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
Commit ·
b07b3ae
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Parent(s): f4d13b5
eng -> fra for crows_pairs_french
Browse files
README.md
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@@ -2153,7 +2153,7 @@ See this repository for JSON files: https://github.com/bigscience-workshop/evalu
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| 2153 |
| cola (Median of 5 prompts) | eng | acc ↑ | 0.39 | 0.444 |
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| 2154 |
| copa (Median of 9 prompts) | eng | acc ↑ | 0.56 | 0.55 |
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| 2155 |
| crows_pairs_english (Median of 6 prompts) | eng | acc ↑ | 0.5 | 0.502 |
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| 2156 |
-
| crows_pairs_french (Median of 7 prompts) |
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| 2157 |
| diabla (Median of 2 prompts) | eng | acc ↑ | 0.295 | 0.289 |
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| 2158 |
| gsarti/flores_101_afr | afr | byte_perplexity ↓ | 4.254 | 3.381 |
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| 2159 |
| gsarti/flores_101_amh | amh | byte_perplexity ↓ | 3.717 | 3.87 |
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| 2153 |
| cola (Median of 5 prompts) | eng | acc ↑ | 0.39 | 0.444 |
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| 2154 |
| copa (Median of 9 prompts) | eng | acc ↑ | 0.56 | 0.55 |
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| 2155 |
| crows_pairs_english (Median of 6 prompts) | eng | acc ↑ | 0.5 | 0.502 |
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| 2156 |
+
| crows_pairs_french (Median of 7 prompts) | fra | acc ↑ | 0.506 | 0.499 |
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| 2157 |
| diabla (Median of 2 prompts) | eng | acc ↑ | 0.295 | 0.289 |
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| 2158 |
| gsarti/flores_101_afr | afr | byte_perplexity ↓ | 4.254 | 3.381 |
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| 2159 |
| gsarti/flores_101_amh | amh | byte_perplexity ↓ | 3.717 | 3.87 |
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