feat: add LiquidAI LFM2 support for query expansion

Add training configuration and documentation for using LiquidAI's LFM2-1.2B
as an alternative base model for query expansion fine-tuning.

LFM2 benefits:
- 2x faster decode/prefill vs standard transformers
- Optimized for edge/on-device inference
- Good at agentic tasks, RAG, and data extraction

Changes:
- Add configs/sft_lfm2.yaml with LFM2-specific LoRA target modules
- Add jobs/sft_lfm2.py for HuggingFace Jobs training
- Update llm.ts with LFM2 GGUF model URIs
- Add documentation for LFM2 training workflow

LFM2 uses a hybrid architecture (convolutions + attention) requiring
different LoRA targets: q_proj, k_proj, v_proj, out_proj, in_proj, w1, w2, w3

finetune/README.md

@@ -297,3 +297,42 @@ deterministic, and suitable as an RL signal. See `SCORING.md` for the full rubri
 |-------|--------------|-----------------|
 | SFT | 92.0% | 30/30 |
 | GRPO | 91.7% | 30/30 |
+
+## Alternative Base Models
+
+### LiquidAI LFM2 (Experimental)
+
+[LFM2](https://www.liquid.ai/blog/liquid-foundation-models-v2-our-second-series-of-generative-ai-models) 
+is a hybrid architecture from Liquid AI optimized for on-device inference. It uses
+a novel combination of convolutions and attention that achieves 2x faster decode
+and prefill speed compared to standard transformers.
+
+**Why LFM2 for query expansion:**
+- **Faster inference**: Lower latency for real-time search applications
+- **Memory efficient**: Smaller memory footprint than equivalent transformers
+- **Edge-optimized**: Can run on mobile devices and embedded systems
+- **Good at agentic tasks**: LiquidAI recommends LFM2 for RAG and data extraction
+
+**Training with LFM2:**
+
+```bash
+# SFT with LFM2-1.2B base model
+uv run train.py sft --config configs/sft_lfm2.yaml
+
+# Evaluate the trained model
+uv run eval.py --model outputs/sft-lfm2
+
+# Convert to GGUF for deployment
+uv run convert_gguf.py --base LiquidAI/LFM2-1.2B \
+                       --sft outputs/sft-lfm2 \
+                       --output tobil/qmd-query-expansion-lfm2-gguf
+```
+
+**Key differences from Qwen3:**
+- Different LoRA target modules: `q_proj, k_proj, v_proj, out_proj, in_proj, w1, w2, w3`
+- Recommended generation parameters: `temp=0.3, min_p=0.15, repetition_penalty=1.05`
+- Requires transformers >= 4.55.0 for architecture support
+
+**Pre-trained GGUF models:**
+- Base: `hf:LiquidAI/LFM2-1.2B-GGUF/LFM2-1.2B-Q4_K_M.gguf` (~731 MB)
+- Instruct: `hf:LiquidAI/LFM2.5-1.2B-Instruct-GGUF/LFM2.5-1.2B-Instruct-Q4_K_M.gguf` (~731 MB)

finetune/configs/sft_lfm2.yaml

@@ -0,0 +1,60 @@
+# SFT Training Config for QMD Query Expansion with LiquidAI LFM2
+# Target: LFM2-1.2B with LoRA (hybrid architecture: convolutions + attention)
+#
+# LFM2 is optimized for on-device inference with fast decode/prefill.
+# Recommended for: agentic tasks, data extraction, RAG, creative writing.
+#
+# Usage: uv run train.py sft --config configs/sft_lfm2.yaml
+#
+# Requirements:
+#   - transformers >= 4.55.0 (LFM2 architecture support)
+#   - May need: pip install -U transformers
+
+model:
+  base: "LiquidAI/LFM2-1.2B"
+  output: "outputs/sft-lfm2"  # Local training output (push to HF manually after eval)
+
+dataset:
+  # Local: run `uv run dataset/prepare_data.py` first, then use "data/train/"
+  # HuggingFace: use "tobil/qmd-query-expansion-train" (already prepared)
+  name: "data/train/"
+  text_field: "text"
+  split: "train"
+  eval_split: 0.1
+
+training:
+  epochs: 5
+  batch_size: 4
+  gradient_accumulation_steps: 4
+  learning_rate: 2e-4
+  max_length: 512
+  warmup_ratio: 0.03
+  lr_scheduler: "cosine"
+
+lora:
+  rank: 16
+  alpha: 32
+  dropout: 0.0
+  # LFM2 uses different architecture than standard transformers:
+  # - Attention layers: q_proj, k_proj, v_proj, out_proj
+  # - Input projection: in_proj  
+  # - FFN/MLP gates: w1, w2, w3 (SwiGLU activation)
+  target_modules:
+    - "q_proj"
+    - "k_proj"
+    - "v_proj"
+    - "out_proj"
+    - "in_proj"
+    - "w1"
+    - "w2"
+    - "w3"
+
+tracking:
+  project: "qmd-query-expansion"
+  run_name: "sft-lfm2-1.2B"
+
+# LFM2-specific generation settings (recommended by LiquidAI)
+generation:
+  temperature: 0.3
+  min_p: 0.15
+  repetition_penalty: 1.05

