Instructions to use bigscience/distill-bloom-1b3-10x with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bigscience/distill-bloom-1b3-10x with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="bigscience/distill-bloom-1b3-10x")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("bigscience/distill-bloom-1b3-10x") model = AutoModelForCausalLM.from_pretrained("bigscience/distill-bloom-1b3-10x", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use bigscience/distill-bloom-1b3-10x with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "bigscience/distill-bloom-1b3-10x" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "bigscience/distill-bloom-1b3-10x", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/bigscience/distill-bloom-1b3-10x
- SGLang
How to use bigscience/distill-bloom-1b3-10x 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/distill-bloom-1b3-10x" \ --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/distill-bloom-1b3-10x", "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/distill-bloom-1b3-10x" \ --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/distill-bloom-1b3-10x", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use bigscience/distill-bloom-1b3-10x with Docker Model Runner:
docker model run hf.co/bigscience/distill-bloom-1b3-10x
| { | |
| "apply_residual_connection_post_layernorm": false, | |
| "architectures": [ | |
| "BloomForCausalLM" | |
| ], | |
| "attention_dropout": 0.0, | |
| "attention_softmax_in_fp32": true, | |
| "bias_dropout_fusion": true, | |
| "bos_token_id": 1, | |
| "depth_downsampling_rate": 0.5, | |
| "eos_token_id": 2, | |
| "hidden_dropout": 0.0, | |
| "initializer_range": 0.02, | |
| "layer_norm_epsilon": 1e-05, | |
| "layer_selection_strategy": "step", | |
| "masked_softmax_fusion": true, | |
| "model_type": "bloom", | |
| "n_embed": 512, | |
| "n_inner": null, | |
| "n_layer": 12, | |
| "num_attention_heads": 4, | |
| "offset_alibi": 100, | |
| "pad_token_id": 3, | |
| "pretraining_tp": 2, | |
| "seq_length": 4096, | |
| "skip_bias_add": true, | |
| "skip_bias_add_qkv": false, | |
| "slow_but_exact": false, | |
| "transformers_version": "4.21.0.dev0", | |
| "unk_token_id": 0, | |
| "use_cache": true, | |
| "vocab_size": 250880, | |
| "weights_aggregation_strategy": "mean", | |
| "width_downsampling_rate": 0.25 | |
| } |