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Model: Supreeth/searchlm-nl2bm25-sft Source: Original Platform
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184
README.md
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---
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language:
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- en
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license: apache-2.0
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base_model: Qwen/Qwen2.5-3B-Instruct
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tags:
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- information-retrieval
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- boolean-search
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- NL2BM25
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- LoRA
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- SFT
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- tantivy
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- BEIR
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- searchlm
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library_name: transformers
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pipeline_tag: text-generation
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---
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# SearchLM NL2BM25 — SFT v1 (Qwen2.5-3B-Instruct)
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**Part of the [SearchLM collection](https://huggingface.co/collections/Supreeth/searchlm) · [GitHub](https://github.com/SupreethRao99/searchLM)**
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A Qwen2.5-3B-Instruct model fine-tuned via LoRA SFT to convert natural language queries into
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[Tantivy](https://github.com/quickwit-oss/tantivy) boolean search queries with explicit
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chain-of-thought reasoning. This is the **warm-start checkpoint** before GRPO reinforcement learning.
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> **Pipeline position:** `base → `**`SFT v1`**` → GRPO v1 (⚠️ reward hacking) → SFT v2 → GRPO v2 ✅`
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>
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> For the best retrieval model, use [GRPO v2](Supreeth/searchlm-nl2bm25-grpo-v2).
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---
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## What it does
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The model outputs a structured two-part response for any natural language information need:
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**Input:**
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```
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Do statins cause breast cancer?
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```
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**Output:**
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```
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<reasoning>
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Key concepts:
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1. Statin drugs — synonyms: statin, HMG-CoA reductase inhibitor, simvastatin, atorvastatin,
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lovastatin, pravastatin, rosuvastatin
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2. Causal relationship — cause, risk, association, induce, increase risk
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3. Breast cancer — "breast cancer", "breast carcinoma", "breast neoplasm", "mammary carcinoma"
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Strategy: AND the three concept groups; OR synonyms within each group.
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Phrase-quote multi-word terms to prevent term splitting.
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</reasoning>
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<query>(statin OR "HMG-CoA reductase inhibitor" OR simvastatin OR atorvastatin OR lovastatin)
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AND (cause OR risk OR association OR "induce" OR "increase risk")
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AND ("breast cancer" OR "breast carcinoma" OR "breast neoplasm")</query>
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```
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The `<query>` block is valid [Tantivy boolean syntax](https://docs.rs/tantivy/latest/tantivy/query/struct.QueryParser.html)
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ready to pass directly to a search engine.
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---
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## All SearchLM checkpoints
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| Model | NFCorpus NDCG@10 | SciFact NDCG@10 | Mean tokens | Boolean ops |
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|-------|-----------------|----------------|-------------|-------------|
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| base (Qwen2.5-3B-Instruct) | 0.455 | 0.386 | 120 | ~20% |
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| [SFT v1](https://huggingface.co/Supreeth/searchlm-nl2bm25-sft) | 0.441 | 0.273 | 95 | ~80% |
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| [GRPO v1](https://huggingface.co/Supreeth/searchlm-nl2bm25-grpo) ⚠️ | 0.556 | 0.608 | **5–7** | **0%** |
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| [SFT v2](https://huggingface.co/Supreeth/searchlm-nl2bm25-sft-v2) | 0.466 | 0.358 | 109 | ~65% |
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| [**GRPO v2**](https://huggingface.co/Supreeth/searchlm-nl2bm25-grpo-v2) ✅ | **0.577** | **0.657** | 147 | ~35% |
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Evaluated on BEIR test splits (NFCorpus: 323 queries, SciFact: 300 queries).
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SFT v1 scores slightly below base on NFCorpus and well below on SciFact. The ~36% of training
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examples with `ndcg_at_10 = 0` taught syntactically correct but semantically wrong boolean
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structure — queries that parsed fine but retrieved nothing. [SFT v2](Supreeth/searchlm-nl2bm25-sft-v2) fixes this
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with a quality filter.
