85 lines
2.4 KiB
Markdown
85 lines
2.4 KiB
Markdown
---
|
|
license: Apache License 2.0
|
|
tasks:
|
|
- text-generation
|
|
language:
|
|
- en
|
|
library_name: transformers
|
|
inference: false
|
|
widgets:
|
|
- task: text-generation
|
|
version: 1
|
|
inputs:
|
|
- type: text
|
|
name: text
|
|
title: 输入文字
|
|
validator:
|
|
max_words: 128
|
|
examples:
|
|
- name: 1
|
|
title: 示例1
|
|
inputs:
|
|
- name: text
|
|
data: 你好
|
|
inferencespec:
|
|
cpu: 4
|
|
memory: 24000
|
|
gpu: 1
|
|
gpu_memory: 16000
|
|
---
|
|
**NOTE: This model has delta files applied and can be used directly.**
|
|
|
|
|
|
# Vicuna Model Card
|
|
|
|
## Model details
|
|
|
|
```
|
|
pip install fschat
|
|
```
|
|
|
|
```python
|
|
from modelscope.utils.constant import Tasks
|
|
from modelscope.pipelines import pipeline
|
|
pipe = pipeline(task=Tasks.text_generation, model='AI-ModelScope/Vicuna-7B', model_revision='v1.0.1', device='cuda')
|
|
inputs = '你好'
|
|
result = pipe(inputs)
|
|
print(result)
|
|
|
|
```
|
|
|
|
**Model type:**
|
|
Vicuna is an open-source chatbot trained by fine-tuning LLaMA on user-shared conversations collected from ShareGPT.
|
|
It is an auto-regressive language model, based on the transformer architecture.
|
|
|
|
**Model date:**
|
|
Vicuna was trained between March 2023 and April 2023.
|
|
|
|
**Organizations developing the model:**
|
|
The Vicuna team with members from UC Berkeley, CMU, Stanford, and UC San Diego.
|
|
|
|
**Paper or resources for more information:**
|
|
https://vicuna.lmsys.org/
|
|
|
|
**License:**
|
|
Apache License 2.0
|
|
|
|
**Where to send questions or comments about the model:**
|
|
https://github.com/lm-sys/FastChat/issues
|
|
|
|
## Intended use
|
|
**Primary intended uses:**
|
|
The primary use of Vicuna is research on large language models and chatbots.
|
|
|
|
**Primary intended users:**
|
|
The primary intended users of the model are researchers and hobbyists in natural language processing, machine learning, and artificial intelligence.
|
|
|
|
## Training dataset
|
|
70K conversations collected from ShareGPT.com.
|
|
|
|
## Evaluation dataset
|
|
A preliminary evaluation of the model quality is conducted by creating a set of 80 diverse questions and utilizing GPT-4 to judge the model outputs. See https://vicuna.lmsys.org/ for more details.
|
|
|
|
## Major updates of weights v1.1
|
|
- Refactor the tokenization and separator. In Vicuna v1.1, the separator has been changed from `"###"` to the EOS token `"</s>"`. This change makes it easier to determine the generation stop criteria and enables better compatibility with other libraries.
|
|
- Fix the supervised fine-tuning loss computation for better model quality. |