Improve language tag
Browse filesHi! As the model is multilingual, this is a PR to add other languages than English to the language tag to improve the referencing. Note that 29 languages are announced in the README, but only 13 are explicitly listed. I was therefore only able to add these 13 languages.
README.md
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license: apache-2.0
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datasets:
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- TigerResearch/pretrain_zh
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language:
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**
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```
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---
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license: apache-2.0
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datasets:
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- TigerResearch/pretrain_zh
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language:
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- zho
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- eng
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- fra
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- spa
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- por
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- deu
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- ita
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- rus
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- jpn
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- kor
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- vie
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- tha
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- ara
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base_model:
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- Qwen/Qwen2.5-3B
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tags:
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- qwen2.5
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- text-generation-inference
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- Text Generation
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- Character
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---
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**Qwen2.5-3B-Character**
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**Introduction:**
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**Qwen2.5-3B-Character** is the Character version of [Qwen2.5-3B](https://huggingface.co/Qwen/Qwen2.5-3B) model. It is developed based on the [Qwen2.5-3B](https://huggingface.co/Qwen/Qwen2.5-3B) model. It is specifically designed for character-to-character transformation and generation tasks.
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**Core Contributions:**
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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.
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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.
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**Training Dataset:**
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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).
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**Training Code:**
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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).
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**Results**
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To assess the efficacy of the Qwen2.5-3B-Character, we evaluated its performance on three widely utilized benchmarks: C-Evel, CMMLU, and MMLU. The results are tabulated as follows:
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| Model | ceval| cmmlu| mmlu|
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| :--- | :---: | :---: | :---: |
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| Qwen2.5-3B | 74.37| 74.94| 65.87 |
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| Qwen2.5-3B-filter | 70.43| 69.69| 65.53 |
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| Qwen2.5-3B-Character | 71.97| 71.94| 65.18 |
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In the table, to discern the model performance more distinctly, we have presented the test results for both the original Qwen2.5-3B (Qwen2.5-3B) and the token-modified Qwen2.5-3B (Qwen2.5-3B-filter).
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**Quickstart**
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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:
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```shell
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from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
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model_name = 'Henry94/Qwen2.5-3B-Character'
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype="auto", device_map="auto")
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prompt = "请简单介绍一下大型语言模型."
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messages = [
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{"role": "system", "content": "You are Qwen, created by Alibaba Cloud. You are a helpful assistant."},
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{"role": "user", "content": prompt}
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]
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text = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True
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)
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model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
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generated_ids = model.generate(
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**model_inputs,
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max_new_tokens=512
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)
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generated_ids = [
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output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
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]
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response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
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print(response)
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```
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