Text Generation
Transformers
PyTorch
TensorBoard
Safetensors
bloom
Eval Results (legacy)
text-generation-inference
Instructions to use bigscience/bloomz with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bigscience/bloomz with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="bigscience/bloomz")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("bigscience/bloomz") model = AutoModelForCausalLM.from_pretrained("bigscience/bloomz") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use bigscience/bloomz with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "bigscience/bloomz" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "bigscience/bloomz", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/bigscience/bloomz
- SGLang
How to use bigscience/bloomz 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/bloomz" \ --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/bloomz", "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/bloomz" \ --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/bloomz", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use bigscience/bloomz with Docker Model Runner:
docker model run hf.co/bigscience/bloomz
Commit ·
29e57a5
1
Parent(s): f8d4850
Update README.md
Browse files
README.md
CHANGED
|
@@ -130,9 +130,6 @@ widget:
|
|
| 130 |
|----|-----------|
|
| 131 |
|
| 132 |
|
| 133 |
-
|
| 134 |
-
|
| 135 |
-
|
| 136 |
# Intended uses
|
| 137 |
|
| 138 |
You can use the models to perform inference on tasks by specifying your query in natural language, and the models will generate a prediction. For instance, you can ask *"Translate this to Chinese: Je t'aime."*, and the model will hopefully generate *"我爱你"*.
|
|
@@ -140,6 +137,8 @@ You can use the models to perform inference on tasks by specifying your query in
|
|
| 140 |
# How to use
|
| 141 |
|
| 142 |
Here is how to use the model in PyTorch:
|
|
|
|
|
|
|
| 143 |
```python
|
| 144 |
from transformers import AutoTokenizer, AutoModelForCausalLM
|
| 145 |
|
|
|
|
| 130 |
|----|-----------|
|
| 131 |
|
| 132 |
|
|
|
|
|
|
|
|
|
|
| 133 |
# Intended uses
|
| 134 |
|
| 135 |
You can use the models to perform inference on tasks by specifying your query in natural language, and the models will generate a prediction. For instance, you can ask *"Translate this to Chinese: Je t'aime."*, and the model will hopefully generate *"我爱你"*.
|
|
|
|
| 137 |
# How to use
|
| 138 |
|
| 139 |
Here is how to use the model in PyTorch:
|
| 140 |
+
|
| 141 |
+
TODO: Better code with auto-precision?
|
| 142 |
```python
|
| 143 |
from transformers import AutoTokenizer, AutoModelForCausalLM
|
| 144 |
|