Instructions to use Supreeth/searchlm-nl2bm25-sft-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Supreeth/searchlm-nl2bm25-sft-v2 with Transformers:

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

pipe = pipeline("text-generation", model="Supreeth/searchlm-nl2bm25-sft-v2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

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

vLLM

How to use Supreeth/searchlm-nl2bm25-sft-v2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Supreeth/searchlm-nl2bm25-sft-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": "Supreeth/searchlm-nl2bm25-sft-v2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Supreeth/searchlm-nl2bm25-sft-v2

SGLang

How to use Supreeth/searchlm-nl2bm25-sft-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 "Supreeth/searchlm-nl2bm25-sft-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": "Supreeth/searchlm-nl2bm25-sft-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 "Supreeth/searchlm-nl2bm25-sft-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": "Supreeth/searchlm-nl2bm25-sft-v2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Supreeth/searchlm-nl2bm25-sft-v2 with Docker Model Runner:
```
docker model run hf.co/Supreeth/searchlm-nl2bm25-sft-v2
```

searchlm-nl2bm25-sft-v2 / README.md

Supreeth

Enrich model card with examples and training details

391b5c7 verified 22 days ago

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	---
	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},
	}
	```