Instructions to use codingsoo/LlamaREST-IPD-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use codingsoo/LlamaREST-IPD-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 codingsoo/LlamaREST-IPD-GGUF:F16 # Run inference directly in the terminal: llama cli -hf codingsoo/LlamaREST-IPD-GGUF:F16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf codingsoo/LlamaREST-IPD-GGUF:F16 # Run inference directly in the terminal: llama cli -hf codingsoo/LlamaREST-IPD-GGUF:F16
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 codingsoo/LlamaREST-IPD-GGUF:F16 # Run inference directly in the terminal: ./llama-cli -hf codingsoo/LlamaREST-IPD-GGUF:F16
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 codingsoo/LlamaREST-IPD-GGUF:F16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf codingsoo/LlamaREST-IPD-GGUF:F16
Use Docker
docker model run hf.co/codingsoo/LlamaREST-IPD-GGUF:F16
- LM Studio
- Jan
- vLLM
How to use codingsoo/LlamaREST-IPD-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "codingsoo/LlamaREST-IPD-GGUF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "codingsoo/LlamaREST-IPD-GGUF", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/codingsoo/LlamaREST-IPD-GGUF:F16
- Ollama
How to use codingsoo/LlamaREST-IPD-GGUF with Ollama:
ollama run hf.co/codingsoo/LlamaREST-IPD-GGUF:F16
- Unsloth Desktop
- Docker Model Runner
How to use codingsoo/LlamaREST-IPD-GGUF with Docker Model Runner:
docker model run hf.co/codingsoo/LlamaREST-IPD-GGUF:F16
- Lemonade
How to use codingsoo/LlamaREST-IPD-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull codingsoo/LlamaREST-IPD-GGUF:F16
Run and chat with the model
lemonade run user.LlamaREST-IPD-GGUF-F16
List all available models
lemonade list
- Atomic Chat
LlamaREST-IPD (GGUF)
LlamaREST-IPD is a Small Language Model (SLM) fine-tuned to predict inter-parameter dependencies (IPDs) among REST API parameters, used by LlamaRestTest to improve automated REST API test generation.
It is one of the two SLMs used by LlamaRestTest; the companion model, LlamaREST-EX, generates realistic example values for parameters.
Models
Quantized with llama.cpp (Q6_K) from a Llama-3-8B base fine-tuned with QLoRA.
| Variant | File | Size |
|---|---|---|
| 2B | LlamaREST-IPD-2B.gguf |
~3.2 GB |
| 4B | LlamaREST-IPD-4B.gguf |
~4.9 GB |
| 8B | LlamaREST-IPD-8B.gguf |
~8.5 GB |
| F16 | Llama3-Ipd-Rest-8.0B-F16.gguf |
~16 GB |
Download
pip install -U "huggingface_hub[cli]"
hf download codingsoo/LlamaREST-IPD-GGUF LlamaREST-IPD-8B.gguf --local-dir .
Usage
Intended to be run through the LlamaRestTest
pipeline. To run the GGUF directly with llama.cpp:
./llama-cli -m LlamaREST-IPD-8B.gguf -p "your prompt"
Training
Fine-tuned from Llama-3-8B with QLoRA, then quantized with llama.cpp (Q6_K). Key hyperparameters:
- 4-bit precision (nf4), compute dtype
float16, no nested quantization - LoRA:
r=64,alpha=16, dropout0.1 - 5 epochs, batch size 4,
paged_adamw_32bit, LR2e-4, weight decay0.001, constant schedule, warmup ratio0.03
Training data (Inter-Parameter Dependency): random1234321/REST-IPD.
License
The training/testing code is released under the MIT License. The model weights are derived from Meta Llama 3 and are therefore subject to the Meta Llama 3 Community License.
Citation
@article{kim2025llamaresttest,
title={LlamaRestTest: Effective REST API Testing with Small Language Models},
author={Kim, Myeongsoo and Sinha, Saurabh and Orso, Alessandro},
journal={arXiv preprint arXiv:2501.08598},
year={2025}
}
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Model tree for codingsoo/LlamaREST-IPD-GGUF
Base model
meta-llama/Meta-Llama-3-8B