Text Generation
Transformers
PyTorch
Safetensors
bloom
Eval Results (legacy)
text-generation-inference
Instructions to use bigscience/bloomz-7b1-p3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bigscience/bloomz-7b1-p3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="bigscience/bloomz-7b1-p3")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("bigscience/bloomz-7b1-p3") model = AutoModelForCausalLM.from_pretrained("bigscience/bloomz-7b1-p3") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use bigscience/bloomz-7b1-p3 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "bigscience/bloomz-7b1-p3" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "bigscience/bloomz-7b1-p3", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/bigscience/bloomz-7b1-p3
- SGLang
How to use bigscience/bloomz-7b1-p3 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/bloomz-7b1-p3" \ --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/bloomz-7b1-p3", "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/bloomz-7b1-p3" \ --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/bloomz-7b1-p3", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use bigscience/bloomz-7b1-p3 with Docker Model Runner:
docker model run hf.co/bigscience/bloomz-7b1-p3
Commit ·
ad49413
1
Parent(s): 8634ce5
Add res
Browse files- evaluation_bloomz-7b1-p3/evaluation_l2/Muennighoff_xwinograd/jp/Replace/results.json +9 -0
- evaluation_bloomz-7b1-p3/evaluation_l2/Muennighoff_xwinograd/jp/True_or_False/results.json +2 -2
- evaluation_bloomz-7b1-p3/evaluation_l2/Muennighoff_xwinograd/jp/does_underscore_refer_to/results.json +9 -0
- evaluation_bloomz-7b1-p3/evaluation_l2/Muennighoff_xwinograd/jp/stand_for/results.json +9 -0
- evaluation_bloomz-7b1-p3/evaluation_l2/Muennighoff_xwinograd/jp/underscore_refer_to/results.json +9 -0
- evaluation_bloomz-7b1-p3/evaluation_val/examples.limited=3000.model=p31lossseqglobal_step1000.task=wmt14_hi_en.templates=gpt3-en-hi-target.fewshot=0.batchsize=16.seed=1234.timestamp=2022-09-09T23:48:38.jsonl +0 -0
- evaluation_bloomz-7b1-p3/evaluation_val/examples.limited=3000.model=p31lossseqglobal_step1000.task=wmt14_hi_en.templates=gpt3-hi-en-target.fewshot=0.batchsize=16.seed=1234.timestamp=2022-09-09T23:48:40.jsonl +0 -0
evaluation_bloomz-7b1-p3/evaluation_l2/Muennighoff_xwinograd/jp/Replace/results.json
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"dataset_name": "Muennighoff/xwinograd",
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"dataset_config_name": "jp",
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"template_name": "Replace",
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"evaluation": {
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"accuracy": 0.5130344108446299
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"arguments": "Namespace(config_name=None, dataset_config_name='jp', dataset_name='Muennighoff/xwinograd', debug=False, dtype='float16', max_length=2048, model_name_or_path='/gpfsscratch/rech/six/commun/experiments/muennighoff/bloomckpt/6b3t0/bloomz-7b1-p3', nospace=False, output_dir='/gpfsscratch/rech/six/commun/experiments/muennighoff/bloomckpt/6b3t0/bloomz-7b1-p3/evaluation', pad_to_max_length=False, per_device_eval_batch_size=4, prefixlm=False, split='test', target_max_length=256, template_config_name='en', template_name='Replace', tokenizer_name=None, use_slow_tokenizer=False)"
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evaluation_bloomz-7b1-p3/evaluation_l2/Muennighoff_xwinograd/jp/True_or_False/results.json
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"dataset_config_name": "jp",
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"accuracy": 0.
