Instructions to use langleu/qmd-query-expansion-lfm2.5-1.2b-instruct-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use langleu/qmd-query-expansion-lfm2.5-1.2b-instruct-v2 with Transformers:

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

pipe = pipeline("text-generation", model="langleu/qmd-query-expansion-lfm2.5-1.2b-instruct-v2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("langleu/qmd-query-expansion-lfm2.5-1.2b-instruct-v2")
model = AutoModelForCausalLM.from_pretrained("langleu/qmd-query-expansion-lfm2.5-1.2b-instruct-v2", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use langleu/qmd-query-expansion-lfm2.5-1.2b-instruct-v2 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 langleu/qmd-query-expansion-lfm2.5-1.2b-instruct-v2:Q5_K_M
# Run inference directly in the terminal:
llama cli -hf langleu/qmd-query-expansion-lfm2.5-1.2b-instruct-v2:Q5_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf langleu/qmd-query-expansion-lfm2.5-1.2b-instruct-v2:Q5_K_M
# Run inference directly in the terminal:
llama cli -hf langleu/qmd-query-expansion-lfm2.5-1.2b-instruct-v2:Q5_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 langleu/qmd-query-expansion-lfm2.5-1.2b-instruct-v2:Q5_K_M
# Run inference directly in the terminal:
./llama-cli -hf langleu/qmd-query-expansion-lfm2.5-1.2b-instruct-v2:Q5_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 langleu/qmd-query-expansion-lfm2.5-1.2b-instruct-v2:Q5_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf langleu/qmd-query-expansion-lfm2.5-1.2b-instruct-v2:Q5_K_M

Use Docker

docker model run hf.co/langleu/qmd-query-expansion-lfm2.5-1.2b-instruct-v2:Q5_K_M

LM Studio
Jan

vLLM

How to use langleu/qmd-query-expansion-lfm2.5-1.2b-instruct-v2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "langleu/qmd-query-expansion-lfm2.5-1.2b-instruct-v2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "langleu/qmd-query-expansion-lfm2.5-1.2b-instruct-v2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/langleu/qmd-query-expansion-lfm2.5-1.2b-instruct-v2:Q5_K_M

SGLang

How to use langleu/qmd-query-expansion-lfm2.5-1.2b-instruct-v2 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 "langleu/qmd-query-expansion-lfm2.5-1.2b-instruct-v2" \
    --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": "langleu/qmd-query-expansion-lfm2.5-1.2b-instruct-v2",
		"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 "langleu/qmd-query-expansion-lfm2.5-1.2b-instruct-v2" \
        --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": "langleu/qmd-query-expansion-lfm2.5-1.2b-instruct-v2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use langleu/qmd-query-expansion-lfm2.5-1.2b-instruct-v2 with Ollama:
```
ollama run hf.co/langleu/qmd-query-expansion-lfm2.5-1.2b-instruct-v2:Q5_K_M
```

Unsloth Studio

How to use langleu/qmd-query-expansion-lfm2.5-1.2b-instruct-v2 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 langleu/qmd-query-expansion-lfm2.5-1.2b-instruct-v2 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 langleu/qmd-query-expansion-lfm2.5-1.2b-instruct-v2 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for langleu/qmd-query-expansion-lfm2.5-1.2b-instruct-v2 to start chatting

How to use langleu/qmd-query-expansion-lfm2.5-1.2b-instruct-v2 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf langleu/qmd-query-expansion-lfm2.5-1.2b-instruct-v2:Q5_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": "langleu/qmd-query-expansion-lfm2.5-1.2b-instruct-v2:Q5_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use langleu/qmd-query-expansion-lfm2.5-1.2b-instruct-v2 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf langleu/qmd-query-expansion-lfm2.5-1.2b-instruct-v2:Q5_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 langleu/qmd-query-expansion-lfm2.5-1.2b-instruct-v2:Q5_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use langleu/qmd-query-expansion-lfm2.5-1.2b-instruct-v2 with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf langleu/qmd-query-expansion-lfm2.5-1.2b-instruct-v2:Q5_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 "langleu/qmd-query-expansion-lfm2.5-1.2b-instruct-v2:Q5_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 langleu/qmd-query-expansion-lfm2.5-1.2b-instruct-v2 with Docker Model Runner:
```
docker model run hf.co/langleu/qmd-query-expansion-lfm2.5-1.2b-instruct-v2:Q5_K_M
```

Lemonade

How to use langleu/qmd-query-expansion-lfm2.5-1.2b-instruct-v2 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull langleu/qmd-query-expansion-lfm2.5-1.2b-instruct-v2:Q5_K_M

Run and chat with the model

lemonade run user.qmd-query-expansion-lfm2.5-1.2b-instruct-v2-Q5_K_M

List all available models

lemonade list

langleu/qmd-query-expansion-lfm2.5-1.2b-instruct-v2

LiquidAI/LFM2.5-1.2B-Instruct fine-tuned with LoRA for QMD query expansion using the v2 data recipe. This repository contains the merged BF16 Transformers checkpoint at its root and QMD-ready GGUF quantizations alongside it.