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---
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## Training Details
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| Setting | Value |
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|---------|-------|
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| Base model | `Qwen/Qwen2.5-3B-Instruct` |
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| Method | LoRA SFT (r=16, α=32), adapter merged into base |
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| Target modules | q/k/v/o projections + gate/up/down projections |
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| Training data | [Supreeth/nl2bm25-sft](https://huggingface.co/datasets/Supreeth/nl2bm25-sft) — 4,999 examples |
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| Source BEIR datasets | NFCorpus, SciFact, FiQA-2018, ArguAna, HotpotQA, NQ |
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| Data generation | GPT-4o / Llama-3.3-70B / Qwen2.5-72B cycling via NVIDIA NIM |
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| Epochs | 1 |
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| Learning rate | 2e-4 (cosine decay, 5% warmup) |
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| Effective batch size | 16 (2 × 8 grad accum) |
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| Max sequence length | 1,024 tokens |
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| Hardware | NVIDIA A10G 24 GB |
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| Training time | ~30 min |
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| Final loss | ~0.23 |
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| Token accuracy | ~94% |
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| W&B run | `supreethrao/searchlm` |
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### Training data distribution
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| Source dataset | Queries | Doc count |
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|---------------|---------|-----------|
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| NFCorpus | ~700 | 3,633 |
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| SciFact | ~500 | 5,183 |
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| FiQA-2018 | ~1,600 | 57,638 |
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| ArguAna | ~800 | 8,674 |
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| HotpotQA | ~800 | 5,233,329 |
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| NQ | ~599 | 2,681,468 |
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---
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## Usage
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model = AutoModelForCausalLM.from_pretrained(
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"Supreeth/searchlm-nl2bm25-sft",
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torch_dtype="auto",
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device_map="auto",
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)
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tokenizer = AutoTokenizer.from_pretrained("Supreeth/searchlm-nl2bm25-sft")
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SYSTEM_PROMPT = """You are an expert information retrieval specialist. Convert the \
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natural language query into a Tantivy boolean search query.
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Output format (strictly follow this):
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<reasoning>
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Step-by-step concept extraction and synonym expansion.
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</reasoning>
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<query>your boolean query here</query>"""
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nl_query = "effects of climate change on coral reef ecosystems"
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messages = [
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{"role": "system", "content": SYSTEM_PROMPT},
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{"role": "user", "content": f"Convert to a Tantivy boolean search query:\n\n{nl_query}"},
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]
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text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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inputs = tokenizer(text, return_tensors="pt").to(model.device)
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outputs = model.generate(**inputs, max_new_tokens=512, temperature=0.7, do_sample=True)
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print(tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))
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```
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---
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## Tantivy Boolean Syntax
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[Tantivy](https://github.com/quickwit-oss/tantivy) is a full-text search engine library.
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The model targets its query language:
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| Construct | Syntax | Example |
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|-----------|--------|---------|
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| Single term | `word` | `cancer` |
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| Exact phrase | `"phrase"` | `"bone density"` |
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| AND | `A AND B` | `vitamin AND calcium` |
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| OR | `A OR B` | `cancer OR tumor OR malignancy` |
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| NOT | `NOT A` | `NOT review` |
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| Grouping | `(A OR B)` | `(cat OR feline) AND behavior` |
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| Field scope | `field:term` | `title:"machine learning"` |
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| Boost | `term^N` | `cancer^2 OR tumor` |
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---
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## Related resources
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- **Dataset:** [Supreeth/nl2bm25-sft](https://huggingface.co/datasets/Supreeth/nl2bm25-sft)
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- **Code:** [SupreethRao99/searchLM](https://github.com/SupreethRao99/searchLM)
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- **Analysis:** [Reward hacking report](https://github.com/SupreethRao99/searchLM/blob/main/REWARD_HACKING_REPORT_V2.md)
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- **Collection:** [SearchLM collection](https://huggingface.co/collections/Supreeth/searchlm)
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## Citation
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```bibtex
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@misc{searchlm2026,
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title = {SearchLM: Training Small Language Models for Boolean Query Generation via RLVR},
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author = {Rao, Supreeth},
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year = {2026},
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url = {https://github.com/SupreethRao99/searchLM},
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}
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```
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{%- if tools %}
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{{- '<|im_start|>system\n' }}
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{%- if messages[0]['role'] == 'system' %}
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{{- messages[0]['content'] }}
