Instructions to use OrcsRise/qmd-query-expansion-lfm2-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OrcsRise/qmd-query-expansion-lfm2-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="OrcsRise/qmd-query-expansion-lfm2-gguf",
	filename="qmd-query-expansion-lfm2-q8_0.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use OrcsRise/qmd-query-expansion-lfm2-gguf with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf OrcsRise/qmd-query-expansion-lfm2-gguf:Q8_0
# Run inference directly in the terminal:
llama-cli -hf OrcsRise/qmd-query-expansion-lfm2-gguf:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf OrcsRise/qmd-query-expansion-lfm2-gguf:Q8_0
# Run inference directly in the terminal:
llama-cli -hf OrcsRise/qmd-query-expansion-lfm2-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 OrcsRise/qmd-query-expansion-lfm2-gguf:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf OrcsRise/qmd-query-expansion-lfm2-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 OrcsRise/qmd-query-expansion-lfm2-gguf:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf OrcsRise/qmd-query-expansion-lfm2-gguf:Q8_0

Use Docker

docker model run hf.co/OrcsRise/qmd-query-expansion-lfm2-gguf:Q8_0

LM Studio
Jan

vLLM

How to use OrcsRise/qmd-query-expansion-lfm2-gguf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "OrcsRise/qmd-query-expansion-lfm2-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": "OrcsRise/qmd-query-expansion-lfm2-gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/OrcsRise/qmd-query-expansion-lfm2-gguf:Q8_0

Ollama
How to use OrcsRise/qmd-query-expansion-lfm2-gguf with Ollama:
```
ollama run hf.co/OrcsRise/qmd-query-expansion-lfm2-gguf:Q8_0
```

Unsloth Studio new

How to use OrcsRise/qmd-query-expansion-lfm2-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 OrcsRise/qmd-query-expansion-lfm2-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 OrcsRise/qmd-query-expansion-lfm2-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for OrcsRise/qmd-query-expansion-lfm2-gguf to start chatting

Pi new

How to use OrcsRise/qmd-query-expansion-lfm2-gguf with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf OrcsRise/qmd-query-expansion-lfm2-gguf:Q8_0

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": "OrcsRise/qmd-query-expansion-lfm2-gguf:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use OrcsRise/qmd-query-expansion-lfm2-gguf with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf OrcsRise/qmd-query-expansion-lfm2-gguf:Q8_0

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 OrcsRise/qmd-query-expansion-lfm2-gguf:Q8_0

Run Hermes

hermes

Docker Model Runner
How to use OrcsRise/qmd-query-expansion-lfm2-gguf with Docker Model Runner:
```
docker model run hf.co/OrcsRise/qmd-query-expansion-lfm2-gguf:Q8_0
```

Lemonade

How to use OrcsRise/qmd-query-expansion-lfm2-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull OrcsRise/qmd-query-expansion-lfm2-gguf:Q8_0

Run and chat with the model

lemonade run user.qmd-query-expansion-lfm2-gguf-Q8_0

List all available models

lemonade list

QMD Query Expansion — LFM2-1.2B GGUF

GGUF quantization of a fine-tuned LiquidAI/LFM2-1.2B for structured query expansion in qmd, a local-first document search engine.

Fine-tuned adapter: OrcsRise/qmd-query-expansion-lfm2-sft

Quantizations

File	Quantization	Size	Use Case
`qmd-query-expansion-lfm2-q8_0.gguf`	Q8_0	1.19 GB	Recommended — near-original quality

Quick Start with qmd

# Set as your qmd query expansion model
export QMD_GEN_MODEL="hf:OrcsRise/qmd-query-expansion-lfm2-gguf/qmd-query-expansion-lfm2-q8_0.gguf"

# Add to ~/.zshrc or ~/.bashrc for persistence
echo 'export QMD_GEN_MODEL="hf:OrcsRise/qmd-query-expansion-lfm2-gguf/qmd-query-expansion-lfm2-q8_0.gguf"' >> ~/.zshrc

# qmd auto-downloads the GGUF on first use
qmd query "your search query"

The model is automatically downloaded to ~/.cache/qmd/models/ on first run.

What This Model Does

Given a short search query, the model generates structured expansions in three formats for hybrid search:

Prefix	Purpose	Example
`lex:`	Lexical keywords for BM25/FTS5 search	`lex: docker container timeout settings`
`vec:`	Natural language for vector similarity search	`vec: how to configure docker container timeout`
`hyde:`	Hypothetical document for HyDE retrieval	`hyde: Docker containers can be configured with timeout settings using the --stop-timeout flag...`

Why LFM2 over Qwen3?

This is an alternative to qmd's default Qwen3-1.7B query expansion model, added in qmd v1.0.7.

	LFM2-1.2B (this)	Qwen3-1.7B (default)
Parameters	1.2B	1.7B
Architecture	Hybrid (convolutions + attention)	Standard transformer
Decode/prefill speed	~2x faster	Baseline
Q8_0 size	1.19 GB	~1.7 GB
Best for	On-device, latency-sensitive	Maximum quality

LFM2's hybrid architecture makes it ideal for on-device inference where latency and memory matter more than marginal quality differences.

Training

Method: SFT with LoRA (rank 16, alpha 32)
Dataset: tobil/qmd-query-expansion-train — 5,157 examples
LoRA targets: q_proj, k_proj, v_proj, out_proj, in_proj, w1, w2, w3
Epochs: 5
Hardware: NVIDIA Tesla T4 (Google Colab, free tier)
Training time: ~2.5 hours

See the SFT adapter card for full training details.

Recommended Generation Parameters

Parameter	Value
Temperature	0.3
min_p	0.15
Repetition penalty	1.05

Compatibility

This GGUF works with any inference engine that supports the LFM2 architecture:

qmd (via node-llama-cpp) — primary use case
llama.cpp (b5921+)
Ollama
LM Studio

qmd — Local-first document search with BM25 + vector + LLM reranking
LiquidAI/LFM2-1.2B — Base model
LiquidAI/LFM2-1.2B-GGUF — Official base model GGUFs (not fine-tuned)
OrcsRise/qmd-query-expansion-lfm2-sft — SFT LoRA adapter
tobil/qmd-query-expansion-1.7B-gguf — Default qmd model (Qwen3-1.7B)
tobil/qmd-query-expansion-train — Training dataset

License

Apache 2.0 — same as the base LFM2-1.2B model.

Downloads last month: 38

GGUF

Model size

1B params

Architecture

lfm2

Hardware compatibility

8-bit

Model tree for OrcsRise/qmd-query-expansion-lfm2-gguf

Base model

LiquidAI/LFM2-1.2B

Quantized

(30)

this model

OrcsRise
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qmd-query-expansion-lfm2-gguf