85 lines
3.0 KiB
Markdown
85 lines
3.0 KiB
Markdown
---
|
|
license: other
|
|
license_name: qwen
|
|
license_link: >-
|
|
https://github.com/QwenLM/Qwen/blob/main/Tongyi%20Qianwen%20LICENSE%20AGREEMENT
|
|
language:
|
|
- en
|
|
- zh
|
|
library_name: transformers
|
|
pipeline_tag: text-generation
|
|
inference: false
|
|
tags:
|
|
- mistral
|
|
- qwen
|
|
- qwen1.5
|
|
- qwen2
|
|
---
|
|
This is the Mistral version of [Qwen1.5-0.5B-Chat](https://huggingface.co/Qwen/Qwen1.5-0.5B-Chat) model by Alibaba Cloud.
|
|
The original codebase can be found at: (https://github.com/hiyouga/LLaMA-Factory/blob/main/tests/llamafy_qwen.py).
|
|
I have made modifications to make it compatible with qwen1.5.
|
|
This model is converted with https://github.com/Minami-su/character_AI_open/blob/main/mistral_qwen2.py
|
|
|
|
## special
|
|
|
|
1.Before using this model, you need to modify modeling_mistral.py in transformers library
|
|
|
|
2.vim /root/anaconda3/envs/train/lib/python3.9/site-packages/transformers/models/mistral/modeling_mistral.py
|
|
|
|
3.find MistralAttention,
|
|
|
|
4.modify q,k,v,o bias=False ----->, bias=config.attention_bias
|
|
|
|
Before:
|
|
|
|
After:
|
|
|
|
|
|
|
|
## Differences between qwen2 mistral and qwen2 llamafy
|
|
|
|
Compared to qwen2 llamafy,qwen2 mistral can use sliding window attention,qwen2 mistral is faster than qwen2 llamafy, and the context length is better
|
|
|
|
|
|
Usage:
|
|
|
|
```python
|
|
|
|
from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
|
|
tokenizer = AutoTokenizer.from_pretrained("Minami-su/Qwen1.5-0.5B-Chat_mistral")
|
|
model = AutoModelForCausalLM.from_pretrained("Minami-su/Qwen1.5-0.5B-Chat_mistral", torch_dtype="auto", device_map="auto")
|
|
streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
|
|
|
|
messages = [
|
|
{"role": "user", "content": "Who are you?"}
|
|
]
|
|
inputs = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt")
|
|
inputs = inputs.to("cuda")
|
|
generate_ids = model.generate(inputs,max_length=2048, streamer=streamer)
|
|
|
|
```
|
|
|
|
## Test
|
|
load in 4bit
|
|
```
|
|
hf-causal (pretrained=Qwen1.5-0.5B-Chat), limit: None, provide_description: False, num_fewshot: 0, batch_size: 32
|
|
| Task |Version| Metric |Value | |Stderr|
|
|
|-------------|------:|--------|-----:|---|-----:|
|
|
|arc_challenge| 0|acc |0.2389|± |0.0125|
|
|
| | |acc_norm|0.2688|± |0.0130|
|
|
|truthfulqa_mc| 1|mc1 |0.2534|± |0.0152|
|
|
| | |mc2 |0.4322|± |0.0151|
|
|
|winogrande | 0|acc |0.5564|± |0.0140|
|
|
```
|
|
load in 4bit
|
|
```
|
|
hf-causal (pretrained=Qwen1.5-0.5B-Chat_mistral), limit: None, provide_description: False, num_fewshot: 0, batch_size: 32
|
|
| Task |Version| Metric |Value | |Stderr|
|
|
|-------------|------:|--------|-----:|---|-----:|
|
|
|arc_challenge| 0|acc |0.2398|± |0.0125|
|
|
| | |acc_norm|0.2705|± |0.0130|
|
|
|truthfulqa_mc| 1|mc1 |0.2534|± |0.0152|
|
|
| | |mc2 |0.4322|± |0.0151|
|
|
|winogrande | 0|acc |0.5549|± |0.0140|
|
|
```
|
|
``` |