Instructions to use iamplus/bloomz-7b1-stanford-alpaca-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use iamplus/bloomz-7b1-stanford-alpaca-v1 with Transformers:

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

pipe = pipeline("text-generation", model="iamplus/bloomz-7b1-stanford-alpaca-v1")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("iamplus/bloomz-7b1-stanford-alpaca-v1")
model = AutoModelForCausalLM.from_pretrained("iamplus/bloomz-7b1-stanford-alpaca-v1", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use iamplus/bloomz-7b1-stanford-alpaca-v1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "iamplus/bloomz-7b1-stanford-alpaca-v1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "iamplus/bloomz-7b1-stanford-alpaca-v1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/iamplus/bloomz-7b1-stanford-alpaca-v1

SGLang

How to use iamplus/bloomz-7b1-stanford-alpaca-v1 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 "iamplus/bloomz-7b1-stanford-alpaca-v1" \
    --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": "iamplus/bloomz-7b1-stanford-alpaca-v1",
		"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 "iamplus/bloomz-7b1-stanford-alpaca-v1" \
        --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": "iamplus/bloomz-7b1-stanford-alpaca-v1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use iamplus/bloomz-7b1-stanford-alpaca-v1 with Docker Model Runner:
```
docker model run hf.co/iamplus/bloomz-7b1-stanford-alpaca-v1
```

First Version of Instruction Tuned Bloomz-7B1 model on Stanford Alpaca Instruction Tuning dataset (52k data) using HF Deepspeed

Base Model: bigscience/bloomz-7b1

Training Details :

Epochs: 4
Batch Size : 5 instantaneous per device x 3 gradient accumulation steps x 8 gpus = 120
Max Length : 1024
Weight Decay : 0
Learning Rate : 5e-5
Learning Rate Scheduler Type : Linear
Number of warmup steps : 40
Machine : 8xA100 80GB

Dataset Details :

Dataset : iamplus/Instruction_Tuning

Files :

stanford_alpaca_it.csv

Downloads last month: 18

iamplus
/

bloomz-7b1-stanford-alpaca-v1

Dataset used to train iamplus/bloomz-7b1-stanford-alpaca-v1