Text Generation
Transformers
PyTorch
Safetensors
multilingual
bloom
generation
question answering
instruction tuning
text-generation-inference
Instructions to use MaLA-LM/lucky52-bloom-7b1-no-47 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MaLA-LM/lucky52-bloom-7b1-no-47 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="MaLA-LM/lucky52-bloom-7b1-no-47")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("MaLA-LM/lucky52-bloom-7b1-no-47") model = AutoModelForCausalLM.from_pretrained("MaLA-LM/lucky52-bloom-7b1-no-47") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use MaLA-LM/lucky52-bloom-7b1-no-47 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "MaLA-LM/lucky52-bloom-7b1-no-47" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "MaLA-LM/lucky52-bloom-7b1-no-47", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/MaLA-LM/lucky52-bloom-7b1-no-47
- SGLang
How to use MaLA-LM/lucky52-bloom-7b1-no-47 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 "MaLA-LM/lucky52-bloom-7b1-no-47" \ --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": "MaLA-LM/lucky52-bloom-7b1-no-47", "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 "MaLA-LM/lucky52-bloom-7b1-no-47" \ --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": "MaLA-LM/lucky52-bloom-7b1-no-47", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use MaLA-LM/lucky52-bloom-7b1-no-47 with Docker Model Runner:
docker model run hf.co/MaLA-LM/lucky52-bloom-7b1-no-47
File size: 789 Bytes
e7a7fce | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 | {
"_name_or_path": "bigscience/bloom-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,
"skip_bias_add": true,
"skip_bias_add_qkv": false,
"slow_but_exact": false,
"torch_dtype": "float16",
"transformers_version": "4.34.0.dev0",
"unk_token_id": 0,
"use_cache": false,
"vocab_size": 250880
}
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