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Model: saadxsalman/Q-SS-0.5B-Reasoning-Math Source: Original Platform
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README.md
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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/Qwen2.5-0.5B-Instruct
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tags:
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- qwen2.5
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- math
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- reasoning
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- grpo
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- reinforcement-learning
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- unsloth
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- gsm8k
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- structured-output
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datasets:
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- openai/gsm8k
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- open-r1/OpenR1-Math-220k
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pipeline_tag: text-generation
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library_name: transformers
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---
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# Q-SS-0.5B-Reasoning-Math
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> *A compact, fast, and structured mathematical reasoning model — built to think before it answers.*
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**Q-SS-0.5B-Reasoning-Math** is a fine-tuned version of [Qwen/Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct), trained using **Group Relative Policy Optimization (GRPO)** reinforcement learning — the same technique behind DeepSeek-R1. The model is designed to reason explicitly and transparently through mathematical problems before producing a clean, parseable final answer.
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> 💾 Looking for the lightweight CPU version? See [Q-SS-0.5B-Reasoning-Math-GGUF](https://huggingface.co/saadxsalman/Q-SS-0.5B-Reasoning-Math-GGUF) for the Q4_K_M quantized model (~300MB).
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---
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## ✨ Highlights
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- 🧠 **Thinks out loud** — explicit step-by-step reasoning inside `<thought>` tags before every answer
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- 🎯 **Clean structured output** — final answer always isolated in `<answer>` tags, trivial to parse
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- 🔁 **RL-trained** — learned through reward signals, not just imitation
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- 🔧 **Fine-tunable** — full FP16 weights, ready for further training or fine-tuning
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- 🔓 **Apache 2.0** — free for personal and commercial use
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---
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## 📋 Model Details
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| Property | Details |
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|---|---|
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| **Model Name** | Q-SS-0.5B-Reasoning-Math |
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| **Base Model** | Qwen/Qwen2.5-0.5B-Instruct |
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| **Parameters** | 500M |
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| **Training Method** | SFT Warm-up + GRPO Reinforcement Learning |
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| **Trained On** | GSM8K + OpenR1-Math-220k |
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| **Precision** | FP16 (merged, no adapter needed) |
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| **License** | Apache 2.0 |
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| **Developer** | Saad Salman |
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---
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## 💬 Output Format
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Every response follows this strict structure:
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```
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<thought>
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[Step-by-step reasoning and calculations]
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</thought>
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<answer>
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[Final numerical answer only]
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</answer>
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```
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---
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## 🚀 Quick Start
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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model_name = "saadxsalman/Q-SS-0.5B-Reasoning-Math"
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForCausalLM.from_pretrained(
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model_name,
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torch_dtype = torch.float16,
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device_map = "auto",
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)
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SYSTEM_PROMPT = \"\"\"You are a mathematical reasoning engine.
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Solve the problem step-by-step inside <thought> tags, then give ONLY the
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final numerical or LaTeX result inside <answer> tags.
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<thought>
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[Your internal reasoning and calculations here]
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</thought>
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<answer>
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[Final answer only]
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</answer>\"\"\"
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def solve(problem):
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messages = [
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{"role": "system", "content": SYSTEM_PROMPT},
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{"role": "user", "content": problem},
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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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return_tensors = "pt",
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).to(model.device)
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with torch.no_grad():
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outputs = model.generate(
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input_ids = inputs,
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max_new_tokens = 384,
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temperature = 0.1,
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do_sample = True,
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pad_token_id = tokenizer.eos_token_id,
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)
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response = tokenizer.decode(outputs[0], skip_special_tokens=True)
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if "<answer>" in response:
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return response.split("<answer>")[-1].split("</answer>")[0].strip()
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return response
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print(solve("Janet has 3 cats. Each cat eats 2 cans of food per day. How many cans does she need for 7 days?"))
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# Output: 42
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```
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---
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## 📝 Example Outputs
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**Problem:** Janet has 3 cats. Each cat eats 2 cans of food per day. How many cans does she need for 7 days?
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```
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<thought>
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Each cat eats 2 cans per day.
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Janet has 3 cats, so they eat 3 × 2 = 6 cans per day together.
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For 7 days: 6 × 7 = 42 cans total.
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</thought>
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<answer>
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42
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</answer>
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```
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**Problem:** Tom has $50. He buys a book for $12 and a pen for $3. How much money does he have left?
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```
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<thought>
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Tom starts with $50.
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He spends $12 on a book and $3 on a pen.
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Total spent: 12 + 3 = $15.
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Money remaining: 50 - 15 = $35.
