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Model: vector-institute/Qwen3-8B-UnBias-Plus-SFT-Instruct Source: Original Platform
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README.md
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---
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license: apache-2.0
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base_model: Qwen/Qwen3-8B
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tags:
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- qwen3
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- bias-detection
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- news-debiasing
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- text-generation
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- fine-tuned
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- unsloth
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- sft
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datasets:
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- vector-institute/Unbias-plus
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language:
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- en
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---
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# Qwen3-8B-UnBias-Plus-SFT-Instruct
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A fine-tuned version of [Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B) for news media bias detection and neutral rewriting, developed by the [Vector Institute](https://vectorinstitute.ai) as part of the [UnBias-Plus](https://github.com/VectorInstitute/unbias-plus) project.
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Given a news article, the model identifies biased language segments, classifies their bias type and severity, provides neutral replacements, and returns a fully rewritten unbiased version of the article — all in a single structured JSON response.
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This is the **Instruct variant** — trained without chain-of-thought thinking blocks (`enable_thinking=False`). It produces clean structured JSON directly, making it faster and more reliable for production inference, including deployment via vLLM or other OpenAI-compatible serving backends.
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## Difference from [Qwen3-8B-UnBias-Plus-SFT](https://huggingface.co/vector-institute/Qwen3-8B-UnBias-Plus-SFT)
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| | SFT (thinking) | SFT-Instruct (this model) |
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|---|---|---|
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| Thinking mode | `enable_thinking=True` | `enable_thinking=False` |
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| Output | `<think>...</think>` + JSON | JSON directly |
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| Inference backend | Transformers | Transformers / vLLM |
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| Latency | Higher | Lower |
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| Recommended for | Research, local use | Production APIs, vLLM deployment |
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## Model Details
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| Property | Value |
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|---|---|
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| Base model | Qwen/Qwen3-8B |
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| Fine-tuning method | Supervised Fine-Tuning (SFT) with LoRA |
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| Training precision | bf16 (full precision, no quantization during training) |
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| LoRA rank | 16 |
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| Training framework | Unsloth + TRL |
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| Context length | 8192 tokens |
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| Thinking mode | Disabled (`enable_thinking=False`) |
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| Output format | Structured JSON |
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## Usage
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch, json
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model_id = "vector-institute/Qwen3-8B-UnBias-Plus-SFT-Instruct"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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dtype=torch.bfloat16,
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device_map="auto",
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)
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model.eval()
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SYSTEM_PROMPT = """You are an expert linguist and bias detection specialist.
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Your task is to carefully read a news article, detect ALL biased language,
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and return a structured JSON response. Return ONLY valid JSON, no extra text."""
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article = "Your news article here..."
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messages = [
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{"role": "system", "content": SYSTEM_PROMPT},
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{"role": "user", "content": f"Analyze the following article for bias and return the result in the required JSON format.\n\nARTICLE:\n{article}"},
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]
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inputs = tokenizer.apply_chat_template(
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messages,
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tokenize=True,
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add_generation_prompt=True,
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enable_thinking=False, # must be False for this variant
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return_tensors="pt",
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return_dict=True,
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truncation=True,
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max_length=8192,
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)
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with torch.no_grad():
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outputs = model.generate(
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input_ids=inputs["input_ids"].to(model.device),
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attention_mask=inputs["attention_mask"].to(model.device),
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max_new_tokens=4096,
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do_sample=False, # greedy decoding for deterministic JSON
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temperature=None,
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top_p=None,
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pad_token_id=tokenizer.eos_token_id,
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)
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new_tokens = outputs[0][inputs["input_ids"].shape[1]:]
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response = tokenizer.decode(new_tokens, skip_special_tokens=True)
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result = json.loads(response)
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```
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### Using with the UnBias-Plus toolkit
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```python
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from unbias_plus import UnBiasPlus
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pipe = UnBiasPlus(
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model_name_or_path="vector-institute/Qwen3-8B-UnBias-Plus-SFT-Instruct",
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load_in_4bit=False, # set True for ~5GB VRAM
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)
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result = pipe.analyze("Your article text here...")
