Instructions to use internlm/AlchemistCoder-CL-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use internlm/AlchemistCoder-CL-7B with Transformers:

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

pipe = pipeline("text-generation", model="internlm/AlchemistCoder-CL-7B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("internlm/AlchemistCoder-CL-7B")
model = AutoModelForCausalLM.from_pretrained("internlm/AlchemistCoder-CL-7B")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use internlm/AlchemistCoder-CL-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "internlm/AlchemistCoder-CL-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "internlm/AlchemistCoder-CL-7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/internlm/AlchemistCoder-CL-7B

SGLang

How to use internlm/AlchemistCoder-CL-7B 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 "internlm/AlchemistCoder-CL-7B" \
    --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": "internlm/AlchemistCoder-CL-7B",
		"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 "internlm/AlchemistCoder-CL-7B" \
        --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": "internlm/AlchemistCoder-CL-7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use internlm/AlchemistCoder-CL-7B with Docker Model Runner:
```
docker model run hf.co/internlm/AlchemistCoder-CL-7B
```

MatCaviar commited on May 30, 2024

Commit

80a7392

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1 Parent(s): 8396408

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@@ -7,7 +7,7 @@ tags:
 # AlchemistCoder: Harmonizing and Eliciting Code Capability by Hindsight Tuning on Multi-source Data
 [[🤗 HuggingFace](https://huggingface.co/internlm/AlchemistCoder-DS-6.7B)]
-[[📃 Paper](https://arxiv.org/abs/xxxxx)]
 [[🌐 Project Page](https://internlm.github.io/AlchemistCoder/)]
@@ -52,5 +52,12 @@ If you have any questions, please create an issue on this repository or contact
 If you find our work useful, please consider citing:
 ```bibtex
 ```

 # AlchemistCoder: Harmonizing and Eliciting Code Capability by Hindsight Tuning on Multi-source Data
 [[🤗 HuggingFace](https://huggingface.co/internlm/AlchemistCoder-DS-6.7B)]
+[[📃 Paper](https://arxiv.org/abs/2405.19265)]
 [[🌐 Project Page](https://internlm.github.io/AlchemistCoder/)]
 If you find our work useful, please consider citing:
 ```bibtex
+@misc{song2024alchemistcoder,
+      title={AlchemistCoder: Harmonizing and Eliciting Code Capability by Hindsight Tuning on Multi-source Data},
+      author={Zifan Song and Yudong Wang and Wenwei Zhang and Kuikun Liu and Chengqi Lyu and Demin Song and Qipeng Guo and Hang Yan and Dahua Lin and Kai Chen and Cairong Zhao},
+      year={2024},
+      eprint={2405.19265},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
 ```