---
language:
- en
license: apache-2.0
base_model: Qwen/Qwen2.5-3B-Instruct
tags:
- information-retrieval
- boolean-search
- NL2BM25
- LoRA
- SFT
- tantivy
- BEIR
- searchlm
library_name: transformers
pipeline_tag: text-generation
---

# SearchLM NL2BM25 — SFT v2 Quality-Filtered (Qwen2.5-3B-Instruct)

**Part of the [SearchLM collection](https://huggingface.co/collections/Supreeth/searchlm) · [GitHub](https://github.com/SupreethRao99/searchLM)**

A quality-filtered LoRA SFT warm-start. v2 keeps only training examples where the
LLM-generated boolean query actually retrieved at least one relevant document
(`ndcg_at_10 > 0`), eliminating the ~65% of v1's data that taught syntactically
correct but semantically useless boolean structure.

This is the base model for [GRPO v2](Supreeth/searchlm-nl2bm25-grpo-v2), the best-performing SearchLM checkpoint.

> **Pipeline position:** `base → SFT v1 → GRPO v1 (⚠️) → `**`SFT v2`**` → GRPO v2 ✅`

---

## Why quality filtering matters

SFT v1 trained on 4,999 examples, ~36% of which had `ndcg_at_10 = 0`. These examples
taught the model to produce complex-looking queries that simply didn't retrieve anything.
SciFact was hit hardest: SFT v1 dropped *below base* (0.273 vs 0.386) because scientific
terminology requires precision — over-specified AND chains returned nothing.

**Before (SFT v1 — query returns zero results):**
```
<query>("ALDH1" OR "aldehyde dehydrogenase 1" OR "ALDH1A1")
AND ("breast cancer" OR "mammary carcinoma" OR "breast neoplasm")
AND (expression OR "gene expression" OR overexpression)
AND (outcome OR prognosis OR survival OR "disease-free survival")
AND (better OR improved OR favorable OR positive)</query>
```

**After (SFT v2 — learned from working examples only):**
```
<query>("ALDH1" OR "aldehyde dehydrogenase 1")
AND ("breast cancer" OR "breast neoplasm")
AND (expression OR overexpression)
AND (outcome OR prognosis OR survival)</query>
```

Fewer AND clauses → Tantivy returns documents → model receives training signal.

---

## All SearchLM checkpoints

| Model | NFCorpus NDCG@10 | SciFact NDCG@10 | Mean tokens | Boolean ops |
|-------|-----------------|----------------|-------------|-------------|
| base (Qwen2.5-3B-Instruct) | 0.455 | 0.386 | 120 | ~20% |
| [SFT v1](https://huggingface.co/Supreeth/searchlm-nl2bm25-sft) | 0.441 | 0.273 | 95 | ~80% |
| [GRPO v1](https://huggingface.co/Supreeth/searchlm-nl2bm25-grpo) ⚠️ | 0.556 | 0.608 | **5–7** | **0%** |
| [SFT v2](https://huggingface.co/Supreeth/searchlm-nl2bm25-sft-v2) | 0.466 | 0.358 | 109 | ~65% |
| [**GRPO v2**](https://huggingface.co/Supreeth/searchlm-nl2bm25-grpo-v2) ✅ | **0.577** | **0.657** | 147 | ~35% |

Evaluated on BEIR test splits (NFCorpus: 323 queries, SciFact: 300 queries).

---

## SFT v1 vs SFT v2

| | [SFT v1](Supreeth/searchlm-nl2bm25-sft) | **SFT v2** |
|-|--------|--------|
| Training examples | 4,999 | **1,751** (35% of v1) |
| Quality filter | all syntax-valid | `ndcg_at_10 > 0` |
| NFCorpus NDCG@10 | 0.441 | **0.466** (+0.025) |
| SciFact NDCG@10 | 0.273 | **0.358** (+0.085) |
| Training time (A10G) | ~30 min | **~22 min** |
| Final loss | ~0.23 | ~0.24 |

SciFact gained the most (+0.085) because it's where over-specification hurts most — precise
scientific documents retrieved by narrow terminology demand tighter query formulation.

---

## Training Details

| Setting | Value |
|---------|-------|
| Base model | `Qwen/Qwen2.5-3B-Instruct` |
| Method | LoRA SFT (r=16, α=32), adapter merged into base |
| Target modules | q/k/v/o projections + gate/up/down projections |
| Training data | [Supreeth/nl2bm25-sft](https://huggingface.co/datasets/Supreeth/nl2bm25-sft) filtered: `ndcg_at_10 > 0` |
| Retained / total | 1,751 / 4,999 (35%) |
| Epochs | 1 |
| Learning rate | 2e-4 (cosine decay, 5% warmup) |
| Effective batch size | 16 (2 × 8 grad accum) |
| Max sequence length | 1,024 tokens |
| Hardware | NVIDIA A10G 24 GB |
| Training time | ~22 min |
| Final loss | ~0.24 |
| Token accuracy | ~93.8% |
| W&B run | `supreethrao/searchlm/runs/k00s9ype` |

---

## Usage

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "Supreeth/searchlm-nl2bm25-sft-v2",
    torch_dtype="auto",
    device_map="auto",
)
tokenizer = AutoTokenizer.from_pretrained("Supreeth/searchlm-nl2bm25-sft-v2")

SYSTEM_PROMPT = """You are an expert information retrieval specialist. Convert the \
natural language query into a Tantivy boolean search query.

Output format (strictly follow this):
<reasoning>
Step-by-step concept extraction and synonym expansion.
</reasoning>
<query>your boolean query here</query>"""

nl_query = "effects of climate change on coral reef ecosystems"
messages = [
    {"role": "system", "content": SYSTEM_PROMPT},
    {"role": "user", "content": f"Convert to a Tantivy boolean search query:\n\n{nl_query}"},
]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=512, temperature=0.7, do_sample=True)
print(tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))
```

---

## Tantivy Boolean Syntax

[Tantivy](https://github.com/quickwit-oss/tantivy) is a full-text search engine library.
The model targets its query language:

| Construct | Syntax | Example |
|-----------|--------|---------|
| Single term | `word` | `cancer` |
| Exact phrase | `"phrase"` | `"bone density"` |
| AND | `A AND B` | `vitamin AND calcium` |
| OR | `A OR B` | `cancer OR tumor OR malignancy` |
| NOT | `NOT A` | `NOT review` |
| Grouping | `(A OR B)` | `(cat OR feline) AND behavior` |
| Field scope | `field:term` | `title:"machine learning"` |
| Boost | `term^N` | `cancer^2 OR tumor` |

---

## Related resources

- **Dataset:** [Supreeth/nl2bm25-sft](https://huggingface.co/datasets/Supreeth/nl2bm25-sft)
- **Next step:** [GRPO v2](Supreeth/searchlm-nl2bm25-grpo-v2) — reinforcement learning from this checkpoint
- **Code:** [SupreethRao99/searchLM](https://github.com/SupreethRao99/searchLM)
- **Analysis:** [Reward hacking report](https://github.com/SupreethRao99/searchLM/blob/main/REWARD_HACKING_REPORT_V2.md)
- **Collection:** [SearchLM collection](https://huggingface.co/collections/Supreeth/searchlm)

## Citation

```bibtex
@misc{searchlm2026,
  title  = {SearchLM: Training Small Language Models for Boolean Query Generation via RLVR},
  author = {Rao, Supreeth},
  year   = {2026},
  url    = {https://github.com/SupreethRao99/searchLM},
}
```