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Model: nickoo004/queryshield-1.5b Source: Original Platform
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README.md
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README.md
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---
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license: mit
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base_model: Qwen/Qwen2.5-1.5B-Instruct
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language:
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- en
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- uz
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- ru
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- kk
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- kaa
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tags:
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- queryshield
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- prompt-optimization
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- multilingual
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- instruction-tuning
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- lora
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- qlora
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- qwen2.5
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- uzbek
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- karakalpak
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- kazakh
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- central-asia
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- fine-tuned
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pipeline_tag: text-generation
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datasets:
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- nickoo004/queryshield-multilingual
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---
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# QueryShield — Multilingual Prompt Optimizer
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**QueryShield-1.5B** is a fine-tuned version of [Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct) trained to rewrite raw, messy user queries into detailed, structured instruction prompts for downstream LLMs — across 5 languages and 30 professional domains.
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> Given a raw user question → outputs an expert-level optimized prompt telling a downstream LLM *how* to answer it.
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---
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## What it does
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Most LLMs perform significantly better when given structured, detailed prompts rather than raw user input. QueryShield sits **between the user and the LLM** — it takes the raw query and rewrites it into a high-quality instruction prompt automatically.
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```
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User: "menga diabetni boshqarish uchun ovqat rejimi ayting"
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↓ QueryShield
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Optimized: "As a Medical Expert, the user is asking in Uzbek about dietary
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management for diabetes with high blood sugar. Provide a structured
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3-tier response covering: diabetes basics, dietary assessment, and
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an actionable meal plan. Respond entirely in Uzbek. Avoid jargon..."
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↓ Downstream LLM
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Final answer in Uzbek ✅
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```
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---
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## Model Details
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| Property | Value |
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|---|---|
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| **Base model** | Qwen/Qwen2.5-1.5B-Instruct |
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| **Training data** | [QueryShield Multilingual Dataset](https://huggingface.co/datasets/nickoo004/queryshield-multilingual) |
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| **Training rows** | 19,530 |
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| **Epochs** | 3 |
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| **Train loss** | 0.88 → 0.47 |
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| **Eval loss** | 0.967 (best checkpoint) |
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| **GPU** | NVIDIA RTX 3090 24GB |
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| **Training time** | ~3.7 hours |
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| **Parameters** | 1.5B total / 147M trainable (8.7%) |
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| **Live demo** | [▶ Kaggle Notebook](https://www.kaggle.com/code/nursultankoshekbaev/queryshield-1-5b) |
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---
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## Languages
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| Language | Code | Support |
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|---|---|---|
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| English | `en` | ✅ Full |
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| Uzbek | `uz` | ✅ Full |
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| Russian | `ru` | ✅ Full |
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| Kazakh | `kk` | ✅ Full |
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| Karakalpak | `kaa` | ✅ Good |
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**Cross-lingual** scenarios supported — user can write in one language and request output in another (e.g., Uzbek input → Russian output).
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---
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## Quick Start
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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import torch
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model_id = "nickoo004/queryshield-1.5b"
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tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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torch_dtype=torch.bfloat16,
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device_map="auto",
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trust_remote_code=True,
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)
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SYSTEM = (
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"You are QueryShield, a multilingual prompt optimizer. "
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"Given a raw user question, rewrite it into a detailed instruction "
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"prompt for a downstream LLM expert. "
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"User language: {in_lang}. Response language: {out_lang}. "
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"Expert role: {role}."
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)
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def optimize_prompt(user_question, input_language, output_language, role):
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messages = [
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{"role": "system", "content": SYSTEM.format(
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in_lang=input_language,
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out_lang=output_language,
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role=role,
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)},
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{"role": "user", "content": user_question},
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]
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text = tokenizer.apply_chat_template(
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messages, tokenize=False, add_generation_prompt=True
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)
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inputs = tokenizer(text, return_tensors="pt").to(model.device)
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with torch.no_grad():
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output = model.generate(
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**inputs,
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max_new_tokens=512,
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temperature=0.7,
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do_sample=True,
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repetition_penalty=1.1,
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pad_token_id=tokenizer.eos_token_id,
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)
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new_tokens = output[0][inputs["input_ids"].shape[1]:]
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return tokenizer.decode(new_tokens, skip_special_tokens=True)
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# Example 1 — Uzbek monolingual
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result = optimize_prompt(
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user_question="menga diabetni boshqarish uchun eng yaxshi ovqatlanish rejimini ayting",
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input_language="Uzbek",
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output_language="Uzbek",
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role="Medical Expert",
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)
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print(result)
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# Example 2 — Cross-lingual: Kazakh -> Uzbek
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result = optimize_prompt(
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user_question="менің фермамда топырақ сапасы нашар, не істеуім керек?",
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input_language="Kazakh",
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output_language="Uzbek",
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role="Agricultural Scientist",
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)
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print(result)
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```
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---
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## Live Demo
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**[▶ Run on Kaggle](https://www.kaggle.com/code/nursultankoshekbaev/queryshield-1-5b)** — no setup needed, free GPU included.
