" \
    --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": "infly/OpenCoder-8B-Base",
		"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 "infly/OpenCoder-8B-Base" \
        --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": "infly/OpenCoder-8B-Base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use infly/OpenCoder-8B-Base with Docker Model Runner:
```
docker model run hf.co/infly/OpenCoder-8B-Base
```

Simingh commited on Nov 11, 2024

Commit

6f7974c

verified ·

1 Parent(s): 0270c4c

Update README.md

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README.md +18 -4

README.md CHANGED Viewed

@@ -41,7 +41,21 @@ library_name: transformers
 | OpenCoder-1.5B-Instruct  |      4K       | 🤗 [HuggingFace](https://huggingface.co/infly/OpenCoder-1.5B-Instruct) |
 | OpenCoder-8B-Instruct  |      8K       | 🤗 [HuggingFace](https://huggingface.co/infly/OpenCoder-8B-Instruct) |
-## 3. Benchmarks
 **Note:** For the detailed evaluation results, please refer to [our paper](https://arxiv.org/pdf/2411.04905).
@@ -65,7 +79,7 @@ library_name: transformers
 | MultiPL-E (AVG) |     57.5     |     71.0     | -->
-## 4. Inference
 ### Inference with Huggingface's Transformers
@@ -90,11 +104,11 @@ print(result)
 <!-- ### Inference with vLLM (recommended) -->
-## 5. License
 OpenCoder series (including Base and Chat) support commercial applications under a permissive [License](https://huggingface.co/infly/OpenCoder-8B-Base/blob/main/LICENSE).
-## 6. Citation
 ```
 @inproceedings{Huang2024OpenCoderTO,
   title={OpenCoder: The Open Cookbook for Top-Tier Code Large Language Models},

 | OpenCoder-1.5B-Instruct  |      4K       | 🤗 [HuggingFace](https://huggingface.co/infly/OpenCoder-1.5B-Instruct) |
 | OpenCoder-8B-Instruct  |      8K       | 🤗 [HuggingFace](https://huggingface.co/infly/OpenCoder-8B-Instruct) |
+## 3. Datasets
+### Pre-training
+|         Dataset       | Size |                                Download                                 |
+|:---------------------:|:---------------:|:-----------------------------------------------------------------------:|
+| fineweb-code-corpus  |      148 GB       | 🤗 [HuggingFace](https://huggingface.co/datasets/OpenCoder-LLM/fineweb-code-corpus)  |
+| fineweb-math-corpus  |       10 GB    | 🤗 [HuggingFace](https://huggingface.co/datasets/OpenCoder-LLM/fineweb-math-corpus)  |
+**This is not the end; we are organizing the remaining data and uploading it progressively.**
+## 4. Benchmarks
 **Note:** For the detailed evaluation results, please refer to [our paper](https://arxiv.org/pdf/2411.04905).
 | MultiPL-E (AVG) |     57.5     |     71.0     | -->
+## 5. Inference
 ### Inference with Huggingface's Transformers
 <!-- ### Inference with vLLM (recommended) -->
+## 6. License
 OpenCoder series (including Base and Chat) support commercial applications under a permissive [License](https://huggingface.co/infly/OpenCoder-8B-Base/blob/main/LICENSE).
+## 7. Citation
 ```
 @inproceedings{Huang2024OpenCoderTO,
   title={OpenCoder: The Open Cookbook for Top-Tier Code Large Language Models},