Instructions to use 0xideas/rustral with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use 0xideas/rustral with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="0xideas/rustral")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("0xideas/rustral") model = AutoModelForCausalLM.from_pretrained("0xideas/rustral", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use 0xideas/rustral with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "0xideas/rustral" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "0xideas/rustral", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/0xideas/rustral
- SGLang
How to use 0xideas/rustral 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 "0xideas/rustral" \ --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": "0xideas/rustral", "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 "0xideas/rustral" \ --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": "0xideas/rustral", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use 0xideas/rustral with Docker Model Runner:
docker model run hf.co/0xideas/rustral
Leon Luithlen commited on
Commit ·
20a099e
1
Parent(s): c5d46ae
Add model card
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---
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# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
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# Doc / guide: https://huggingface.co/docs/hub/model-cards
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{{ card_data }}
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---
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# Model Card for rustral-2-2k-2024-1-6
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This is an instruction finetune of Mistral-7B-v0.1 on a custom dataset of Rust code snippets. The current version was finetuned on 2k examples for 3 epochs, with instructions being extracted from the comments of the code itself. Future versions will be based on more data and generated instructions.
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## Model Details
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### Model Description
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<!-- Provide a longer summary of what this model is. -->
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{{ model_description | default("", true) }}
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- **Developed by:** Leon Luithlen
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- **Model type:** Mistral-7B-v0.1 variant
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- **Language(s) (NLP):** Rust, English
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- **License:** apache-2.0
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- **Finetuned from model [optional]:** Mistral-7B-v0.1
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## Uses
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This model is intended as a code assistant that develops short snippets, such as a single Rust struct or function, from the immediately preceding code and an instruction.
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### Direct Use
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After compiling to the appropriate quantization size for your hardware with llama.cpp, it can be used like other Mistral-7B variants with the following input format:
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```
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Below is an instruction that describes a task, paired with an input that
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provides further context. Write a response that appropriately completes the
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request.
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### Instruction:
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{instruction}
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### Input:
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{up_to_two_previous_snippets}
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### Response:
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```
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### Out-of-Scope Use
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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Any programming language other than Rust, natural languages
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## Bias, Risks, and Limitations
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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Currently not evaluated systematically, as there is no agreed benchmark for code assistance tasks in Rust.
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For uses outside its intended scope, the model is likely to behave similarly to Mistral-7B, but probably worse.
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### Recommendations
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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The same caveats that apply to Mistral-7B apply to this model as well.
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## Training Details
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### Training Data
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Training data is sourced from open source and permissively licensed Rust projects, but is not public.
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