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 ·
aee8c40
1
Parent(s): d9bf58e
Correct HumanEval scores (#79)
Browse files- Update README.md (5c5d5cf4a2e4c59287a69f8de35f2e9225df527a)
README.md
CHANGED
|
@@ -1754,15 +1754,15 @@ model-index:
|
|
| 1754 |
metrics:
|
| 1755 |
- name: pass@1
|
| 1756 |
type: pass@1
|
| 1757 |
-
value: 0.
|
| 1758 |
verified: false
|
| 1759 |
- name: pass@10
|
| 1760 |
type: pass@10
|
| 1761 |
-
value: 0.
|
| 1762 |
verified: false
|
| 1763 |
- name: pass@100
|
| 1764 |
type: pass@100
|
| 1765 |
-
value: 0.
|
| 1766 |
verified: false
|
| 1767 |
---
|
| 1768 |
|
|
@@ -2338,8 +2338,8 @@ See this repository for JSON files: https://github.com/bigscience-workshop/evalu
|
|
| 2338 |
| wnli (Median of 6 prompts) | eng | acc ↑ | 0.57 | 0.563 |
|
| 2339 |
| wsc (Median of 11 prompts) | eng | acc ↑ | 0.519 | 0.413 |
|
| 2340 |
| humaneval | python | pass@1 ↑ | 0.155 | 0.0 |
|
| 2341 |
-
| humaneval | python | pass@10 ↑ | 0.
|
| 2342 |
-
| humaneval | python | pass@100 ↑ | 0.
|
| 2343 |
|
| 2344 |
|
| 2345 |
**Train-time Evaluation:**
|
|
|
|
| 1754 |
metrics:
|
| 1755 |
- name: pass@1
|
| 1756 |
type: pass@1
|
| 1757 |
+
value: 0.15542682926829265
|
| 1758 |
verified: false
|
| 1759 |
- name: pass@10
|
| 1760 |
type: pass@10
|
| 1761 |
+
value: 0.3278356276947017
|
| 1762 |
verified: false
|
| 1763 |
- name: pass@100
|
| 1764 |
type: pass@100
|
| 1765 |
+
value: 0.5719815685597749
|
| 1766 |
verified: false
|
| 1767 |
---
|
| 1768 |
|
|
|
|
| 2338 |
| wnli (Median of 6 prompts) | eng | acc ↑ | 0.57 | 0.563 |
|
| 2339 |
| wsc (Median of 11 prompts) | eng | acc ↑ | 0.519 | 0.413 |
|
| 2340 |
| humaneval | python | pass@1 ↑ | 0.155 | 0.0 |
|
| 2341 |
+
| humaneval | python | pass@10 ↑ | 0.328 | 0.0 |
|
| 2342 |
+
| humaneval | python | pass@100 ↑ | 0.572 | 0.003 |
|
| 2343 |
|
| 2344 |
|
| 2345 |
**Train-time Evaluation:**
|