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---
datasets:
- TigerResearch/pretrain_zh
base_model:
- Qwen/Qwen2.5-14B
tags:
- character
- generation
license: apache-2.0
---
**Qwen2.5-14B-Character**
**Introduction:**
**Qwen2.5-14B-Character** is the Character version of [Qwen2.5-14B](https://huggingface.co/Qwen/Qwen2.5-14B) model. It is developed based on the [Qwen2.5-14B](https://huggingface.co/Qwen/Qwen2.5-14B) model. It is specifically designed for character-to-character transformation and generation tasks.
**Core Contributions:**
1. **Modified Token Vocabulary:** The original model's token vocabulary has been revised to remove tokens representing phrases and multiple characters. This refinement enhances the model's focus on individual character processing.
2. **Continued Pre-training:** Based on the modified vocabulary, the model has undergone further pre-training to optimize its performance and adaptability for character-level tasks.
**Training Dataset:**
The model has been trained using the `TigerResearch/pretrain_zh` dataset, a comprehensive Chinese pre-training dataset provided by **TigerResearch**. For more information about the dataset, please visit: [TigerResearch/pretrain_zh](https://huggingface.co/datasets/TigerResearch/pretrain_zh).
**Training Code:**
The training process for this model was facilitated by the **LLaMA-Factory**, an open-source project that provides tools and frameworks for training language models. The LLaMa-factory codebase is available at: [LLaMA-Factory](https://github.com/hiyouga/LLaMA-Factory).
**Results**
To assess the efficacy of the Qwen2.5-14B-Character, we evaluated its performance on three widely utilized benchmarks: C-Evel, CMMLU, and MMLU. The results are tabulated as follows:
| Model | ceval| cmmlu| mmlu|
| :---: | :---: | :---: | :---: |
| Qwen2.5-14B | 85.29| 85.84| 79.86|
| Qwen2.5-14B-filter | 83.43| 83.72| 79.75|
| Qwen2.5-14B-Character | 84.99| 84.60| 79.61|
In the table, to discern the model performance more distinctly, we have presented the test results for both the original Qwen2.5-14B (Qwen2.5-14B) and the token-modified Qwen2.5-14B (Qwen2.5-14B-filter).
**Quickstart**
The latest version of transformers is recommended (at least 4.37.0). Here we show a code snippet to show you how to use the chat model with transformers:
```shell
from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
model_name = 'Henry94/Qwen2.5-14B-Character'
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype="auto", device_map="auto")
prompt = "请简单介绍一下大型语言模型."
messages = [
{"role": "system", "content": "You are Qwen, created by Alibaba Cloud. You are a helpful assistant."},
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.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]
print(response)
``` |