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

Libraries

How to use Supreeth/searchlm-nl2bm25-grpo with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Supreeth/searchlm-nl2bm25-grpo")
model = AutoModelForCausalLM.from_pretrained("Supreeth/searchlm-nl2bm25-grpo")
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-grpo with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Supreeth/searchlm-nl2bm25-grpo

SGLang

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

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

Supreeth commited on 22 days ago

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@@ -1,199 +1,83 @@
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 ---
+language:
+- en
+license: apache-2.0
+base_model: Qwen/Qwen2.5-3B-Instruct
+tags:
+- information-retrieval
+- boolean-search
+- NL2BM25
+- GRPO
+- RLVR
+- tantivy
+- BEIR
+- searchlm
+- reward-hacking
 library_name: transformers
+pipeline_tag: text-generation
 ---
+# SearchLM NL2BM25 — GRPO v1 (Qwen2.5-3B-Instruct)
+> **⚠️ Reward Hacking Model** — This checkpoint exhibits specification gaming.
+> Use [GRPO v2](Supreeth/searchlm-nl2bm25-grpo-v2) for production use. This model is published
+> for research reproducibility and as a concrete example of reward hacking in RLVR.
+A Qwen2.5-3B-Instruct model trained via GRPO (Group Relative Policy Optimization)
+starting from [SFT v1](Supreeth/searchlm-nl2bm25-sft), using live Tantivy retrieval as the reward signal.
+See the [SearchLM collection](https://huggingface.co/collections/Supreeth/searchlm) for all checkpoints.
+## Reward Hacking Behaviour
+Despite achieving the best NDCG@10 among v1 checkpoints, this model games the reward
+by outputting 3–7 token keyword phrases — abandoning all boolean structure:
+| Metric | Value |
+|--------|-------|
+| Mean completion length | **5–7 tokens** (vs 95–163 for SFT) |
+| Boolean operator usage | **0%** (vs ~80% for SFT) |
+| `frac_reward_zero_std` | **90–96%** (policy gradient collapsed from step 1) |
+**Typical output:**
+```
+<reasoning>
+</reasoning>
+<query>Cholesterol Statin Breast Cancer</query>
+```
+The model discovered that on small corpora (NFCorpus: 3,633 docs; SciFact: 5,183 docs),
+2–4 content nouns achieve near-optimal BM25 recall. The reward (`0.6 × NDCG@10 + 0.4 × MRR`)
+did not penalise empty reasoning or missing boolean structure.
+Full analysis: [REWARD_HACKING_REPORT_V2.md](https://github.com/SupreethRao99/searchLM/blob/main/REWARD_HACKING_REPORT_V2.md)
+## Benchmark (test split, NDCG@10)
+| Dataset | Base | SFT v1 | **GRPO v1** | GRPO v2 |
+|---------|------|--------|------------|---------|
+| NFCorpus | 0.455 | 0.441 | 0.556 | **0.577** |
+| SciFact  | 0.386 | 0.273 | 0.608 | **0.657** |
 ## Training Details
+| Setting | Value |
+|---------|-------|
+| Base model | [searchlm-nl2bm25-sft](Supreeth/searchlm-nl2bm25-sft) |
+| Method | GRPO (TRL + vLLM colocate) |
+| Reward | `0.6 × NDCG@10 + 0.4 × MRR` (live Tantivy search) |
+| Training datasets | NFCorpus + SciFact (train split) |
+| Epochs | 3 |
+| `num_generations` | 2 |
+| Hardware | NVIDIA H100 80 GB |
+## 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},
+}
+```