Instructions to use Murasame0011/CulinaAI-Qwen2.5-7B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Murasame0011/CulinaAI-Qwen2.5-7B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Murasame0011/CulinaAI-Qwen2.5-7B-GGUF",
	filename="qwen2.5-7b-instruct.Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Murasame0011/CulinaAI-Qwen2.5-7B-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 Murasame0011/CulinaAI-Qwen2.5-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Murasame0011/CulinaAI-Qwen2.5-7B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Murasame0011/CulinaAI-Qwen2.5-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Murasame0011/CulinaAI-Qwen2.5-7B-GGUF:Q4_K_M

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 Murasame0011/CulinaAI-Qwen2.5-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Murasame0011/CulinaAI-Qwen2.5-7B-GGUF:Q4_K_M

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 Murasame0011/CulinaAI-Qwen2.5-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Murasame0011/CulinaAI-Qwen2.5-7B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/Murasame0011/CulinaAI-Qwen2.5-7B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use Murasame0011/CulinaAI-Qwen2.5-7B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Murasame0011/CulinaAI-Qwen2.5-7B-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Murasame0011/CulinaAI-Qwen2.5-7B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Murasame0011/CulinaAI-Qwen2.5-7B-GGUF:Q4_K_M

Ollama
How to use Murasame0011/CulinaAI-Qwen2.5-7B-GGUF with Ollama:
```
ollama run hf.co/Murasame0011/CulinaAI-Qwen2.5-7B-GGUF:Q4_K_M
```

Unsloth Studio

How to use Murasame0011/CulinaAI-Qwen2.5-7B-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 Murasame0011/CulinaAI-Qwen2.5-7B-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 Murasame0011/CulinaAI-Qwen2.5-7B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Murasame0011/CulinaAI-Qwen2.5-7B-GGUF to start chatting

How to use Murasame0011/CulinaAI-Qwen2.5-7B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Murasame0011/CulinaAI-Qwen2.5-7B-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "Murasame0011/CulinaAI-Qwen2.5-7B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Murasame0011/CulinaAI-Qwen2.5-7B-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Murasame0011/CulinaAI-Qwen2.5-7B-GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default Murasame0011/CulinaAI-Qwen2.5-7B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use Murasame0011/CulinaAI-Qwen2.5-7B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Murasame0011/CulinaAI-Qwen2.5-7B-GGUF:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "Murasame0011/CulinaAI-Qwen2.5-7B-GGUF:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use Murasame0011/CulinaAI-Qwen2.5-7B-GGUF with Docker Model Runner:
```
docker model run hf.co/Murasame0011/CulinaAI-Qwen2.5-7B-GGUF:Q4_K_M
```

Lemonade

How to use Murasame0011/CulinaAI-Qwen2.5-7B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Murasame0011/CulinaAI-Qwen2.5-7B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.CulinaAI-Qwen2.5-7B-GGUF-Q4_K_M

List all available models

lemonade list

CulinaAI - Qwen2.5-7B Recipe Generation (GGUF, Q4_K_M)

CulinaAI is a fine-tuned version of Qwen2.5-7B-Instruct, specialized for generating cooking recipes. It was trained using Unsloth Studio and exported to GGUF format for easy local inference.

Model Details

Base model: Qwen/Qwen2.5-7B-Instruct
Fine-tuning method: LoRA (QLoRA-style, 4-bit base model)
Training framework: Unsloth Studio
Format: GGUF, quantized to Q4_K_M (~4.68 GB)

Training Configuration

Hyperparameter	Value
Epochs	3
Batch size	2
Learning rate	2e-4
Optimizer	AdamW 8-bit
Context length	1024
Warmup steps	380
LoRA rank (r)	16
LoRA alpha	32
LoRA dropout	0.05

Final metrics:

Smoothed training loss: 0.941
Best eval loss: 0.719

Usage

This model is in GGUF format and works with:

llama.cpp
Ollama
LM Studio
Unsloth Studio itself

Ollama

ollama run hf.co/Murasame0011/CulinaAI-Qwen2.5-7B-GGUF

Example Prompt

### Instruction:
Give me a recipe for a quick vegetarian dinner using chickpeas.

### Response:
[model generates a recipe]

(Update this with an actual example from your model's output.)

Limitations

Generated recipes may contain inaccurate quantities, cooking times, or steps. Always use judgment and review before cooking.
The model may occasionally hallucinate ingredients or techniques that aren't standard.
Trained with a 1024-token context window, so very long recipes or multi-recipe requests may be truncated.

Training Data

Fine-tuned on a 22,000-recipe subset drawn from an enhanced version of the Recipe1M+ dataset (MIT CSAIL). The full enhanced source dataset contains 1,023,489 recipes with the following columns, from which this training subset was sampled:

title,
meal_type, primary_classification, secondary_classification, context_dependent_classification — recipe categorization
ingredients, NER — ingredient lists (raw and NER-extracted entities)
instructions — step-by-step cooking instructions

Note: This repository contains only the fine-tuned model weights, not the training dataset itself. Recipe1M+ is distributed under MIT's own terms of access — refer to the official Recipe1M+ site for dataset licensing if you plan to redistribute the data.

Downloads last month: 277

GGUF

Model size

8B params

Architecture

qwen2

Hardware compatibility

4-bit

Model tree for Murasame0011/CulinaAI-Qwen2.5-7B-GGUF

Base model

Qwen/Qwen2.5-7B

Finetuned

Qwen/Qwen2.5-7B-Instruct

Adapter

(2368)

this model