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Model: AI-ModelScope/Vicuna-7B
Source: Original Platform

README.md

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+---
+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.