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 Settings
- 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 ·
5ebccd1
1
Parent(s): d5d729d
Update training statistics
Browse files
README.md
CHANGED
|
@@ -2319,14 +2319,15 @@ See this repository for JSON files: https://github.com/bigscience-workshop/evalu
|
|
| 2319 |
|
| 2320 |
**Train-time Evaluation:**
|
| 2321 |
|
| 2322 |
-
|
| 2323 |
|
| 2324 |
-
- Training Loss:
|
| 2325 |
|
| 2326 |
-
- Validation Loss: 2.
|
| 2327 |
|
| 2328 |
-
- Perplexity:
|
| 2329 |
|
|
|
|
| 2330 |
|
| 2331 |
</details>
|
| 2332 |
|
|
|
|
| 2319 |
|
| 2320 |
**Train-time Evaluation:**
|
| 2321 |
|
| 2322 |
+
Final checkpoint after 95K steps:
|
| 2323 |
|
| 2324 |
+
- Training Loss: 1.939
|
| 2325 |
|
| 2326 |
+
- Validation Loss: 2.061
|
| 2327 |
|
| 2328 |
+
- Perplexity: 7.045
|
| 2329 |
|
| 2330 |
+
For more see: https://huggingface.co/bigscience/tr11-176B-ml-logs
|
| 2331 |
|
| 2332 |
</details>
|
| 2333 |
|