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Model: Arthur-75/storm-qwen3-8B Source: Original Platform
2026-08-10 08:15:18 +08:00
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+---
+base_model: Qwen/Qwen3-8B
+pipeline_tag: text-generation
+tags:
+- qwen
+- qwen3
+- lora
+- storm
+- information-retrieval
+- keyword-generation
+- query-expansion
+---
+
+# STORM Qwen3 Keyword Generator
+
+Preprint for EMNLP 2026
+
+## Abstract
+Modern retrieval increasingly relies on dense and learned-sparse neural models that are effective but require encoding the 
+entire corpus into a specialized index, which must be rebuilt whenever the model changes. Lexical retrievers like BM25 stay 
+efficient, transparent, and run on a standard inverted index that need not change as models evolve, but suffer from vocabulary
+mismatch. LLM query rewriting can help, yet prompted rewriters emit well-formed but retrieval-ineffective---or harmful---terms,
+and training against a retrieval reward gives only delayed, sequence-level supervision that obscures which terms helped. 
+We introduce STORM (Stepwise Token Optimization with Reward-guided beaM search), a self-supervised framework for lexical 
+query expansion. STORM trains the rewriter through generation guided by retrieval metrics: at each step, a beam of candidate
+expansions is scored against the BM25 index and low-reward continuations are pruned, turning the retrieval reward into a 
+token-level signal that concentrates exploration on retrieval-effective vocabulary. Across TREC DL and BEIR, STORM lets 
+0.6B--8B backbones match or surpass competitive LLM rewriters and far larger proprietary models while retrieving as fast 
+as plain BM25, and transfers zero-shot to 18 languages (MIRACL), beating dedicated multilingual dense retrievers. 
+STORM is thus a competitive, infrastructure-light alternative to dense neural retrieval.
+
+## Install
+
+```bash
+pip install -U torch transformers accelerate safetensors
+```
+
+## Load and run
+Note: Group beam search is optional; standard beam search (num_beams=5, num_return_sequences=1 or 3) or 
+decoding without beam search also works, although our slightly best-performing configuration used group beam search with num_beams=6 and num_return_sequences=3.
+
+
+```python
+import torch
+from transformers import AutoModelForCausalLM
+from transformers import AutoTokenizer
+
+model_id = "Arthur-75/storm-qwen3-8B"
+
+system_prompt = (
+    "From the query generate new semantic related keywords.\n"
+    "Output the result strictly as a single comma-separated line."
+)
+
+if torch.cuda.is_available():
+    device = "cuda"
+elif torch.backends.mps.is_available():
+    device = "mps"
+else:
+    device = "cpu"
+
+tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
+#tokenizer.padding_side = "left"
+
+if tokenizer.pad_token is None:
+    tokenizer.pad_token = tokenizer.eos_token
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    dtype=torch.float32,
+    #device_map=None,
+    #low_cpu_mem_usage=True,
+    trust_remote_code=True,
+)
+
+model = model.to(device)
+model.eval()
+
+
+
+def generate_keywords(query: str):
+    messages = [
+        {"role": "system", "content": system_prompt},
+        {"role": "user", "content": f"[QUERY]: {query.strip()}\n[KEYWORDS]: "},
+    ]
+
+    prompt = tokenizer.apply_chat_template(
+        messages,
+        tokenize=False,
+        add_generation_prompt=True,
+        enable_thinking=False,
+    )
+
+    inputs = tokenizer(
+        prompt,
+        return_tensors="pt",
+        add_special_tokens=False,
+    ).to(device)
+
+    prompt_len = inputs["input_ids"].shape[1]
+
+    with torch.inference_mode():
+        output = model.generate(
+            **inputs,
+            max_new_tokens=64,#[32,64]
+            do_sample=False,
+            num_beams=6,
+            num_beam_groups=3,
+            diversity_penalty=1.0,
+            num_return_sequences=3,
+            #repetition_penalty=1.0,
+            custom_generate="transformers-community/group-beam-search"
+           # pad_token_id=tokenizer.pad_token_id,
+           # eos_token_id=tokenizer.eos_token_id,
+        )
+
+    decoded = tokenizer.batch_decode(
+        output[:, prompt_len:],
+        skip_special_tokens=True,
+    )
+
+    return decoded
+
+
+query = "What are the symptoms of vitamin D deficiency?"
+outputs = generate_keywords(query)
+
+print(outputs)
+```
+