
# Model Card for GPT4All-J-LoRA

An Apache-2 licensed chatbot trained over a massive curated corpus of assistant interactions including word problems, multi-turn dialogue, code, poems, songs, and stories.

## Model Details

### Model Description

<!-- Provide a longer summary of what this model is. -->

This model has been finetuned from [GPT-J](https://huggingface.co/EleutherAI/gpt-j-6B)

- **Developed by:** [Nomic AI](https://home.nomic.ai)
- **Model Type:** A finetuned GPT-J model on assistant style interaction data
- **Language(s) (NLP):** English
- **License:** Apache-2
- **Finetuned from model [optional]:** [GPT-J](https://huggingface.co/EleutherAI/gpt-j-6B)

### Model Sources [optional]

<!-- Provide the basic links for the model. -->

- **Repository:** [https://github.com/nomic-ai/gpt4all](https://github.com/nomic-ai/gpt4all)
- **Base Model Repository:** [https://github.com/kingoflolz/mesh-transformer-jax](https://github.com/kingoflolz/mesh-transformer-jax)
- **Paper [optional]:** [GPT4All-J: An Apache-2 Licensed Assistant-Style Chatbot](https://s3.amazonaws.com/static.nomic.ai/gpt4all/2023_GPT4All-J_Technical_Report_2.pdf)
- **Demo [optional]:** [https://gpt4all.io/](https://gpt4all.io/)


### Training Procedure 
GPT4All is made possible by our compute partner [Paperspace](https://www.paperspace.com/).

Trained on a DGX cluster with 8 A100 80GB GPUs for ~12 hours. Using Deepspeed + Accelerate, we use a global batch size of 32 with a learning rate of 2e-5 using LoRA. More information can be found in the repo.


### Results

Results on common sense reasoning benchmarks

```
 Model                     BoolQ       PIQA     HellaSwag   WinoGrande    ARC-e      ARC-c       OBQA
  ----------------------- ---------- ---------- ----------- ------------ ---------- ---------- ----------
  GPT4All-J 6.7B             73.4       74.8       63.4         64.7        54.9       36.0       40.2
  GPT4All-J Lora 6.7B        68.6       75.8       66.2         63.5        56.4       35.7       40.2
  GPT4All LLaMa Lora 7B      73.1       77.6       72.1         67.8        51.1       40.4       40.2
  Dolly 6B                   68.8       77.3       67.6         63.9        62.9       38.7       41.2
  Dolly 12B                  56.7       75.4       71.0         62.2       *64.6*      38.5        40.4
  Alpaca 7B                  73.9       77.2       73.9         66.1        59.8       43.3       43.4
  Alpaca Lora 7B            *74.3*     *79.3*     *74.0*       *68.8*       56.6      *43.9*     *42.6*
  GPT-J 6.7B                 65.4       76.2       66.2         64.1        62.2       36.6       38.2
  LLaMa 7B                   73.1       77.4       73.0         66.9        52.5       41.4       42.4
  Pythia 6.7B                63.5       76.3       64.0         61.1        61.3       35.2       37.2
  Pythia 12B                 67.7       76.6       67.3         63.8        63.9       34.8        38
```
{"language": ["en"], "license": "apache-2.0", "datasets": ["nomic-ai/gpt4all-j-prompt-generations"], "pipeline_tag": "text-generation"}
text-generation en dataset:nomic-ai/gpt4all-j-prompt-generations license:apache-2.0 has_space region:us
TAGS
#text-generation #en #dataset-nomic-ai/gpt4all-j-prompt-generations #license-apache-2.0 #has_space #region-us 


# Model Card for GPT4All-J-LoRA

An Apache-2 licensed chatbot trained over a massive curated corpus of assistant interactions including word problems, multi-turn dialogue, code, poems, songs, and stories.