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{%- else %}
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||||
{{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
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{%- endif %}
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{{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
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{%- for tool in tools %}
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{{- "\n" }}
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{{- tool | tojson }}
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{%- endfor %}
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||||
{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
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{%- else %}
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{%- if messages[0]['role'] == 'system' %}
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{{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
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{%- else %}
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{{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
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||||
{%- endif %}
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||||
{%- endif %}
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||||
{%- for message in messages %}
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||||
{%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
|
||||
{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
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{%- elif message.role == "assistant" %}
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{{- '<|im_start|>' + message.role }}
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||||
{%- if message.content %}
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||||
{{- '\n' + message.content }}
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||||
{%- endif %}
|
||||
{%- for tool_call in message.tool_calls %}
|
||||
{%- if tool_call.function is defined %}
|
||||
{%- set tool_call = tool_call.function %}
|
||||
{%- endif %}
|
||||
{{- '\n<tool_call>\n{"name": "' }}
|
||||
{{- tool_call.name }}
|
||||
{{- '", "arguments": ' }}
|
||||
{{- tool_call.arguments | tojson }}
|
||||
{{- '}\n</tool_call>' }}
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||||
{%- endfor %}
|
||||
{{- '<|im_end|>\n' }}
|
||||
{%- elif message.role == "tool" %}
|
||||
{%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
|
||||
{{- '<|im_start|>user' }}
|
||||
{%- endif %}
|
||||
{{- '\n<tool_response>\n' }}
|
||||
{{- message.content }}
|
||||
{{- '\n</tool_response>' }}
|
||||
{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
|
||||
{{- '<|im_end|>\n' }}
|
||||
{%- endif %}
|
||||
{%- endif %}
|
||||
{%- endfor %}
|
||||
{%- if add_generation_prompt %}
|
||||
{{- '<|im_start|>assistant\n' }}
|
||||
{%- endif %}
|
||||
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config.json
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config.json
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{
|
||||
"architectures": [
|
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"Qwen2ForCausalLM"
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],
|
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"attention_dropout": 0.0,
|
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"dtype": "float32",
|
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"eos_token_id": 151645,
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"hidden_act": "silu",
|
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"hidden_size": 2048,
|
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"initializer_range": 0.02,
|
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"intermediate_size": 11008,
|
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"layer_types": [
|
||||
"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
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"full_attention",
|
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention"
|
||||
],
|
||||
"max_position_embeddings": 32768,
|
||||
"max_window_layers": 70,
|
||||
"model_type": "qwen2",
|
||||
"num_attention_heads": 16,
|
||||
"num_hidden_layers": 36,
|
||||
"num_key_value_heads": 2,
|
||||
"pad_token_id": 151643,
|
||||
"rms_norm_eps": 1e-06,
|
||||
"rope_scaling": null,
|
||||
"rope_theta": 1000000.0,
|
||||
"sliding_window": null,
|
||||
"tie_word_embeddings": true,
|
||||
"transformers_version": "4.57.6",
|
||||
"use_cache": true,
|
||||
"use_sliding_window": false,
|
||||
"vocab_size": 151936
|
||||
}
|
||||
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generation_config.json
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{
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"do_sample": true,
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"eos_token_id": [
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151645,
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151643
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],
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"pad_token_id": 151643,
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"repetition_penalty": 1.05,
|
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"temperature": 0.7,
|
||||
"top_k": 20,
|
||||
"top_p": 0.8,
|
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"transformers_version": "4.57.6"
|
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}
|
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merges.txt
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merges.txt
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model-00001-of-00003.safetensors
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model-00001-of-00003.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:0a695407c817d3840cc7564be1bb33a72685080a9f01523f2a4fb486a88af496
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size 4982131536
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model-00002-of-00003.safetensors
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version https://git-lfs.github.com/spec/v1
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||||
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|
||||
31
special_tokens_map.json
Normal file
31
special_tokens_map.json
Normal file
@@ -0,0 +1,31 @@
|
||||
{
|
||||
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|
||||
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||||
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||||
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|
||||
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|
||||
}
|
||||
3
tokenizer.json
Normal file
3
tokenizer.json
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:9c5ae00e602b8860cbd784ba82a8aa14e8feecec692e7076590d014d7b7fdafa
|
||||
size 11421896
|
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207
tokenizer_config.json
Normal file
207
tokenizer_config.json
Normal file
@@ -0,0 +1,207 @@
|
||||
{
|
||||
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|
||||
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||||
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||||
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|
||||
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||||
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|
||||
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|
||||
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||||
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||||
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|
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||||
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||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
1
vocab.json
Normal file
1
vocab.json
Normal file
File diff suppressed because one or more lines are too long
Reference in New Issue
Block a user