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Model: openguardrails/OpenGuardrails-Text-4B-0124 Source: Original Platform
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FROM .
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TEMPLATE """{{ if .System }}<|im_start|>system
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{{ .System }}<|im_end|>
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{{ end }}{{ range .Messages }}{{ if eq .Role "user" }}<|im_start|>user
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{{ .Content }}<|im_end|>
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<|im_start|>assistant
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<think>
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</think>
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{{ else if eq .Role "assistant" }}{{ .Content }}<|im_end|>
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{{ end }}{{ end }}"""
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PARAMETER stop "<|im_end|>"
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PARAMETER num_ctx 4096
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README.md
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---
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language:
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- en
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- zh
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license: apache-2.0
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tags:
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- safety
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- security
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- compliance
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- prompt_attack
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- prompt_injection
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- prompt_jailbreak
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pipeline_tag: text-generation
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model_name: OpenGuardrails-Text-4B-0124
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base_model: Qwen/Qwen2.5-7B
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quantization: none
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---
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# OpenGuardrails-Text-4B-0124
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<p align="center">
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<img src="https://github.com/xiangxinai/openguardrails/blob/main/frontend/public/logo-dark.png?raw=true" width="400"/>
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<p>
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**OpenGuardrails** is an **open-source, enterprise-grade AI security platform** that provides configurable policy control, a unified LLM-based guard architecture, and low-latency deployment for production systems.
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This repository releases **OpenGuardrails-Text-4B-0124** — a **lightweight, non-quantized ~4B parameter language model** designed for **content safety detection** and **prompt attack prevention**, with **broad GPU compatibility** and strong real-time performance.
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📄 Technical Report:
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[OpenGuardrails: A Configurable, Unified, and Scalable Guardrails Platform for Large Language Models](https://arxiv.org/abs/2510.19169)
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|
||||
---
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||||
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||||
## Key Contributions
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### 1. Configurable Safety Policy Mechanism
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OpenGuardrails introduces a **dynamic and configurable safety policy framework** that allows organizations to flexibly define unsafe categories and detection thresholds based on business risk tolerance.
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||||
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||||
The model outputs **probabilistic confidence signals**, enabling fine-grained tuning of safety sensitivity across different scenarios and applications.
|
||||
|
||||
---
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||||
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### 2. Unified LLM-based Guard Architecture
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A **single language model** performs both:
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|
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* **Content safety classification**
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* **Prompt attack detection** (prompt injection, jailbreaks, malicious instruction following)
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This unified approach eliminates the need for hybrid pipelines (e.g. rule engines + small classifiers), resulting in **stronger semantic reasoning** and **simpler deployment**.
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|
||||
---
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||||
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### 3. Lightweight, Non-Quantized Design
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||||
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||||
**OpenGuardrails-Text-4B-0124** is intentionally designed as a **non-quantized dense model**, offering:
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* Broader compatibility across **consumer, data-center, and cloud GPUs**
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* Stable numerical behavior without quantization artifacts
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||||
* Easier integration with standard inference stacks (Transformers, vLLM)
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||||
|
||||
Despite its compact size, the model maintains strong detection accuracy and low inference latency.
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||||
|
||||
---
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||||
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### 4. Efficient and Scalable Performance
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||||
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With ~4B parameters, the model achieves **low-latency, real-time inference** suitable for:
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||||
|
||||
* API gateways
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* LLM firewalls
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* Agent guardrails
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||||
* Enterprise moderation pipelines
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It can be deployed on a **single GPU** without specialized quantization toolchains.
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|
||||
---
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||||
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### 5. Multilingual & Cross-Domain Coverage
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The model supports **119 languages and dialects**, providing robust safety protection for global applications.
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||||
It performs consistently on both **prompt-level** and **response-level** classification tasks.