finetune/jobs/sft_lfm2.py

@@ -0,0 +1,106 @@
+# /// script
+# requires-python = ">=3.10"
+# dependencies = [
+#     "trl>=0.12.0",
+#     "peft>=0.7.0",
+#     "transformers>=4.55.0",
+#     "accelerate>=0.24.0",
+#     "huggingface_hub>=0.20.0",
+#     "datasets",
+#     "bitsandbytes",
+#     "torch",
+# ]
+# ///
+"""
+SFT training for QMD query expansion with LiquidAI LFM2-1.2B.
+
+LFM2 is a hybrid architecture optimized for edge/on-device inference.
+Uses different LoRA target modules than standard transformers.
+
+Self-contained script for HuggingFace Jobs:
+    hf jobs uv run --flavor a10g-large --secrets HF_TOKEN --timeout 2h jobs/sft_lfm2.py
+"""
+
+import os
+from huggingface_hub import login
+
+# --- Config (inlined from configs/sft_lfm2.yaml) ---
+BASE_MODEL = "LiquidAI/LFM2-1.2B"
+OUTPUT_MODEL = "tobil/qmd-query-expansion-lfm2-sft"
+DATASET = "tobil/qmd-query-expansion-train"
+
+hf_token = os.environ.get("HF_TOKEN")
+if hf_token:
+    login(token=hf_token)
+
+from datasets import load_dataset
+from peft import LoraConfig
+from transformers import AutoTokenizer
+from trl import SFTTrainer, SFTConfig
+
+# Load and split dataset
+print(f"Loading dataset: {DATASET}...")
+dataset = load_dataset(DATASET, split="train")
+print(f"Dataset loaded: {len(dataset)} examples")
+
+split = dataset.train_test_split(test_size=0.1, seed=42)
+train_dataset = split["train"]
+eval_dataset = split["test"]
+print(f"  Train: {len(train_dataset)}, Eval: {len(eval_dataset)}")
+
+# SFT config
+config = SFTConfig(
+    output_dir="qmd-query-expansion-lfm2-sft",
+    push_to_hub=True,
+    hub_model_id=OUTPUT_MODEL,
+    hub_strategy="every_save",
+
+    num_train_epochs=5,
+    per_device_train_batch_size=4,
+    gradient_accumulation_steps=4,
+    learning_rate=2e-4,
+    max_length=512,
+
+    logging_steps=10,
+    save_strategy="steps",
+    save_steps=200,
+    save_total_limit=2,
+    eval_strategy="steps",
+    eval_steps=200,
+
+    warmup_ratio=0.03,
+    lr_scheduler_type="cosine",
+    bf16=True,
+
+    report_to="none",
+)
+
+# LoRA config for LFM2 architecture
+# LFM2 uses different layer names than standard transformers:
+# - Attention: q_proj, k_proj, v_proj, out_proj
+# - Input projection: in_proj
+# - FFN/MLP gates (SwiGLU): w1, w2, w3
+peft_config = LoraConfig(
+    r=16,
+    lora_alpha=32,
+    lora_dropout=0.0,
+    bias="none",
+    task_type="CAUSAL_LM",
+    target_modules=["q_proj", "k_proj", "v_proj", "out_proj", "in_proj", "w1", "w2", "w3"],
+)
+
+print("Initializing SFT trainer...")
+trainer = SFTTrainer(
+    model=BASE_MODEL,
+    train_dataset=train_dataset,
+    eval_dataset=eval_dataset,
+    args=config,
+    peft_config=peft_config,
+)
+
+print("Starting SFT training (LFM2-1.2B)...")
+trainer.train()
+
+print("Pushing to Hub...")
+trainer.push_to_hub()
+print(f"Done! Model: https://huggingface.co/{OUTPUT_MODEL}")

src/llm.ts

@@ -178,6 +178,12 @@ const DEFAULT_RERANK_MODEL = "hf:ggml-org/Qwen3-Reranker-0.6B-Q8_0-GGUF/qwen3-re
 // const DEFAULT_GENERATE_MODEL = "hf:ggml-org/Qwen3-0.6B-GGUF/Qwen3-0.6B-Q8_0.gguf";
 const DEFAULT_GENERATE_MODEL = "hf:tobil/qmd-query-expansion-1.7B-gguf/qmd-query-expansion-1.7B-q4_k_m.gguf";
 
+// Alternative generation models for query expansion:
+// LiquidAI LFM2 - hybrid architecture optimized for edge/on-device inference
+// Use these as base for fine-tuning with configs/sft_lfm2.yaml
+export const LFM2_GENERATE_MODEL = "hf:LiquidAI/LFM2-1.2B-GGUF/LFM2-1.2B-Q4_K_M.gguf";
+export const LFM2_INSTRUCT_MODEL = "hf:LiquidAI/LFM2.5-1.2B-Instruct-GGUF/LFM2.5-1.2B-Instruct-Q4_K_M.gguf";
+
 export const DEFAULT_EMBED_MODEL_URI = DEFAULT_EMBED_MODEL;
 export const DEFAULT_RERANK_MODEL_URI = DEFAULT_RERANK_MODEL;
 export const DEFAULT_GENERATE_MODEL_URI = DEFAULT_GENERATE_MODEL;