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

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: Q5KM
• —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

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.