Instructions to use HamadML/bloomz-560m_p_low_learning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use HamadML/bloomz-560m_p_low_learning with Transformers:

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

pipe = pipeline("text-generation", model="HamadML/bloomz-560m_p_low_learning")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("HamadML/bloomz-560m_p_low_learning")
model = AutoModelForCausalLM.from_pretrained("HamadML/bloomz-560m_p_low_learning")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use HamadML/bloomz-560m_p_low_learning with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "HamadML/bloomz-560m_p_low_learning"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "HamadML/bloomz-560m_p_low_learning",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/HamadML/bloomz-560m_p_low_learning

SGLang

How to use HamadML/bloomz-560m_p_low_learning 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 "HamadML/bloomz-560m_p_low_learning" \
    --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": "HamadML/bloomz-560m_p_low_learning",
		"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 "HamadML/bloomz-560m_p_low_learning" \
        --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": "HamadML/bloomz-560m_p_low_learning",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use HamadML/bloomz-560m_p_low_learning with Docker Model Runner:
```
docker model run hf.co/HamadML/bloomz-560m_p_low_learning
```

Adding `safetensors` variant of this model

by SFconvertbot - opened Feb 3, 2025

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

-0

SFconvertbot

Feb 3, 2025

This is an automated PR created with https://huggingface.co/spaces/safetensors/convert

This new file is equivalent to pytorch_model.bin but safe in the sense that
no arbitrary code can be put into it.

These files also happen to load much faster than their pytorch counterpart:
https://colab.research.google.com/github/huggingface/notebooks/blob/main/safetensors_doc/en/speed.ipynb

The widgets on your model page will run using this model even if this is not merged
making sure the file actually works.

If you find any issues: please report here: https://huggingface.co/spaces/safetensors/convert/discussions

Feel free to ignore this PR.

Adding `safetensors` variant of this model1d6f7fe2

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