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Tests all 7 cases: English, Uzbek, Russian, Kazakh, Karakalpak + 2 cross-lingual pairs.
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---
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## Supported Domains (30 total)
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| Domain | Expert Role |
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|---|---|
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| Software Engineering | Senior Software Engineer |
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| Healthcare & Medicine | Medical Expert |
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| Finance & Banking | Financial Analyst |
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| Legal & Law | Legal Advisor |
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| Data Science & AI | Data Scientist |
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| Cybersecurity | Cybersecurity Specialist |
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| Aviation & Aerospace | Aerospace Engineer |
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| Agriculture | Agricultural Scientist |
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| Education & Teaching | Experienced Educator |
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| Automotive | Automotive Engineer |
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| Pharmaceuticals | Pharmaceutical Researcher |
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| Manufacturing | Manufacturing Expert |
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| Civil / Mechanical / Electrical Engineering | Domain Engineer |
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| Business & Marketing | Business Strategist |
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| Creative Writing | Professional Writer |
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| … and 15 more | … |
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---
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## Training Details
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### Dataset
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- **Source:** [nickoo004/queryshield-multilingual](https://huggingface.co/datasets/nickoo004/queryshield-multilingual)
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- **19,530 rows** across 5 languages and 30 domains
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- Generated by DeepSeek, Gemini, and Qwen2.5-14B
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### Loss Curve
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```
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Epoch 1.0 -> train: 1.023 | eval: 0.997
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Epoch 2.5 -> train: 0.731 | eval: 0.967 <- best checkpoint
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```
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---
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## Limitations
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- Karakalpak support is functional but may be less consistent than other languages due to limited training data for this low-resource language
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- `optimized_prompt` output is always structured as an English instruction — this is by design
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- Best results on domains covered in training data; novel domains may produce generic prompts
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- Not suitable for harmful, illegal, or unethical query optimization
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---
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## Citation
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```bibtex
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@model{queryshield_1_5b_2026,
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author = {nickoo004},
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title = {QueryShield-1.5B: Multilingual Prompt Optimizer},
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year = {2026},
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publisher = {Hugging Face},
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url = {https://huggingface.co/nickoo004/queryshield-1.5b}
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}
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```
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---
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## License
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This model is released under the **MIT License**.
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Base model license: [Qwen License](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct/blob/main/LICENSE)
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config.json
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config.json
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},
|
||||
"151652": {
|
||||
"content": "<|vision_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151653": {
|
||||
"content": "<|vision_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151654": {
|
||||
"content": "<|vision_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151655": {
|
||||
"content": "<|image_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151656": {
|
||||
"content": "<|video_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151657": {
|
||||
"content": "<tool_call>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151658": {
|
||||
"content": "</tool_call>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151659": {
|
||||
"content": "<|fim_prefix|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151660": {
|
||||
"content": "<|fim_middle|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151661": {
|
||||
"content": "<|fim_suffix|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151662": {
|
||||
"content": "<|fim_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151663": {
|
||||
"content": "<|repo_name|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151664": {
|
||||
"content": "<|file_sep|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
}
|
||||
},
|
||||
"additional_special_tokens": [
|
||||
"<|im_start|>",
|
||||
"<|im_end|>",
|
||||
"<|object_ref_start|>",
|
||||
"<|object_ref_end|>",
|
||||
"<|box_start|>",
|
||||
"<|box_end|>",
|
||||
"<|quad_start|>",
|
||||
"<|quad_end|>",
|
||||
"<|vision_start|>",
|
||||
"<|vision_end|>",
|
||||
"<|vision_pad|>",
|
||||
"<|image_pad|>",
|
||||
"<|video_pad|>"
|
||||
],
|
||||
"bos_token": null,
|
||||
"chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0]['role'] == 'system' %}\n {{- messages[0]['content'] }}\n {%- else %}\n {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}\n {%- endif %}\n {{- \"\\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>\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\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\" }}\n{%- else %}\n {%- if messages[0]['role'] == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0]['content'] + '<|im_end|>\\n' }}\n {%- else %}\n {{- '<|im_start|>system\\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and not message.tool_calls) %}\n {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {{- '<|im_start|>' + message.role }}\n {%- if message.content %}\n {{- '\\n' + message.content }}\n {%- endif %}\n {%- for tool_call in message.tool_calls %}\n {%- if tool_call.function is defined %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '\\n<tool_call>\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {{- tool_call.arguments | tojson }}\n {{- '}\\n</tool_call>' }}\n {%- endfor %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != \"tool\") %}\n {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n<tool_response>\\n' }}\n {{- message.content }}\n {{- '\\n</tool_response>' }}\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n' }}\n{%- endif %}\n",
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "<|im_end|>",
|
||||
"errors": "replace",
|
||||
"model_max_length": 131072,
|
||||
"pad_token": "<|im_end|>",
|
||||
"padding_side": "right",
|
||||
"split_special_tokens": false,
|
||||
"tokenizer_class": "Qwen2Tokenizer",
|
||||
"unk_token": null
|
||||
}
|
||||
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