Instructions to use AfkaraLP/rustlean-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AfkaraLP/rustlean-gguf with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("AfkaraLP/rustlean-gguf", device_map="auto") - llama-cpp-python
How to use AfkaraLP/rustlean-gguf with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="AfkaraLP/rustlean-gguf", filename="rustlean-final.Q8_0.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use AfkaraLP/rustlean-gguf with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf AfkaraLP/rustlean-gguf:Q8_0 # Run inference directly in the terminal: llama cli -hf AfkaraLP/rustlean-gguf:Q8_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf AfkaraLP/rustlean-gguf:Q8_0 # Run inference directly in the terminal: llama cli -hf AfkaraLP/rustlean-gguf:Q8_0
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf AfkaraLP/rustlean-gguf:Q8_0 # Run inference directly in the terminal: ./llama-cli -hf AfkaraLP/rustlean-gguf:Q8_0
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf AfkaraLP/rustlean-gguf:Q8_0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf AfkaraLP/rustlean-gguf:Q8_0
Use Docker
docker model run hf.co/AfkaraLP/rustlean-gguf:Q8_0
- LM Studio
- Jan
- Ollama
How to use AfkaraLP/rustlean-gguf with Ollama:
ollama run hf.co/AfkaraLP/rustlean-gguf:Q8_0
- Unsloth Studio
How to use AfkaraLP/rustlean-gguf with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for AfkaraLP/rustlean-gguf to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for AfkaraLP/rustlean-gguf to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for AfkaraLP/rustlean-gguf to start chatting
- Atomic Chat new
- Docker Model Runner
How to use AfkaraLP/rustlean-gguf with Docker Model Runner:
docker model run hf.co/AfkaraLP/rustlean-gguf:Q8_0
- Lemonade
How to use AfkaraLP/rustlean-gguf with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull AfkaraLP/rustlean-gguf:Q8_0
Run and chat with the model
lemonade run user.rustlean-gguf-Q8_0
List all available models
lemonade list
File size: 1,212 Bytes
4a1a729 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 | {# RustLean — native fill-in-the-middle (FIM) Rust completion model (Qwen2.5-Coder-1.5B base).
This model was trained with the Qwen2.5-Coder FIM objective:
<|fim_prefix|>{prefix}<|fim_suffix|>{suffix}<|fim_middle|>{middle}<|endoftext|>
The dominant serving mode (per the technical report) is *prefix completion*
with an empty suffix, i.e. the model is fed:
<|fim_prefix|>{prefix}<|fim_suffix|><|fim_middle|>
and generates the missing middle. This template implements exactly that for a
single user turn: the user message content is treated as the prefix and the
FIM markers are emitted around it.
Multi-turn / suffix-aware infilling is handled natively by llama.cpp via the
auto-detected <|fim_prefix|>/<|fim_suffix|>/<|fim_middle|> special tokens
(infill endpoint / --fim-* flags), which does not use this template.
llama.cpp variable contract: `messages`, `add_generation_prompt`.
#}
{%- if messages -%}
{%- set ns = namespace(prefix="") -%}
{%- for message in messages -%}
{%- if message["role"] == "user" -%}
{%- set ns.prefix = ns.prefix + message["content"] -%}
{%- endif -%}
{%- endfor -%}
<|fim_prefix|>{{ ns.prefix }}<|fim_suffix|><|fim_middle|>
{%- else -%}
{{ prompt }}
{%- endif -%}
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