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print(result.binary_label) # "biased" or "unbiased"
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print(result.severity) # 0, 2, 3, or 4
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print(len(result.biased_segments))
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print(result.unbiased_text)
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```
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### Using with vLLM
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```bash
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vllm serve vector-institute/Qwen3-8B-UnBias-Plus-SFT-Instruct \
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--max-model-len 8192
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```
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```python
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from openai import OpenAI
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client = OpenAI(base_url="http://localhost:8000/v1", api_key="EMPTY")
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completion = client.chat.completions.create(
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model="vector-institute/Qwen3-8B-UnBias-Plus-SFT-Instruct",
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messages=messages,
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max_tokens=4096,
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temperature=0,
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extra_body={"chat_template_kwargs": {"enable_thinking": False}},
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)
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result = json.loads(completion.choices[0].message.content)
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```
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## Output Schema
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```json
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{
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"binary_label": "biased" | "unbiased",
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"severity": 0 | 2 | 3 | 4,
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"bias_found": true | false,
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"biased_segments": [
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{
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"original": "exact substring from input article",
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"replacement": "neutral alternative phrase",
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"severity": "high" | "medium" | "low",
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"bias_type": "loaded language | dehumanizing framing | false generalizations | framing bias | euphemism/dysphemism | politically charged terminology | sensationalism",
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"reasoning": "1-2 sentence explanation"
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}
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],
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"unbiased_text": "Full rewritten neutral article"
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}
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```
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### Severity Scale
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| Value | Meaning |
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|---|---|
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| 0 | Neutral — no bias detected |
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| 2 | Recurring biased framing |
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| 3 | Strong persuasive tone |
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| 4 | Inflammatory rhetoric |
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## Bias Types Detected
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- **Loaded language** — words with strong emotional connotations
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- **Dehumanizing framing** — language that strips dignity from groups
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- **False generalizations** — sweeping statements ("they always", "all of them")
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- **Framing bias** — selective wording that implies a viewpoint
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- **Euphemism/dysphemism** — softening or hardening language to manipulate perception
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- **Politically charged terminology** — labels used to provoke rather than describe
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- **Sensationalism** — exaggerated language to evoke emotional responses
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## Training Data
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Fine-tuned on [vector-institute/Unbias-plus](https://huggingface.co/datasets/vector-institute/Unbias-plus), a curated dataset of news articles with expert-annotated bias labels, segment-level annotations, and neutral rewrites.
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## Hardware Requirements
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| Setup | Configuration |
|
||||
|---|---|
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| Recommended (server) | `load_in_4bit=False, dtype=torch.bfloat16` (~16GB VRAM) |
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| Lightweight (laptop) | `load_in_4bit=True` (~5GB VRAM) |
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## Model Variants
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| | [4B SFT](https://huggingface.co/vector-institute/Qwen3-4B-UnBias-Plus-SFT) | [8B SFT](https://huggingface.co/vector-institute/Qwen3-8B-UnBias-Plus-SFT) | 8B SFT-Instruct (this) |
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|---|---|---|---|
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| VRAM (bf16) | ~8GB | ~16GB | ~16GB |
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| VRAM (4-bit) | ~3GB | ~5GB | ~5GB |
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| Thinking mode | ✓ | ✓ | ✗ |
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| vLLM compatible | Partial | Partial | ✓ |
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| Quality | Strong | Higher | Higher |
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| Recommended for | Laptops | Research | Production APIs |
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## Limitations
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- Trained primarily on English-language news articles
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- Political bias detection reflects patterns in the training data
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- Best performance on articles under 5000 characters
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- As with all language models, outputs should be reviewed by a human before use in production
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## Citation
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If you use this model in your research or application, please cite:
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```bibtex
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@misc{unbias-plus-8b-instruct,
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title = {Qwen3-8B-UnBias-Plus-SFT-Instruct},