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"arguments": "Namespace(config_name=None, dataset_config_name='jp', dataset_name='Muennighoff/xwinograd', debug=False, dtype='float16', max_length=2048, model_name_or_path='/gpfsscratch/rech/six/commun/experiments/muennighoff/bloomckpt/6b3t0/bloomz-7b1-p3', nospace=False, output_dir='/gpfsscratch/rech/six/commun/experiments/muennighoff/bloomckpt/6b3t0/bloomz-7b1-p3/evaluation', pad_to_max_length=False, per_device_eval_batch_size=
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"dataset_config_name": "jp",
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"template_name": "True or False",
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"accuracy": 0.5036496350364964
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"arguments": "Namespace(config_name=None, dataset_config_name='jp', dataset_name='Muennighoff/xwinograd', debug=False, dtype='float16', max_length=2048, model_name_or_path='/gpfsscratch/rech/six/commun/experiments/muennighoff/bloomckpt/6b3t0/bloomz-7b1-p3', nospace=False, output_dir='/gpfsscratch/rech/six/commun/experiments/muennighoff/bloomckpt/6b3t0/bloomz-7b1-p3/evaluation', pad_to_max_length=False, per_device_eval_batch_size=4, prefixlm=False, split='test', target_max_length=256, template_config_name='en', template_name='True or False', tokenizer_name=None, use_slow_tokenizer=False)"
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evaluation_bloomz-7b1-p3/evaluation_l2/Muennighoff_xwinograd/jp/does_underscore_refer_to/results.json
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"dataset_name": "Muennighoff/xwinograd",
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"dataset_config_name": "jp",
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"template_name": "does underscore refer to",
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"evaluation": {
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"accuracy": 0.5192909280500522
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"arguments": "Namespace(config_name=None, dataset_config_name='jp', dataset_name='Muennighoff/xwinograd', debug=False, dtype='float16', max_length=2048, model_name_or_path='/gpfsscratch/rech/six/commun/experiments/muennighoff/bloomckpt/6b3t0/bloomz-7b1-p3', nospace=False, output_dir='/gpfsscratch/rech/six/commun/experiments/muennighoff/bloomckpt/6b3t0/bloomz-7b1-p3/evaluation', pad_to_max_length=False, per_device_eval_batch_size=4, prefixlm=False, split='test', target_max_length=256, template_config_name='en', template_name='does underscore refer to', tokenizer_name=None, use_slow_tokenizer=False)"
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}
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evaluation_bloomz-7b1-p3/evaluation_l2/Muennighoff_xwinograd/jp/stand_for/results.json
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"template_name": "stand for",
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"evaluation": {
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"accuracy": 0.49635036496350365
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"arguments": "Namespace(config_name=None, dataset_config_name='jp', dataset_name='Muennighoff/xwinograd', debug=False, dtype='float16', max_length=2048, model_name_or_path='/gpfsscratch/rech/six/commun/experiments/muennighoff/bloomckpt/6b3t0/bloomz-7b1-p3', nospace=False, output_dir='/gpfsscratch/rech/six/commun/experiments/muennighoff/bloomckpt/6b3t0/bloomz-7b1-p3/evaluation', pad_to_max_length=False, per_device_eval_batch_size=4, prefixlm=False, split='test', target_max_length=256, template_config_name='en', template_name='stand for', tokenizer_name=None, use_slow_tokenizer=False)"
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evaluation_bloomz-7b1-p3/evaluation_l2/Muennighoff_xwinograd/jp/underscore_refer_to/results.json
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"dataset_name": "Muennighoff/xwinograd",
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"evaluation": {
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"accuracy": 0.4994786235662148
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"arguments": "Namespace(config_name=None, dataset_config_name='jp', dataset_name='Muennighoff/xwinograd', debug=False, dtype='float16', max_length=2048, model_name_or_path='/gpfsscratch/rech/six/commun/experiments/muennighoff/bloomckpt/6b3t0/bloomz-7b1-p3', nospace=False, output_dir='/gpfsscratch/rech/six/commun/experiments/muennighoff/bloomckpt/6b3t0/bloomz-7b1-p3/evaluation', pad_to_max_length=False, per_device_eval_batch_size=4, prefixlm=False, split='test', target_max_length=256, template_config_name='en', template_name='underscore refer to', tokenizer_name=None, use_slow_tokenizer=False)"
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}
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evaluation_bloomz-7b1-p3/evaluation_val/examples.limited=3000.model=p31lossseqglobal_step1000.task=wmt14_hi_en.templates=gpt3-en-hi-target.fewshot=0.batchsize=16.seed=1234.timestamp=2022-09-09T23:48:38.jsonl
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evaluation_bloomz-7b1-p3/evaluation_val/examples.limited=3000.model=p31lossseqglobal_step1000.task=wmt14_hi_en.templates=gpt3-hi-en-target.fewshot=0.batchsize=16.seed=1234.timestamp=2022-09-09T23:48:40.jsonl
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