Instructions to use sugiv/leetmonkey-peft-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sugiv/leetmonkey-peft-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="sugiv/leetmonkey-peft-gguf",
	filename="leetmonkey_peft__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 sugiv/leetmonkey-peft-gguf with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf sugiv/leetmonkey-peft-gguf:Q8_0
# Run inference directly in the terminal:
llama-cli -hf sugiv/leetmonkey-peft-gguf:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf sugiv/leetmonkey-peft-gguf:Q8_0
# Run inference directly in the terminal:
llama-cli -hf sugiv/leetmonkey-peft-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 sugiv/leetmonkey-peft-gguf:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf sugiv/leetmonkey-peft-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 sugiv/leetmonkey-peft-gguf:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf sugiv/leetmonkey-peft-gguf:Q8_0

Use Docker

docker model run hf.co/sugiv/leetmonkey-peft-gguf:Q8_0

LM Studio
Jan
Ollama
How to use sugiv/leetmonkey-peft-gguf with Ollama:
```
ollama run hf.co/sugiv/leetmonkey-peft-gguf:Q8_0
```

Unsloth Studio

How to use sugiv/leetmonkey-peft-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 sugiv/leetmonkey-peft-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 sugiv/leetmonkey-peft-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for sugiv/leetmonkey-peft-gguf to start chatting

Docker Model Runner
How to use sugiv/leetmonkey-peft-gguf with Docker Model Runner:
```
docker model run hf.co/sugiv/leetmonkey-peft-gguf:Q8_0
```

Lemonade

How to use sugiv/leetmonkey-peft-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull sugiv/leetmonkey-peft-gguf:Q8_0

Run and chat with the model

lemonade run user.leetmonkey-peft-gguf-Q8_0

List all available models

lemonade list

YAML Metadata Warning:empty or missing yaml metadata in repo card

Check out the documentation for more information.

LeetMonkey PEFT GGUF Model

This repository contains the GGUF version of the LeetMonkey PEFT model, fine-tuned for solving LeetCode problems.

Model Details

Base Model: DeepSeek Coder 6.7B
Fine-tuning: PEFT (Parameter-Efficient Fine-Tuning), LeetMonkey PEFT Model
Format: GGUF (GPT-Generated Unified Format)
Use Case: Generating Python solutions for LeetCode problems

Usage

This model can be used with libraries that support GGUF format, such as llama.cpp.

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GGUF

Model size

7B params

Architecture

llama

Hardware compatibility

8-bit

16-bit

View +2 variants

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sugiv
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leetmonkey-peft-gguf

LeetMonkey PEFT GGUF Model

Model Details

Usage

Spaces using sugiv/leetmonkey-peft-gguf 3