初始化项目，由ModelHub XC社区提供模型
Model: QuantFactory/Qwen2.5-Math-7B-Instruct-GGUF Source: Original Platform
This commit is contained in:
ModelHub XC
132
README.md
Normal file
132
README.md
Normal file
@@ -0,0 +1,132 @@
|
||||
|
||||
---
|
||||
|
||||
base_model: Qwen/Qwen2.5-Math-7B
|
||||
language:
|
||||
- en
|
||||
pipeline_tag: text-generation
|
||||
tags:
|
||||
- chat
|
||||
library_name: transformers
|
||||
license: apache-2.0
|
||||
license_link: https://huggingface.co/Qwen/Qwen2.5-Math-7B-Instruct/blob/main/LICENSE
|
||||
|
||||
---
|
||||
|
||||
[](https://hf.co/QuantFactory)
|
||||
|
||||
|
||||
# QuantFactory/Qwen2.5-Math-7B-Instruct-GGUF
|
||||
This is quantized version of [Qwen/Qwen2.5-Math-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Math-7B-Instruct) created using llama.cpp
|
||||
|
||||
# Original Model Card
|
||||
|
||||
|
||||
|
||||
# Qwen2.5-Math-7B-Instruct
|
||||
|
||||
> [!Warning]
|
||||
> <div align="center">
|
||||
> <b>
|
||||
> 🚨 Qwen2.5-Math mainly supports solving English and Chinese math problems through CoT and TIR. We do not recommend using this series of models for other tasks.
|
||||
> </b>
|
||||
> </div>
|
||||
|
||||
## Introduction
|
||||
|
||||
In August 2024, we released the first series of mathematical LLMs - [Qwen2-Math](https://qwenlm.github.io/blog/qwen2-math/) - of our Qwen family. A month later, we have upgraded it and open-sourced **Qwen2.5-Math** series, including base models **Qwen2.5-Math-1.5B/7B/72B**, instruction-tuned models **Qwen2.5-Math-1.5B/7B/72B-Instruct**, and mathematical reward model **Qwen2.5-Math-RM-72B**.
|
||||
|
||||
Unlike Qwen2-Math series which only supports using Chain-of-Thught (CoT) to solve English math problems, Qwen2.5-Math series is expanded to support using both CoT and Tool-integrated Reasoning (TIR) to solve math problems in both Chinese and English. The Qwen2.5-Math series models have achieved significant performance improvements compared to the Qwen2-Math series models on the Chinese and English mathematics benchmarks with CoT.
|
||||
|
||||
|
||||
|
||||
While CoT plays a vital role in enhancing the reasoning capabilities of LLMs, it faces challenges in achieving computational accuracy and handling complex mathematical or algorithmic reasoning tasks, such as finding the roots of a quadratic equation or computing the eigenvalues of a matrix. TIR can further improve the model's proficiency in precise computation, symbolic manipulation, and algorithmic manipulation. Qwen2.5-Math-1.5B/7B/72B-Instruct achieve 79.7, 85.3, and 87.8 respectively on the MATH benchmark using TIR.
|
||||
|
||||
## Model Details
|
||||
|
||||
|
||||
For more details, please refer to our [blog post](https://qwenlm.github.io/blog/qwen2.5-math/) and [GitHub repo](https://github.com/QwenLM/Qwen2.5-Math).
|
||||
|
||||
|
||||
## Requirements
|
||||
* `transformers>=4.37.0` for Qwen2.5-Math models. The latest version is recommended.
|
||||
|
||||
> [!Warning]
|
||||
> <div align="center">
|
||||
> <b>
|
||||
> 🚨 This is a must because <code>transformers</code> integrated Qwen2 codes since <code>4.37.0</code>.
|
||||
> </b>
|
||||
> </div>
|
||||
|
||||
For requirements on GPU memory and the respective throughput, see similar results of Qwen2 [here](https://qwen.readthedocs.io/en/latest/benchmark/speed_benchmark.html).
|
||||
|
||||
## Quick Start
|
||||
|
||||
> [!Important]
|
||||
>
|
||||
> **Qwen2.5-Math-7B-Instruct** is an instruction model for chatting;
|
||||
>
|
||||
> **Qwen2.5-Math-7B** is a base model typically used for completion and few-shot inference, serving as a better starting point for fine-tuning.
|
||||
>
|
||||
|
||||
### 🤗 Hugging Face Transformers
|
||||
|
||||
Qwen2.5-Math can be deployed and infered in the same way as [Qwen2.5](https://github.com/QwenLM/Qwen2.5). Here we show a code snippet to show you how to use the chat model with `transformers`:
|
||||
|
||||
```python
|
||||
from transformers import AutoModelForCausalLM, AutoTokenizer
|
||||
|
||||
model_name = "Qwen/Qwen2.5-Math-7B-Instruct"
|
||||
device = "cuda" # the device to load the model onto
|
||||
|
||||
model = AutoModelForCausalLM.from_pretrained(
|
||||
model_name,
|
||||
torch_dtype="auto",
|
||||
device_map="auto"
|
||||
)
|
||||
tokenizer = AutoTokenizer.from_pretrained(model_name)
|
||||
|
||||
prompt = "Find the value of $x$ that satisfies the equation $4x+5 = 6x+7$."
|
||||
|
||||
# CoT
|
||||
messages = [
|
||||
{"role": "system", "content": "Please reason step by step, and put your final answer within \\boxed{}."},
|
||||
{"role": "user", "content": prompt}
|
||||
]
|
||||
|
||||
# TIR
|
||||
messages = [
|
||||
{"role": "system", "content": "Please integrate natural language reasoning with programs to solve the problem above, and put your final answer within \\boxed{}."},
|
||||
{"role": "user", "content": prompt}
|
||||
]
|
||||
|
||||
text = tokenizer.apply_chat_template(
|
||||
messages,
|
||||
tokenize=False,
|
||||
add_generation_prompt=True
|
||||
)
|
||||
model_inputs = tokenizer([text], return_tensors="pt").to(device)
|
||||
|
||||
generated_ids = model.generate(
|
||||
**model_inputs,
|
||||
max_new_tokens=512
|
||||
)
|
||||
generated_ids = [
|
||||
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
|
||||
]
|
||||
|
||||
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
|
||||
```
|
||||
|
||||
## Citation
|
||||
|
||||
If you find our work helpful, feel free to give us a citation.
|
||||
|
||||
```
|
||||
@article{yang2024qwen2,
|
||||
title={Qwen2 technical report},
|
||||
author={Yang, An and Yang, Baosong and Hui, Binyuan and Zheng, Bo and Yu, Bowen and Zhou, Chang and Li, Chengpeng and Li, Chengyuan and Liu, Dayiheng and Huang, Fei and others},
|
||||
journal={arXiv preprint arXiv:2407.10671},
|
||||
year={2024}
|
||||
}
|
||||
```
|
||||
Reference in New Issue
Block a user