Instructions to use TheBloke/orca_mini_v2_7B-GGML with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TheBloke/orca_mini_v2_7B-GGML with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="TheBloke/orca_mini_v2_7B-GGML")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("TheBloke/orca_mini_v2_7B-GGML", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use TheBloke/orca_mini_v2_7B-GGML with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "TheBloke/orca_mini_v2_7B-GGML" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "TheBloke/orca_mini_v2_7B-GGML", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/TheBloke/orca_mini_v2_7B-GGML
- SGLang
How to use TheBloke/orca_mini_v2_7B-GGML 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 "TheBloke/orca_mini_v2_7B-GGML" \ --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": "TheBloke/orca_mini_v2_7B-GGML", "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 "TheBloke/orca_mini_v2_7B-GGML" \ --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": "TheBloke/orca_mini_v2_7B-GGML", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use TheBloke/orca_mini_v2_7B-GGML with Docker Model Runner:
docker model run hf.co/TheBloke/orca_mini_v2_7B-GGML
Update README.md
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README.md
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inference: false
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license:
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---
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<!-- header start -->
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* [2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference](https://huggingface.co/TheBloke/orca_mini_v2_7B-GGML)
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* [Unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/psmathur/orca_mini_v2_7b)
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<!-- compatibility_ggml start -->
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## Compatibility
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I use the following command line; adjust for your tastes and needs:
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```
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./main -t 10 -ngl 32 -m orca-mini-v2_7b.ggmlv3.q5_0.bin --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "###
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```
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If you're able to use full GPU offloading, you should use `-t 1` to get best performance.
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inference: false
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license: cc-by-nc-sa-4.0
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language:
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- en
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library_name: transformers
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pipeline_tag: text-generation
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datasets:
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- psmathur/orca_minis_uncensored_dataset
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<!-- header start -->
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* [2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference](https://huggingface.co/TheBloke/orca_mini_v2_7B-GGML)
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* [Unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/psmathur/orca_mini_v2_7b)
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## Prompt template
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```
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### System:
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You are an AI assistant that follows instruction extremely well. Help as much as you can.
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### User:
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prompt
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### Input:
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### Response:
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```
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<!-- compatibility_ggml start -->
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## Compatibility
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I use the following command line; adjust for your tastes and needs:
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```
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./main -t 10 -ngl 32 -m orca-mini-v2_7b.ggmlv3.q5_0.bin --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "### System: You are an AI assistant that follows instruction extremely well. Help as much as you can.### User: Write a story about llamas\n### Response:"
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```
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If you're able to use full GPU offloading, you should use `-t 1` to get best performance.
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