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</thought>
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<answer>
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35
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</answer>
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```
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---
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||||
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## ✅ What It's Good At
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||||
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||||
| Problem Type | Support |
|
||||
|---|---|
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||||
| Basic arithmetic | ✅ Reliable |
|
||||
| Multi-step word problems | ✅ Reliable |
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||||
| Problems with units and currency | ✅ Reliable |
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||||
| Basic algebra | ⚠️ Partial |
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||||
| Competition math (AMC/AIME) | ❌ Beyond capacity |
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||||
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||||
---
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||||
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## 📦 Related Models
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||||
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||||
| Repo | Format | Size | Best For |
|
||||
|---|---|---|---|
|
||||
| [Q-SS-0.5B-Reasoning-Math](https://huggingface.co/saadxsalman/Q-SS-0.5B-Reasoning-Math) | FP16 | ~988MB | GPU inference & further fine-tuning |
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||||
| [Q-SS-0.5B-Reasoning-Math-GGUF](https://huggingface.co/saadxsalman/Q-SS-0.5B-Reasoning-Math-GGUF) | Q4_K_M | ~300MB | Local CPU inference |
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||||
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||||
---
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||||
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||||
## ⚠️ Limitations
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||||
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||||
- Optimized for English language math problems only
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- Complex abstract reasoning, geometry, and calculus are beyond reliable capacity at 0.5B scale
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- Always verify critical calculations — the model may occasionally produce confident but incorrect answers
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---
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## 🙏 Acknowledgements
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- [Unsloth](https://github.com/unslothai/unsloth) — efficient fine-tuning framework
|
||||
- [Qwen Team](https://huggingface.co/Qwen) — Qwen2.5-0.5B-Instruct base model
|
||||
- [HuggingFace TRL](https://github.com/huggingface/trl) — GRPO implementation
|
||||
- [OpenR1](https://huggingface.co/open-r1) — OpenR1-Math-220k dataset
|
||||
- [OpenAI](https://huggingface.co/openai) — GSM8K dataset
|
||||
|
||||
---
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||||
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## 📄 Citation
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||||
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||||
```bibtex
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@misc{qss-reasoning-math-2025,
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||||
author = {Saad Salman},
|
||||
title = {Q-SS-0.5B-Reasoning-Math},
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||||
year = {2025},
|
||||
publisher = {HuggingFace},
|
||||
howpublished = {\\url{https://huggingface.co/saadxsalman/Q-SS-0.5B-Reasoning-Math}},
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||||
}
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||||
```
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chat_template.jinja
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chat_template.jinja
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{%- if tools %}
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{{- '<|im_start|>system\n' }}
|
||||
{%- if messages[0]['role'] == 'system' %}
|
||||
{{- messages[0]['content'] }}
|
||||
{%- else %}
|
||||
{{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
|
||||
{%- endif %}
|
||||
{{- "\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>" }}
|
||||
{%- for tool in tools %}
|
||||
{{- "\n" }}
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||||
{{- 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" }}
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||||
{%- else %}
|
||||
{%- if messages[0]['role'] == 'system' %}
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||||
{{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
|
||||
{%- else %}
|
||||
{{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
|
||||
{%- endif %}
|
||||
{%- endif %}
|
||||
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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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|
||||
{%- 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>' }}
|
||||
{%- 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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{
|
||||
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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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|
||||
"vocab_size": 151936
|
||||
}
|
||||
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model.safetensors
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|
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|
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|
||||
"151649": {
|
||||
"content": "<|box_end|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": true
|
||||
},
|
||||
"151650": {
|
||||
"content": "<|quad_start|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": true
|
||||
},
|
||||
"151651": {
|
||||
"content": "<|quad_end|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": true
|
||||
},
|
||||
"151652": {
|
||||
"content": "<|vision_start|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": true
|
||||
},
|
||||
"151653": {
|
||||
"content": "<|vision_end|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": true
|
||||
},
|
||||
"151654": {
|
||||
"content": "<|vision_pad|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": true
|
||||
},
|
||||
"151655": {
|
||||
"content": "<|image_pad|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": true
|
||||
},
|
||||
"151656": {
|
||||
"content": "<|video_pad|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": true
|
||||
},
|
||||
"151657": {
|
||||
"content": "<tool_call>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": false
|
||||
},
|
||||
"151658": {
|
||||
"content": "</tool_call>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": false
|
||||
},
|
||||
"151659": {
|
||||
"content": "<|fim_prefix|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": false
|
||||
},
|
||||
"151660": {
|
||||
"content": "<|fim_middle|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": false
|
||||
},
|
||||
"151661": {
|
||||
"content": "<|fim_suffix|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": false
|
||||
},
|
||||
"151662": {
|
||||
"content": "<|fim_pad|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": false
|
||||
},
|
||||
"151663": {
|
||||
"content": "<|repo_name|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": false
|
||||
},
|
||||
"151664": {
|
||||
"content": "<|file_sep|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": false
|
||||
},
|
||||
"151665": {
|
||||
"content": "<|PAD_TOKEN|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": true
|
||||
}
|
||||
},
|
||||
"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"
|
||||
}
|
||||
Reference in New Issue
Block a user