Instructions to use joaoalvarenga/bloom-8bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use joaoalvarenga/bloom-8bit with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="joaoalvarenga/bloom-8bit")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("joaoalvarenga/bloom-8bit") model = AutoModelForCausalLM.from_pretrained("joaoalvarenga/bloom-8bit") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use joaoalvarenga/bloom-8bit with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "joaoalvarenga/bloom-8bit" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "joaoalvarenga/bloom-8bit", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/joaoalvarenga/bloom-8bit
- SGLang
How to use joaoalvarenga/bloom-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 "joaoalvarenga/bloom-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": "joaoalvarenga/bloom-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 "joaoalvarenga/bloom-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": "joaoalvarenga/bloom-8bit", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use joaoalvarenga/bloom-8bit with Docker Model Runner:
docker model run hf.co/joaoalvarenga/bloom-8bit
Commit ·
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README.md
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@@ -59,7 +59,7 @@ Here, we also apply [LoRA (Low Rank Adpatars](https://arxiv.org/abs/2106.09685)
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### How to use
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This model can be used by adapting Bloom original implementation:
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```python
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import transformers
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prompt = tokenizer("Given a table named salaries and columns id, created_at, salary, age. Creates a SQL to answer What is the average salary for 22 years old:", return_tensors='pt')
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out = model.generate(**prompt, min_length=10, do_sample=True)
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tokenizer.decode(out[0])
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### How to use
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This model can be used by adapting Bloom original implementation. This is an adaptation from [Hivemind's GPT-J 8-bit](https://nbviewer.org/urls/huggingface.co/hivemind/gpt-j-6B-8bit/raw/main/convert-gpt-j.ipynb):
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```python
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import transformers
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prompt = tokenizer("Given a table named salaries and columns id, created_at, salary, age. Creates a SQL to answer What is the average salary for 22 years old:", return_tensors='pt')
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out = model.generate(**prompt, min_length=10, do_sample=True)
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tokenizer.decode(out[0])
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```
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