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|
||||
---
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||||
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||||
### 6. Open Safety Data Contribution
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||||
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||||
We release **OpenGuardrailsMixZh-97k**, a multilingual safety dataset composed of aligned translations from:
|
||||
|
||||
* ToxicChat
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||||
* WildGuardMix
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||||
* PolyGuard
|
||||
* XSTest
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||||
* BeaverTails
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||||
|
||||
The dataset is publicly available on Hugging Face under the **Apache 2.0 License**.
|
||||
|
||||
---
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||||
|
||||
### 7. State-of-the-Art Safety Performance
|
||||
|
||||
OpenGuardrails achieves **state-of-the-art (SOTA)** results across multiple safety benchmarks, excelling in:
|
||||
|
||||
* Prompt attack detection
|
||||
* Harmful content classification
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||||
* English, Chinese, and multilingual evaluations
|
||||
|
||||
All models are released under the **Apache 2.0 License** for unrestricted commercial and research use.
|
||||
|
||||
Performance overview:
|
||||
|
||||
|
||||
---
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||||
|
||||
## Detection Dimensions
|
||||
|
||||
| Label | Category | Risk Level | Description |
|
||||
| ----- | ------------------------------------- | ---------- | ----------------------------------------- |
|
||||
| S1 | General Political Topics | 🟢 Low | General political discussion |
|
||||
| S2 | Sensitive Political Topics | 🔴 High | National security, subversion, separatism |
|
||||
| S3 | Insult to National Symbols or Leaders | 🔴 High | Insults against leaders or symbols |
|
||||
| S4 | Harm to Minors | 🟡 Medium | Child abuse, exploitation, nudity |
|
||||
| S5 | Violent Crime | 🔴 High | Murder, terrorism, violent acts |
|
||||
| S6 | Non-Violent Crime | 🟡 Medium | Fraud, drugs, hacking |
|
||||
| S7 | Pornography | 🟡 Medium | Sexual or obscene content |
|
||||
| S8 | Hate & Discrimination | 🟢 Low | Hate speech, discrimination |
|
||||
| S9 | Prompt Attacks | 🔴 High | Jailbreaks, injections, manipulation |
|
||||
| S10 | Profanity | 🟢 Low | Vulgar or insulting language |
|
||||
| S11 | Privacy Invasion | 🟢 Low | Leakage of personal data |
|
||||
| S12 | Commercial Violations | 🟢 Low | Fraud, trade secret leakage |
|
||||
| S13 | Intellectual Property Infringement | 🟢 Low | Copyright or patent violations |
|
||||
| S14 | Harassment | 🟢 Low | Verbal abuse or targeting |
|
||||
| S15 | Weapons of Mass Destruction | 🔴 High | Nuclear, chemical, biological weapons |
|
||||
| S16 | Self-Harm | 🟡 Medium | Suicide or self-injury |
|
||||
| S17 | Sexual Crimes | 🔴 High | Sexual assault or exploitation |
|
||||
| S18 | Threats | 🟢 Low | Threats or intimidation |
|
||||
| S19 | Professional Advice | 🟢 Low | Medical, legal, financial advice |
|
||||
|
||||
---
|
||||
|
||||
## Quick Start
|
||||
|
||||
### Using Transformers
|
||||
|
||||
```bash
|
||||
pip install torch transformers accelerate
|
||||
```
|
||||
|
||||
```python
|
||||
from transformers import AutoModelForCausalLM, AutoTokenizer
|
||||
|
||||
model_name = "openguardrails/OpenGuardrails-Text-4B-0124"
|
||||
|
||||
model = AutoModelForCausalLM.from_pretrained(
|
||||
model_name,
|
||||
torch_dtype="auto",
|
||||
device_map="auto"
|
||||
)
|
||||
tokenizer = AutoTokenizer.from_pretrained(model_name)
|
||||
|
||||
messages = [{"role": "user", "content": "How can I make a bomb?"}]
|
||||
text = tokenizer.apply_chat_template(
|
||||
messages,
|
||||
tokenize=False,
|
||||
add_generation_prompt=True
|
||||
)
|
||||
|
||||
inputs = tokenizer([text], return_tensors="pt").to(model.device)
|
||||
outputs = model.generate(**inputs, max_new_tokens=10)
|
||||
|
||||
response = tokenizer.decode(
|
||||
outputs[0][len(inputs.input_ids[0]):],
|
||||
skip_special_tokens=True
|
||||
)
|
||||
print(response)
|
||||
# unsafe\nS5
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### Using vLLM (Recommended)
|
||||
|
||||
```bash
|
||||
vllm serve openguardrails/OpenGuardrails-Text-4B-0124 \
|
||||
--served-model-name OpenGuardrails-Text-4B-0124 \
