Instructions to use park-bit/ProFAQLM-3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use park-bit/ProFAQLM-3B with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-3B-Instruct")
model = PeftModel.from_pretrained(base_model, "park-bit/ProFAQLM-3B")

Transformers

How to use park-bit/ProFAQLM-3B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="park-bit/ProFAQLM-3B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("park-bit/ProFAQLM-3B", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use park-bit/ProFAQLM-3B 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 park-bit/ProFAQLM-3B:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf park-bit/ProFAQLM-3B:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf park-bit/ProFAQLM-3B:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf park-bit/ProFAQLM-3B: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 park-bit/ProFAQLM-3B:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf park-bit/ProFAQLM-3B: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 park-bit/ProFAQLM-3B:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf park-bit/ProFAQLM-3B:Q4_K_M

Use Docker

docker model run hf.co/park-bit/ProFAQLM-3B:Q4_K_M

LM Studio
Jan

vLLM

How to use park-bit/ProFAQLM-3B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "park-bit/ProFAQLM-3B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "park-bit/ProFAQLM-3B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/park-bit/ProFAQLM-3B:Q4_K_M

SGLang

How to use park-bit/ProFAQLM-3B with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "park-bit/ProFAQLM-3B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "park-bit/ProFAQLM-3B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "park-bit/ProFAQLM-3B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "park-bit/ProFAQLM-3B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use park-bit/ProFAQLM-3B with Ollama:
```
ollama run hf.co/park-bit/ProFAQLM-3B:Q4_K_M
```
Unsloth Desktop

How to use park-bit/ProFAQLM-3B with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf park-bit/ProFAQLM-3B:Q4_K_M

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": "park-bit/ProFAQLM-3B:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use park-bit/ProFAQLM-3B with Docker Model Runner:
```
docker model run hf.co/park-bit/ProFAQLM-3B:Q4_K_M
```

Lemonade

How to use park-bit/ProFAQLM-3B with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull park-bit/ProFAQLM-3B:Q4_K_M

Run and chat with the model

lemonade run user.ProFAQLM-3B-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use park-bit/ProFAQLM-3B with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf park-bit/ProFAQLM-3B: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 park-bit/ProFAQLM-3B:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use park-bit/ProFAQLM-3B with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf park-bit/ProFAQLM-3B: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 "park-bit/ProFAQLM-3B: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"

ProFAQLM-3B / README.md

park-bit

Add trained ProFAQLM LoRA adapter

d3b89a0 verified 18 days ago

preview code

Raw History Blame Contribute Delete

1.48 kB

	---
	base_model: Qwen/Qwen2.5-3B-Instruct
	library_name: peft
	model_name: lora_adapter
	tags:
	- base_model:adapter:Qwen/Qwen2.5-3B-Instruct
	- lora
	- sft
	- transformers
	- trl
	licence: license
	pipeline_tag: text-generation
	---

	# Model Card for lora_adapter

	This model is a fine-tuned version of [Qwen/Qwen2.5-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-3B-Instruct).
	It has been trained using [TRL](https://github.com/huggingface/trl).

	## Quick start

	```python
	from transformers import pipeline

	question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
	generator = pipeline("text-generation", model="None", device_map="auto")
	output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
	print(output["generated_text"])
	```

	## Training procedure





	This model was trained with SFT.

	### Framework versions

	- PEFT 0.19.1
	- TRL: 1.14.0
	- Transformers: 5.0.0
	- Pytorch: 2.10.0+cu128
	- Datasets: 5.0.0
	- Tokenizers: 0.22.2

	## Citations



	Cite TRL as:

	```bibtex
	@software{vonwerra2020trl,
	title = {{TRL: Transformers Reinforcement Learning}},
	author = {von Werra, Leandro and Belkada, Younes and Tunstall, Lewis and Beeching, Edward and Thrush, Tristan and Lambert, Nathan and Huang, Shengyi and Rasul, Kashif and Gallouédec, Quentin},
	license = {Apache-2.0},
	url = {https://github.com/huggingface/trl},
	year = {2020}
	}
	```