Text Generation
Transformers
PyTorch
TensorBoard
Safetensors
bloom
Eval Results (legacy)
text-generation-inference
Instructions to use bigscience/bloom with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bigscience/bloom with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="bigscience/bloom")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("bigscience/bloom") model = AutoModelForCausalLM.from_pretrained("bigscience/bloom") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use bigscience/bloom with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "bigscience/bloom" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "bigscience/bloom", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/bigscience/bloom
- SGLang
How to use bigscience/bloom 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 "bigscience/bloom" \ --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": "bigscience/bloom", "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 "bigscience/bloom" \ --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": "bigscience/bloom", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use bigscience/bloom with Docker Model Runner:
docker model run hf.co/bigscience/bloom
Correct number of languages (#24)
Browse files- Correct number of languages (69e78440cdbd55e8588883927a460cd8aa222c24)
- Update README.md (0de6009ab9827a696a1d2fb1fcdedeb21088c6c0)
Co-authored-by: Christopher Akiki <cakiki@users.noreply.huggingface.co>
README.md
CHANGED
|
@@ -179,11 +179,11 @@ Details for each dataset are provided in individual [Data Cards](https://hugging
|
|
| 179 |
|
| 180 |
Training data includes:
|
| 181 |
|
| 182 |
-
-
|
| 183 |
|
| 184 |
-
-
|
| 185 |
|
| 186 |
-
- In 1.
|
| 187 |
|
| 188 |
### Languages
|
| 189 |
|
|
|
|
| 179 |
|
| 180 |
Training data includes:
|
| 181 |
|
| 182 |
+
- 46 natural languages
|
| 183 |
|
| 184 |
+
- 13 programming languages
|
| 185 |
|
| 186 |
+
- In 1.6TB of pre-processed text, converted into 350B unique tokens (see [the tokenizer section](#tokenization) for more.)
|
| 187 |
|
| 188 |
### Languages
|
| 189 |
|