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author = {Vector Institute},
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year = {2026},
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publisher = {HuggingFace},
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howpublished = {\url{https://huggingface.co/vector-institute/Qwen3-8B-UnBias-Plus-SFT-Instruct}},
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note = {Part of the UnBias-Plus project: https://github.com/VectorInstitute/unbias-plus}
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}
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```
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## Links
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- 🔗 Project: [UnBias-Plus on GitHub](https://github.com/VectorInstitute/unbias-plus)
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- 📊 Dataset: [vector-institute/Unbias-plus](https://huggingface.co/datasets/vector-institute/Unbias-plus)
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- 🏛️ Organization: [Vector Institute](https://vectorinstitute.ai)
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- 🤖 4B version: [vector-institute/Qwen3-4B-UnBias-Plus-SFT](https://huggingface.co/vector-institute/Qwen3-4B-UnBias-Plus-SFT)
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- 🤖 8B thinking version: [vector-institute/Qwen3-8B-UnBias-Plus-SFT](https://huggingface.co/vector-institute/Qwen3-8B-UnBias-Plus-SFT)
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added_tokens.json
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{
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"</think>": 151668,
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"</tool_call>": 151658,
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"</tool_response>": 151666,
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"<think>": 151667,
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"<tool_call>": 151657,
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"<tool_response>": 151665,
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"<|PAD_TOKEN|>": 151669,
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"<|box_end|>": 151649,
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"<|box_start|>": 151648,
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"<|endoftext|>": 151643,
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"<|file_sep|>": 151664,
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"<|fim_middle|>": 151660,
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"<|fim_pad|>": 151662,
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"<|fim_prefix|>": 151659,
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"<|fim_suffix|>": 151661,
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"<|im_end|>": 151645,
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"<|im_start|>": 151644,
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"<|image_pad|>": 151655,
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"<|object_ref_end|>": 151647,
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"<|object_ref_start|>": 151646,
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"<|quad_end|>": 151651,
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"<|quad_start|>": 151650,
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"<|repo_name|>": 151663,
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"<|video_pad|>": 151656,
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"<|vision_end|>": 151653,
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"<|vision_pad|>": 151654,
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"<|vision_start|>": 151652
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}
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chat_template.jinja
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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 + '\n\n' }}
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{%- endif %}
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{{- "# 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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{%- endif %}
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{%- endif %}
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{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
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{%- for forward_message in messages %}
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{%- set index = (messages|length - 1) - loop.index0 %}
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{%- set message = messages[index] %}
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{%- set current_content = message.content if message.content is not none else '' %}
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{%- set tool_start = '<tool_response>' %}
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{%- set tool_start_length = tool_start|length %}
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{%- set start_of_message = current_content[:tool_start_length] %}
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{%- set tool_end = '</tool_response>' %}
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{%- set tool_end_length = tool_end|length %}
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{%- set start_pos = (current_content|length) - tool_end_length %}
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{%- if start_pos < 0 %}
|
||||
{%- set start_pos = 0 %}
|
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{%- endif %}
|
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{%- set end_of_message = current_content[start_pos:] %}
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{%- if ns.multi_step_tool and message.role == "user" and not(start_of_message == tool_start and end_of_message == tool_end) %}
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{%- set ns.multi_step_tool = false %}
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{%- set ns.last_query_index = index %}
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{%- endif %}
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||||
{%- endfor %}
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{%- for message in messages %}
|
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{%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
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{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
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{%- elif message.role == "assistant" %}
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{%- set content = message.content %}
|
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{%- set reasoning_content = '' %}
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{%- if message.reasoning_content is defined and message.reasoning_content is not none %}
|
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{%- set reasoning_content = message.reasoning_content %}
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{%- else %}
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{%- if '</think>' in message.content %}
|
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{%- set content = (message.content.split('</think>')|last).lstrip('\n') %}
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||||
{%- set reasoning_content = (message.content.split('</think>')|first).rstrip('\n') %}
