
**NOTE: This "delta model" cannot be used directly.**  
Users have to apply it on top of the original LLaMA weights to get actual Vicuna weights.  
See https://github.com/lm-sys/FastChat#vicuna-weights for instructions.
<br>
<br>

# Vicuna Model Card

## Model details

**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.
{"license": "apache-2.0", "inference": false}
transformers pytorch llama text-generation license:apache-2.0 autotrain_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #llama #text-generation #license-apache-2.0 #autotrain_compatible #text-generation-inference #region-us 


NOTE: This "delta model" cannot be used directly.  
Users have to apply it on top of the original LLaMA weights to get actual Vicuna weights.  
See URL for instructions.
<br>
<br>

# Vicuna Model Card

## Model details

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:
URL

License:
Apache License 2.0

Where to send questions or comments about the model:
URL

## 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 URL.

## 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 URL 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.
# Vicuna Model Card ## Model details

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:
URL

License:
Apache License 2.0

Where to send questions or comments about the model:
URL ## 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 URL. ## 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 URL 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.
TAGS
#transformers #pytorch #llama #text-generation #license-apache-2.0 #autotrain_compatible #text-generation-inference #region-us 
 # Vicuna Model Card ## Model details

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:
URL

License:
Apache License 2.0

Where to send questions or comments about the model:
URL ## 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 URL. ## 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 URL 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.
TAGS
#transformers #pytorch #llama #text-generation #license-apache-2.0 #autotrain_compatible #text-generation-inference #region-us 
# Vicuna Model Card## Model details

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:
URL

License:
Apache License 2.0

Where to send questions or comments about the model:
URL## 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 URL.## 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 URL 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.