## Model Details

### Model Description



This model has been finetuned from GPT-J

- Developed by: Nomic AI
- Model Type: A finetuned GPT-J model on assistant style interaction data
- Language(s) (NLP): English
- License: Apache-2
- Finetuned from model [optional]: GPT-J

### Model Sources [optional]



- Repository: URL
- Base Model Repository: URL
- Paper [optional]: GPT4All-J: An Apache-2 Licensed Assistant-Style Chatbot
- Demo [optional]: URL


### Training Procedure 
GPT4All is made possible by our compute partner Paperspace.

Trained on a DGX cluster with 8 A100 80GB GPUs for ~12 hours. Using Deepspeed + Accelerate, we use a global batch size of 32 with a learning rate of 2e-5 using LoRA. More information can be found in the repo.


### Results

Results on common sense reasoning benchmarks


# Model Card for GPT4All-J-LoRA

An Apache-2 licensed chatbot trained over a massive curated corpus of assistant interactions including word problems, multi-turn dialogue, code, poems, songs, and stories. ## Model Details ### Model Description



This model has been finetuned from GPT-J

- Developed by: Nomic AI
- Model Type: A finetuned GPT-J model on assistant style interaction data
- Language(s) (NLP): English
- License: Apache-2
- Finetuned from model [optional]: GPT-J ### Model Sources [optional]



- Repository: URL
- Base Model Repository: URL
- Paper [optional]: GPT4All-J: An Apache-2 Licensed Assistant-Style Chatbot
- Demo [optional]: URL ### Training Procedure 
GPT4All is made possible by our compute partner Paperspace.

Trained on a DGX cluster with 8 A100 80GB GPUs for ~12 hours. Using Deepspeed + Accelerate, we use a global batch size of 32 with a learning rate of 2e-5 using LoRA. More information can be found in the repo. ### Results

Results on common sense reasoning benchmarks
TAGS
#text-generation #en #dataset-nomic-ai/gpt4all-j-prompt-generations #license-apache-2.0 #has_space #region-us 
 # Model Card for GPT4All-J-LoRA

An Apache-2 licensed chatbot trained over a massive curated corpus of assistant interactions including word problems, multi-turn dialogue, code, poems, songs, and stories. ## Model Details ### Model Description



This model has been finetuned from GPT-J

- Developed by: Nomic AI
- Model Type: A finetuned GPT-J model on assistant style interaction data
- Language(s) (NLP): English
- License: Apache-2
- Finetuned from model [optional]: GPT-J ### Model Sources [optional]



- Repository: URL
- Base Model Repository: URL
- Paper [optional]: GPT4All-J: An Apache-2 Licensed Assistant-Style Chatbot
- Demo [optional]: URL ### Training Procedure 
GPT4All is made possible by our compute partner Paperspace.

Trained on a DGX cluster with 8 A100 80GB GPUs for ~12 hours. Using Deepspeed + Accelerate, we use a global batch size of 32 with a learning rate of 2e-5 using LoRA. More information can be found in the repo. ### Results

Results on common sense reasoning benchmarks
TAGS
#text-generation #en #dataset-nomic-ai/gpt4all-j-prompt-generations #license-apache-2.0 #has_space #region-us 
# Model Card for GPT4All-J-LoRA

An Apache-2 licensed chatbot trained over a massive curated corpus of assistant interactions including word problems, multi-turn dialogue, code, poems, songs, and stories.## Model Details### Model Description



This model has been finetuned from GPT-J

- Developed by: Nomic AI
- Model Type: A finetuned GPT-J model on assistant style interaction data
- Language(s) (NLP): English
- License: Apache-2
- Finetuned from model [optional]: GPT-J### Model Sources [optional]



- Repository: URL
- Base Model Repository: URL
- Paper [optional]: GPT4All-J: An Apache-2 Licensed Assistant-Style Chatbot
- Demo [optional]: URL### Training Procedure 
GPT4All is made possible by our compute partner Paperspace.

Trained on a DGX cluster with 8 A100 80GB GPUs for ~12 hours. Using Deepspeed + Accelerate, we use a global batch size of 32 with a learning rate of 2e-5 using LoRA. More information can be found in the repo.### Results

Results on common sense reasoning benchmarks