---
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 v1 (Qwen2.5-3B-Instruct)

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

A Qwen2.5-3B-Instruct model fine-tuned via LoRA SFT to convert natural language queries into
[Tantivy](https://github.com/quickwit-oss/tantivy) boolean search queries with explicit
chain-of-thought reasoning. This is the **warm-start checkpoint** before GRPO reinforcement learning.

> **Pipeline position:** `base → `**`SFT v1`**` → GRPO v1 (⚠️ reward hacking) → SFT v2 → GRPO v2 ✅`
>
> For the best retrieval model, use [GRPO v2](Supreeth/searchlm-nl2bm25-grpo-v2).

---

## What it does

The model outputs a structured two-part response for any natural language information need:

**Input:**
```
Do statins cause breast cancer?
```

**Output:**
```
<reasoning>
Key concepts:
1. Statin drugs — synonyms: statin, HMG-CoA reductase inhibitor, simvastatin, atorvastatin,
   lovastatin, pravastatin, rosuvastatin
2. Causal relationship — cause, risk, association, induce, increase risk
3. Breast cancer — "breast cancer", "breast carcinoma", "breast neoplasm", "mammary carcinoma"

Strategy: AND the three concept groups; OR synonyms within each group.
Phrase-quote multi-word terms to prevent term splitting.
</reasoning>
<query>(statin OR "HMG-CoA reductase inhibitor" OR simvastatin OR atorvastatin OR lovastatin)
AND (cause OR risk OR association OR "induce" OR "increase risk")
AND ("breast cancer" OR "breast carcinoma" OR "breast neoplasm")</query>
```

The `<query>` block is valid [Tantivy boolean syntax](https://docs.rs/tantivy/latest/tantivy/query/struct.QueryParser.html)
ready to pass directly to a search engine.

---

## 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 scores slightly below base on NFCorpus and well below on SciFact. The ~36% of training
examples with `ndcg_at_10 = 0` taught syntactically correct but semantically wrong boolean
structure — queries that parsed fine but retrieved nothing. [SFT v2](Supreeth/searchlm-nl2bm25-sft-v2) fixes this
with a quality filter.

---

## 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) — 4,999 examples |
| Source BEIR datasets | NFCorpus, SciFact, FiQA-2018, ArguAna, HotpotQA, NQ |
| Data generation | GPT-4o / Llama-3.3-70B / Qwen2.5-72B cycling via NVIDIA NIM |
| 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 | ~30 min |
| Final loss | ~0.23 |
| Token accuracy | ~94% |
| W&B run | `supreethrao/searchlm` |

### Training data distribution

| Source dataset | Queries | Doc count |
|---------------|---------|-----------|
| NFCorpus | ~700 | 3,633 |
| SciFact | ~500 | 5,183 |
| FiQA-2018 | ~1,600 | 57,638 |
| ArguAna | ~800 | 8,674 |
| HotpotQA | ~800 | 5,233,329 |
| NQ | ~599 | 2,681,468 |

---

## Usage

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

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

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)
- **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},
}
```