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
# 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")Model Card for rustral-2-2k-2024-1-6
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.
Model Details
Model Description
- Developed by: Leon Luithlen
- Model type: Mistral-7B-v0.1 variant
- Language(s) (NLP): Rust, English
- License: apache-2.0
- Finetuned from model [optional]: Mistral-7B-v0.1
Uses
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.
Direct Use
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:
Below is an instruction that describes a task, paired with an input that
provides further context. Write a response that appropriately completes the
request.
### Instruction:
{instruction}
### Input:
{up_to_two_previous_snippets}
### Response:
Out-of-Scope Use
Any programming language other than Rust, natural languages
Bias, Risks, and Limitations
Currently not evaluated systematically, as there is no agreed benchmark for code assistance tasks in Rust.
For uses outside its intended scope, the model is likely to behave similarly to Mistral-7B, but probably worse.
Recommendations
The same caveats that apply to Mistral-7B apply to this model as well.
Training Details
Training Data
Training data is sourced from open source and permissively licensed Rust projects, but is not public.
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="0xideas/rustral")