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{%- set reasoning_content = (reasoning_content.split('<think>')|last).lstrip('\n') %}
|
||||
{%- endif %}
|
||||
{%- endif %}
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||||
{%- if loop.index0 > ns.last_query_index %}
|
||||
{%- if loop.last or (not loop.last and reasoning_content) %}
|
||||
{{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
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{%- else %}
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||||
{{- '<|im_start|>' + message.role + '\n' + content }}
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||||
{%- endif %}
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||||
{%- else %}
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||||
{{- '<|im_start|>' + message.role + '\n' + content }}
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||||
{%- endif %}
|
||||
{%- if message.tool_calls %}
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||||
{%- for tool_call in message.tool_calls %}
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{%- if (loop.first and content) or (not loop.first) %}
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{{- '\n' }}
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||||
{%- endif %}
|
||||
{%- if tool_call.function %}
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||||
{%- set tool_call = tool_call.function %}
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||||
{%- endif %}
|
||||
{{- '<tool_call>\n{"name": "' }}
|
||||
{{- tool_call.name }}
|
||||
{{- '", "arguments": ' }}
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||||
{%- if tool_call.arguments is string %}
|
||||
{{- tool_call.arguments }}
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||||
{%- else %}
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||||
{{- tool_call.arguments | tojson }}
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||||
{%- endif %}
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||||
{{- '}\n</tool_call>' }}
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||||
{%- endfor %}
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||||
{%- endif %}
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||||
{{- '<|im_end|>\n' }}
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||||
{%- elif message.role == "tool" %}
|
||||
{%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
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||||
{{- '<|im_start|>user' }}
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||||
{%- endif %}
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||||
{{- '\n<tool_response>\n' }}
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||||
{{- message.content }}
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||||
{{- '\n</tool_response>' }}
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||||
{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
|
||||
{{- '<|im_end|>\n' }}
|
||||
{%- endif %}
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||||
{%- endif %}
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||||
{%- endfor %}
|
||||
{%- if add_generation_prompt %}
|
||||
{{- '<|im_start|>assistant\n' }}
|
||||
{%- if enable_thinking is defined and enable_thinking is false %}
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{{- '<think>\n\n</think>\n\n' }}
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25
special_tokens_map.json
Normal file
25
special_tokens_map.json
Normal file
@@ -0,0 +1,25 @@
|
||||
{
|
||||
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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
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||||
oid sha256:10ba4ba91270b1a50e5cd8e51023bccc66fc4ac4909dd7ae7ab29433411c9bb9
|
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size 11422844
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249
tokenizer_config.json
Normal file
249
tokenizer_config.json
Normal file
@@ -0,0 +1,249 @@
|
||||
{
|
||||
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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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||||
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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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|
||||
"chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0].role == 'system' %}\n {{- messages[0].content + '\\n\\n' }}\n {%- endif %}\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 {%- endif %}\n{%- endif %}\n{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}\n{%- for forward_message in messages %}\n {%- set index = (messages|length - 1) - loop.index0 %}\n {%- set message = messages[index] %}\n {%- set current_content = message.content if message.content is not none else '' %}\n {%- set tool_start = '<tool_response>' %}\n {%- set tool_start_length = tool_start|length %}\n {%- set start_of_message = current_content[:tool_start_length] %}\n {%- set tool_end = '</tool_response>' %}\n {%- set tool_end_length = tool_end|length %}\n {%- set start_pos = (current_content|length) - tool_end_length %}\n {%- if start_pos < 0 %}\n {%- set start_pos = 0 %}\n {%- endif %}\n {%- set end_of_message = current_content[start_pos:] %}\n {%- if ns.multi_step_tool and message.role == \"user\" and not(start_of_message == tool_start and end_of_message == tool_end) %}\n {%- set ns.multi_step_tool = false %}\n {%- set ns.last_query_index = index %}\n {%- endif %}\n{%- endfor %}\n{%- for message in messages %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) %}\n {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {%- set content = message.content %}\n {%- set reasoning_content = '' %}\n {%- if message.reasoning_content is defined and message.reasoning_content is not none %}\n {%- set reasoning_content = message.reasoning_content %}\n {%- else %}\n {%- if '</think>' in message.content %}\n {%- set content = (message.content.split('</think>')|last).lstrip('\\n') %}\n {%- set reasoning_content = (message.content.split('</think>')|first).rstrip('\\n') %}\n {%- set reasoning_content = (reasoning_content.split('<think>')|last).lstrip('\\n') %}\n {%- endif %}\n {%- endif %}\n {%- if loop.index0 > ns.last_query_index %}\n {%- if loop.last or (not loop.last and reasoning_content) %}\n {{- '<|im_start|>' + message.role + '\\n<think>\\n' + reasoning_content.strip('\\n') + '\\n</think>\\n\\n' + content.lstrip('\\n') }}\n {%- else %}\n {{- '<|im_start|>' + message.role + '\\n' + content }}\n {%- endif %}\n {%- else %}\n {{- '<|im_start|>' + message.role + '\\n' + content }}\n {%- endif %}\n {%- if message.tool_calls %}\n {%- for tool_call in message.tool_calls %}\n {%- if (loop.first and content) or (not loop.first) %}\n {{- '\\n' }}\n {%- endif %}\n {%- if tool_call.function %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '<tool_call>\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {%- if tool_call.arguments is string %}\n {{- tool_call.arguments }}\n {%- else %}\n {{- tool_call.arguments | tojson }}\n {%- endif %}\n {{- '}\\n</tool_call>' }}\n {%- endfor %}\n {%- endif %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if loop.first 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 {%- if enable_thinking is defined and enable_thinking is false %}\n {{- '<think>\\n\\n</think>\\n\\n' }}\n {%- endif %}\n{%- endif %}"
|
||||
}
|
||||
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