Repository formats

Merged BF16 Transformers checkpoint: load the repository directly with AutoModelForCausalLM.from_pretrained("langleu/qmd-query-expansion-lfm2.5-1.2b-instruct-v2", torch_dtype=torch.bfloat16).
GGUF for QMD/llama.cpp: qmd-query-expansion-lfm2.5-1.2b-instruct-q5_k_m.gguf

The BF16 checkpoint is the unquantized merged fine-tune. The GGUF files are derived from that same merged checkpoint.

Artifact validated by this publishing invocation

File: qmd-query-expansion-lfm2.5-1.2b-instruct-q5_k_m.gguf
Kind: GGUF
Size: 843,354,528 bytes
SHA-256: 12cdc31cb1848cd1bd6b8dbf07c57339c5e2555550598e8469814837e5351c88
Base model: LiquidAI/LFM2.5-1.2B-Instruct
Base revision: 868df74dd56ff8a0c2ac5dbf281690c2dbebe4c9
Dataset: tobil/qmd-query-expansion-train-v2
Dataset revision: 9b9c3dd41a11209f3ce907908f9b865f0ead415e
Configured release quantizations: Q5_K_M
Evaluation report: q5_k_m

Prompt and behavior

/no_think Expand this search query: {query}

This variant supports the production v2 QMD behavior: an optional Query intent: line and /only:lex, /only:vec, and /only:hyde directives.

Each emitted line begins with lex:, vec:, or hyde:.

Use with QMD

The Transformers checkpoint is not loaded by QMD directly; select one of the GGUF files.

For a private repository, authenticate once with hf auth login, or set HF_TOKEN. QMD's node-llama-cpp downloader reads the cached Hugging Face token from ~/.cache/huggingface/token by default.

export QMD_GENERATE_MODEL="hf:langleu/qmd-query-expansion-lfm2.5-1.2b-instruct-v2/qmd-query-expansion-lfm2.5-1.2b-instruct-q5_k_m.gguf"
qmd query --json --explain --no-rerank "docker container shutdown timeout"

Training-data provenance

Training records were rebuilt from the pinned dataset's messages field with the target LFM2.5 tokenizer. The dataset's preformatted Qwen text was not used. Completion-only loss masked the user prompt.

Validation

Average QMD reward: 94.42%
Format compliance: 99.68%
Entity preservation: 96.61%
/only:* behavior: 100.00%
Hard failures: 0

BF16 and GGUF comparison

Format	Avg reward	Δ vs BF16	Format compliance	Entity	`/only:*`
BF16	94.47%	baseline	100.00%	96.61%	100.00%
Q5_K_M	94.42%	-0.05 pp	99.68%	96.61%	100.00%

Quality changes are reported in percentage points (pp), not relative percent. Small positive GGUF deltas can occur because sampled generation is not bit-for-bit deterministic.

BF16 (batched Transformers): approximately 406.9 output tokens/s and 4.04 examples/s.
Q5_K_M (GGUF evaluation): approximately 126.1 output tokens/s and 1.28 examples/s.

Speed percentages are intentionally not reported. BF16 was measured with batched Transformers inference, while QMD runs GGUF through llama.cpp one query at a time. Their observed throughput and latency are useful operational measurements, but dividing them would not be an apples-to-apples speedup.

Credits

Liquid AI for LFM2.5 and the LFM Open License v1.0.
Tobi for QMD, the QMD query-expansion datasets, evaluation/scoring design, and the Qwen3 query-expansion model.
OrcsRise for the earlier LFM2 QMD fine-tuning work that informed the LFM target-module recipe.
QMD, TRL, PEFT, and llama.cpp.

Licensing and dataset notice

This derivative is governed by the LFM Open License v1.0. The included LICENSE must be retained, including its attribution and commercial-use terms.

The upstream QMD dataset card did not declare an explicit dataset license at the pinned revision. This repository records that fact and does not imply that a license was granted. Users and redistributors are responsible for confirming that their use is authorized.

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Safetensors

Model size

1B params

Tensor type

BF16

Model tree for langleu/qmd-query-expansion-lfm2.5-1.2b-instruct-v2

Base model

LiquidAI/LFM2.5-1.2B-Base

Finetuned

LiquidAI/LFM2.5-1.2B-Instruct

Quantized

(72)

this model

langleu
/

qmd-query-expansion-lfm2.5-1.2b-instruct-v2

langleu/qmd-query-expansion-lfm2.5-1.2b-instruct-v2

Repository formats

Artifact validated by this publishing invocation

Prompt and behavior

Use with QMD

Training-data provenance

Validation

BF16 and GGUF comparison

Credits

Licensing and dataset notice

Model tree for langleu/qmd-query-expansion-lfm2.5-1.2b-instruct-v2

Dataset used to train langleu/qmd-query-expansion-lfm2.5-1.2b-instruct-v2