Instructions to use ethzanalytics/dolly-v2-7b-sharded-8bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ethzanalytics/dolly-v2-7b-sharded-8bit with Transformers:

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

pipe = pipeline("text-generation", model="ethzanalytics/dolly-v2-7b-sharded-8bit")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ethzanalytics/dolly-v2-7b-sharded-8bit")
model = AutoModelForCausalLM.from_pretrained("ethzanalytics/dolly-v2-7b-sharded-8bit")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ethzanalytics/dolly-v2-7b-sharded-8bit with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ethzanalytics/dolly-v2-7b-sharded-8bit"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ethzanalytics/dolly-v2-7b-sharded-8bit",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ethzanalytics/dolly-v2-7b-sharded-8bit

SGLang

How to use ethzanalytics/dolly-v2-7b-sharded-8bit 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/dolly-v2-7b-sharded-8bit" \
    --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/dolly-v2-7b-sharded-8bit",
		"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/dolly-v2-7b-sharded-8bit" \
        --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/dolly-v2-7b-sharded-8bit",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ethzanalytics/dolly-v2-7b-sharded-8bit with Docker Model Runner:
```
docker model run hf.co/ethzanalytics/dolly-v2-7b-sharded-8bit
```

dolly-v2-7b: 8-bit sharded checkpoint

This is a sharded checkpoint (with ~2GB shards) of the databricks/dolly-v2-7b model in 8-bit precision using bitsandbytes.

Refer to the original model for all details. For more info on loading 8bit models, refer to the example repo and/or the 4.28.0 release info.

total model size is only ~7.5 GB!
this enables low-RAM loading, i.e. Colab :)

Basic Usage

install/upgrade transformers, accelerate, and bitsandbytes. For this to work you must have transformers>=4.28.0 and bitsandbytes>0.37.2.

pip install -U -q transformers bitsandbytes accelerate

Load the model. As it is serialized in 8bit you don't need to do anything special:

from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "ethzanalytics/dolly-v2-7b-sharded-8bit"
tokenizer = AutoTokenizer.from_pretrained(model_name)

model = AutoModelForCausalLM.from_pretrained(model_name)

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ethzanalytics
/

dolly-v2-7b-sharded-8bit

dolly-v2-7b: 8-bit sharded checkpoint

Basic Usage

Dataset used to train ethzanalytics/dolly-v2-7b-sharded-8bit