Instructions to use hllj/BloomZ-7B1-Vi-Math with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hllj/BloomZ-7B1-Vi-Math with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="hllj/BloomZ-7B1-Vi-Math")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("hllj/BloomZ-7B1-Vi-Math") model = AutoModelForCausalLM.from_pretrained("hllj/BloomZ-7B1-Vi-Math") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use hllj/BloomZ-7B1-Vi-Math with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "hllj/BloomZ-7B1-Vi-Math" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "hllj/BloomZ-7B1-Vi-Math", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/hllj/BloomZ-7B1-Vi-Math
- SGLang
How to use hllj/BloomZ-7B1-Vi-Math 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 "hllj/BloomZ-7B1-Vi-Math" \ --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": "hllj/BloomZ-7B1-Vi-Math", "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 "hllj/BloomZ-7B1-Vi-Math" \ --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": "hllj/BloomZ-7B1-Vi-Math", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use hllj/BloomZ-7B1-Vi-Math with Docker Model Runner:
docker model run hf.co/hllj/BloomZ-7B1-Vi-Math
| { | |
| "_name_or_path": "bigscience/bloomz-7b1", | |
| "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, | |
| "eos_token_id": 2, | |
| "hidden_dropout": 0.0, | |
| "hidden_size": 4096, | |
| "initializer_range": 0.02, | |
| "layer_norm_epsilon": 1e-05, | |
| "masked_softmax_fusion": true, | |
| "model_type": "bloom", | |
| "n_head": 32, | |
| "n_inner": null, | |
| "n_layer": 30, | |
| "offset_alibi": 100, | |
| "pad_token_id": 3, | |
| "pretraining_tp": 1, | |
| "seq_length": 2048, | |
| "skip_bias_add": true, | |
| "skip_bias_add_qkv": false, | |
| "slow_but_exact": false, | |
| "torch_dtype": "float16", | |
| "transformers_version": "4.36.2", | |
| "unk_token_id": 0, | |
| "use_cache": true, | |
| "vocab_size": 250880 | |
| } | |