Text Generation
Transformers
PyTorch
Chinese
English
chatglm
codegeex
glm
custom_code
Eval Results (legacy)
Instructions to use bigcode/octogeex with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bigcode/octogeex with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="bigcode/octogeex", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("bigcode/octogeex", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use bigcode/octogeex with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "bigcode/octogeex" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "bigcode/octogeex", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/bigcode/octogeex
- SGLang
How to use bigcode/octogeex 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 "bigcode/octogeex" \ --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": "bigcode/octogeex", "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 "bigcode/octogeex" \ --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": "bigcode/octogeex", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use bigcode/octogeex with Docker Model Runner:
docker model run hf.co/bigcode/octogeex
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# Model Summary
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OctoGeeX is an instruction tuned model with 6B parameters created by fine-tuning [CodeGeeX2](https://huggingface.co/THUDM/codegeex2-6b) on [CommitPackFT](https://huggingface.co/datasets/bigcode/commitpackft) & [OASST](https://huggingface.co/datasets/bigcode/oasst-octopack) as described in the OctoPack paper.
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- **Repository:** [bigcode-project/octopack](https://github.com/bigcode-project/octopack)
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- **Paper:** [OctoPack: Instruction Tuning Code Large Language Models](https://arxiv.org/abs/2308.07124)
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# Model Summary
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> OctoGeeX is an instruction tuned model with 6B parameters created by fine-tuning [CodeGeeX2](https://huggingface.co/THUDM/codegeex2-6b) on [CommitPackFT](https://huggingface.co/datasets/bigcode/commitpackft) & [OASST](https://huggingface.co/datasets/bigcode/oasst-octopack) as described in the OctoPack paper.
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- **Repository:** [bigcode-project/octopack](https://github.com/bigcode-project/octopack)
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- **Paper:** [OctoPack: Instruction Tuning Code Large Language Models](https://arxiv.org/abs/2308.07124)
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