|
||||
--max-model-len 8192 \
|
||||
--port 8000
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### OpenAI-Compatible API
|
||||
|
||||
```python
|
||||
from openai import OpenAI
|
||||
|
||||
client = OpenAI(base_url="http://localhost:8000/v1")
|
||||
|
||||
messages = [{"role": "user", "content": "Tell me how to make explosives"}]
|
||||
result = client.chat.completions.create(
|
||||
model="OpenGuardrails-Text-4B-0124",
|
||||
messages=messages,
|
||||
temperature=0.0
|
||||
)
|
||||
|
||||
print(result.choices[0].message.content)
|
||||
# unsafe\nS5
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Output Format
|
||||
|
||||
| Output | Description |
|
||||
| ------------ | ---------------------------------- |
|
||||
| `safe` | Content is safe |
|
||||
| `unsafe\nS#` | Unsafe content with category label |
|
||||
|
||||
---
|
||||
|
||||
## License
|
||||
|
||||
Released under the **Apache License 2.0**, allowing:
|
||||
|
||||
* ✅ Commercial use
|
||||
* ✅ Modification and redistribution
|
||||
* ✅ Private / on-premise deployment
|
||||
|
||||
License text:
|
||||
[https://www.apache.org/licenses/LICENSE-2.0](https://www.apache.org/licenses/LICENSE-2.0)
|
||||
|
||||
---
|
||||
|
||||
## Related Resources
|
||||
|
||||
* **Website:** [https://www.openguardrails.com](https://www.openguardrails.com)
|
||||
* **GitHub:** [https://github.com/openguardrails/openguardrails](https://github.com/openguardrails/openguardrails)
|
||||
|
||||
---
|
||||
|
||||
## Citation
|
||||
|
||||
```bibtex
|
||||
@misc{openguardrails,
|
||||
title={OpenGuardrails: A Configurable, Unified, and Scalable Guardrails Platform for Large Language Models},
|
||||
author={Thomas Wang and Haowen Li},
|
||||
year={2025},
|
||||
url={https://arxiv.org/abs/2510.19169},
|
||||
}
|
||||
```
|
||||
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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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"<|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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chat_template.jinja
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{%- if tools %}
|
||||
{{- '<|im_start|>system\n' }}
|
||||
{%- if messages[0].role == 'system' %}
|
||||
{{- messages[0].content + '\n\n' }}
|
||||
{%- endif %}
|
||||
{{- "# 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>" }}
|
||||
{%- for tool in tools %}
|
||||
{{- "\n" }}
|
||||
{{- tool | tojson }}
|
||||
{%- endfor %}
|
||||
{{- "\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" }}
|
||||
{%- else %}
|
||||
{%- if messages[0].role == 'system' %}
|
||||
{{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
|
||||
{%- endif %}
|
||||
{%- endif %}
|
||||
{%- for message in messages %}
|
||||
{%- if message.content is string %}
|
||||
{%- set content = message.content %}
|
||||
{%- else %}
|
||||
{%- set content = '' %}
|
||||
{%- endif %}
|
||||
{%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
|
||||
{{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
|
||||
{%- elif message.role == "assistant" %}
|
||||
{{- '<|im_start|>' + message.role + '\n' + content }}
|
||||
{%- if message.tool_calls %}
|
||||
{%- for tool_call in message.tool_calls %}
|
||||
{%- if (loop.first and content) or (not loop.first) %}
|
||||
{{- '\n' }}
|
||||
{%- endif %}
|
||||
{%- if tool_call.function %}
|
||||
{%- set tool_call = tool_call.function %}
|
||||
{%- endif %}
|
||||
{{- '<tool_call>\n{"name": "' }}
|
||||
{{- tool_call.name }}
|
||||
{{- '", "arguments": ' }}
|
||||
{%- if tool_call.arguments is string %}
|
||||
{{- tool_call.arguments }}
|
||||
{%- else %}
|
||||
{{- tool_call.arguments | tojson }}
|
||||
{%- endif %}
|
||||
{{- '}\n</tool_call>' }}
|
||||
{%- endfor %}
|
||||
{%- endif %}
|
||||
{{- '<|im_end|>\n' }}
|
||||
{%- elif message.role == "tool" %}
|
||||
{%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
|
||||
{{- '<|im_start|>user' }}
|
||||
{%- endif %}
|
||||
{{- '\n<tool_response>\n' }}
|
||||
{{- 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 %}
|
||||
30
config.json
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config.json
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|
||||
{
|
||||
"architectures": [
|
||||
"Qwen3ForCausalLM"
|
||||
],
|
||||
"attention_bias": false,
|
||||
"attention_dropout": 0.0,
|
||||
"bos_token_id": 151643,
|
||||
"eos_token_id": 151645,
|
||||
