Instructions to use ethzanalytics/gpt-j-8bit-daily_dialogues with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ethzanalytics/gpt-j-8bit-daily_dialogues with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="ethzanalytics/gpt-j-8bit-daily_dialogues")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ethzanalytics/gpt-j-8bit-daily_dialogues")
model = AutoModelForCausalLM.from_pretrained("ethzanalytics/gpt-j-8bit-daily_dialogues")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ethzanalytics/gpt-j-8bit-daily_dialogues with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ethzanalytics/gpt-j-8bit-daily_dialogues"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ethzanalytics/gpt-j-8bit-daily_dialogues",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ethzanalytics/gpt-j-8bit-daily_dialogues

SGLang

How to use ethzanalytics/gpt-j-8bit-daily_dialogues with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "ethzanalytics/gpt-j-8bit-daily_dialogues" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ethzanalytics/gpt-j-8bit-daily_dialogues",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "ethzanalytics/gpt-j-8bit-daily_dialogues" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ethzanalytics/gpt-j-8bit-daily_dialogues",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ethzanalytics/gpt-j-8bit-daily_dialogues with Docker Model Runner:
```
docker model run hf.co/ethzanalytics/gpt-j-8bit-daily_dialogues
```

ethzanalytics/gpt-j-8bit-daily_dialogues

This version of hivemind/gpt-j-6B-8bit is fine-tuned on a parsed version of the daily dialogues dataset for an epoch. It can be used as a chatbot.

It is designed to be used with ai-msgbot to take advantage of prompt engineering in fine-tuning.

Usage

NOTE: this needs to be loaded via the special patching technique outlined in the hivemind model card (as with all 8bit models)

Examples of how to load the model correctly are already in place in the notebook linked above. A .py of said notebook was uploaded to the repo for reference - link here

Training

For details, please see this wandb report for both the daily-dialogues version and the WoW version.

Downloads last month: 17

Safetensors

Model size

6B params

Tensor type

F32

ethzanalytics
/

gpt-j-8bit-daily_dialogues

ethzanalytics/gpt-j-8bit-daily_dialogues

Usage

Training

Dataset used to train ethzanalytics/gpt-j-8bit-daily_dialogues