Instructions to use farhadabas/test-project with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use farhadabas/test-project with LiteRT:

# No code snippets available yet for this library.

# To use this model, check the repository files and the library's documentation.

# Want to help? PRs adding snippets are welcome at:
# https://github.com/huggingface/huggingface.js

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use farhadabas/test-project 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 farhadabas/test-project:F16
# Run inference directly in the terminal:
llama cli -hf farhadabas/test-project:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf farhadabas/test-project:F16
# Run inference directly in the terminal:
llama cli -hf farhadabas/test-project:F16

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf farhadabas/test-project:F16
# Run inference directly in the terminal:
./build/bin/llama cli -hf farhadabas/test-project:F16

Use Docker

docker model run hf.co/farhadabas/test-project:F16

Llama new
LM Studio
Jan

vLLM

How to use farhadabas/test-project with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/farhadabas/test-project:F16

Ollama
How to use farhadabas/test-project with Ollama:
```
ollama run hf.co/farhadabas/test-project:F16
```
Unsloth Desktop

How to use farhadabas/test-project with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf farhadabas/test-project:F16

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "farhadabas/test-project:F16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use farhadabas/test-project with Docker Model Runner:
```
docker model run hf.co/farhadabas/test-project:F16
```

Lemonade

How to use farhadabas/test-project with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull farhadabas/test-project:F16

Run and chat with the model

lemonade run user.test-project-F16

List all available models

lemonade list

Hermes Agent

How to use farhadabas/test-project with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf farhadabas/test-project:F16

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 farhadabas/test-project:F16

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use farhadabas/test-project with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf farhadabas/test-project:F16

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 "farhadabas/test-project:F16" \
  --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"

test-project

File size: 2,222 Bytes

---
license: apache-2.0
base_model: "Qwen/Qwen3-0.6B"
base_model_relation: finetune
pipeline_tag: text-generation
library_name: gguf
tags:
- quantized
- lora
- litert
- gguf
---

# test-project

basewise

## Model provenance

This model was modified from [Qwen/Qwen3-0.6B](https://huggingface.co/Qwen/Qwen3-0.6B/tree/c1899de289a04d12100db370d81485cdf75e47ca) at revision `c1899de289a04d12100db370d81485cdf75e47ca`.
These files are exports of LoRA fine-tuned, merged models. They share the verified base revision above; different exports may come from different fine-tuning checkpoints.

## Downloads and runtime guidance

### gguf — F16

[model-F16.gguf](model-F16.gguf)

SHA-256: `ccb6c34d4169aff4982f007e963cc7f780e53231f8a6495247902880d6170540`. Size: 1198182080 bytes.

Use with LM Studio or a compatible llama.cpp/GGUF runtime. F16 is a nonquantized, half-precision (16-bit floating-point) export of the merged model. The upstream chat template is preserved; disable thinking in runtime or chat-template options for this model, which was fine-tuned with no-thinking prompts.

### litertlm — dynamic_wi4b32_afp32

[model-int4-block32.litertlm](model-int4-block32.litertlm)

SHA-256: `6995957e1d1ef4250c93a85c9ac2993366fcf2a488f0ea610852b20dd47d8452`. Size: 341233184 bytes.

Use with LiteRT-LM. This export uses dynamic 4-bit weights in blocks of 32 with FP32 activations (dynamic_wi4b32_afp32) and runtime metadata with enableThinking=false.

### litertlm — dynamic_wi4_afp32

[model.litertlm](model.litertlm)

SHA-256: `64abcacbeca4b9f5a26c1de3a44e65db4d64dd676dc191fea155085dc67fe515`. Size: 315485328 bytes.

Use with LiteRT-LM. This export uses dynamic 4-bit weights with FP32 activations (dynamic_wi4_afp32) and runtime metadata with enableThinking=false.

## Limitations

Fine-tuning and quantization can change model behavior. No general quality, safety, or device compatibility claims are made. Evaluate on your own tasks and target runtime before use. Training examples, private datasets, and logs are not included.

## License and notices

The base license is preserved in LICENSE. See NOTICE for upstream attribution and changes. LiteRT metadata licensing is preserved in licenses/LICENSE-LiteRT-LM.