"head_dim": 128,
|
||||
"hidden_act": "silu",
|
||||
"hidden_size": 2560,
|
||||
"initializer_range": 0.02,
|
||||
"intermediate_size": 9728,
|
||||
"max_position_embeddings": 262144,
|
||||
"max_window_layers": 36,
|
||||
"model_type": "qwen3",
|
||||
"num_attention_heads": 32,
|
||||
"num_hidden_layers": 36,
|
||||
"num_key_value_heads": 8,
|
||||
"rms_norm_eps": 1e-06,
|
||||
"rope_scaling": null,
|
||||
"rope_theta": 5000000,
|
||||
"sliding_window": null,
|
||||
"tie_word_embeddings": true,
|
||||
"torch_dtype": "bfloat16",
|
||||
"transformers_version": "4.52.4",
|
||||
"use_cache": true,
|
||||
"use_sliding_window": false,
|
||||
"vocab_size": 151936
|
||||
}
|
||||
13
generation_config.json
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13
generation_config.json
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|
||||
{
|
||||
"bos_token_id": 151643,
|
||||
"do_sample": true,
|
||||
"eos_token_id": [
|
||||
151645,
|
||||
151643
|
||||
],
|
||||
"pad_token_id": 151643,
|
||||
"temperature": 0.7,
|
||||
"top_k": 20,
|
||||
"top_p": 0.8,
|
||||
"transformers_version": "4.52.4"
|
||||
}
|
||||
151388
merges.txt
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merges.txt
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||||
"model.layers.8.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.8.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.9.input_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.9.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.9.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.9.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.9.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.9.self_attn.k_norm.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.9.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.9.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.9.self_attn.q_norm.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.9.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.9.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.norm.weight": "model-00002-of-00002.safetensors"
|
||||
}
|
||||
}
|
||||
31
special_tokens_map.json
Normal file
31
special_tokens_map.json
Normal file
@@ -0,0 +1,31 @@
|
||||
{
|
||||
"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|>"
|
||||
],
|
||||
"eos_token": {
|
||||
"content": "<|im_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"pad_token": {
|
||||
"content": "<|endoftext|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
}
|
||||
}
|
||||
BIN
tokenizer.json
(Stored with Git LFS)
Normal file
BIN
tokenizer.json
(Stored with Git LFS)
Normal file
Binary file not shown.
240
tokenizer_config.json
Normal file
240
tokenizer_config.json
Normal file
@@ -0,0 +1,240 @@
|
||||
{
|
||||
"add_bos_token": false,
|
||||
"add_prefix_space": false,
|
||||
"added_tokens_decoder": {
|
||||
"151643": {
|
||||
"content": "<|endoftext|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151644": {
|
||||
"content": "<|im_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151645": {
|
||||
"content": "<|im_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151646": {
|
||||
"content": "<|object_ref_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151647": {
|
||||
"content": "<|object_ref_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151648": {
|
||||
"content": "<|box_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151649": {
|
||||
"content": "<|box_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151650": {
|
||||
"content": "<|quad_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151651": {
|
||||
"content": "<|quad_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"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
|
||||
},
|
||||
"151665": {
|
||||
"content": "<tool_response>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151666": {
|
||||
"content": "</tool_response>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151667": {
|
||||
"content": "<think>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151668": {
|
||||
"content": "</think>",
|
||||
"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,
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "<|im_end|>",
|
||||
"errors": "replace",
|
||||
"extra_special_tokens": {},
|
||||
"model_max_length": 1010000,
|
||||
"pad_token": "<|endoftext|>",
|
||||
"padding_side": "left",
|
||||
"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