Upload folder using huggingface_hub (#7)
Browse files- b533d6edf83515a50a2ad358631dfa9704acb42768f25feb96837ae7f735c2d8 (d68d8e5f123b0a0e01dcef329a1822f771220d42)
- README.md +3 -2
- config.json +1 -1
- merges.txt +0 -0
- results.json +1 -17
- smash_config.json +1 -1
- special_tokens_map.json +30 -0
- tokenizer.json +0 -0
- tokenizer_config.json +31 -0
- vocab.json +0 -0
README.md
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---
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thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
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metrics:
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- memory_disk
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- memory_inference
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@@ -59,9 +60,9 @@ You can run the smashed model with these steps:
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2. Load & run the model.
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model =
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tokenizer = AutoTokenizer.from_pretrained("facebook/opt-125m")
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input_ids = tokenizer("What is the color of prunes?,", return_tensors='pt').to(model.device)["input_ids"]
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---
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thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
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base_model: facebook/opt-125m
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metrics:
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- memory_disk
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- memory_inference
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2. Load & run the model.
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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from awq import AutoAWQForCausalLM
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model = AutoAWQForCausalLM.from_quantized("PrunaAI/facebook-opt-125m-AWQ-4bit-smashed", trust_remote_code=True, device_map='auto')
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tokenizer = AutoTokenizer.from_pretrained("facebook/opt-125m")
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input_ids = tokenizer("What is the color of prunes?,", return_tensors='pt').to(model.device)["input_ids"]
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config.json
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{
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"_name_or_path": "/tmp/
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"_remove_final_layer_norm": false,
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"activation_dropout": 0.0,
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"activation_function": "relu",
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{
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"_name_or_path": "/tmp/tmpm3p07smi",
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"_remove_final_layer_norm": false,
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"activation_dropout": 0.0,
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"activation_function": "relu",
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merges.txt
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results.json
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{
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"base_current_gpu_type": "NVIDIA A100-PCIE-40GB",
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"base_current_gpu_total_memory": 40339.3125,
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"base_memory_inference_first": 690.0,
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"base_memory_inference": 570.0,
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"base_token_generation_latency_sync": 25.73595085144043,
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"base_token_generation_latency_async": 25.555139780044556,
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"base_token_generation_throughput_sync": 0.03885615129483473,
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"base_token_generation_throughput_async": 0.03913107142465634,
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"base_token_generation_CO2_emissions": 7.04025152217974e-06,
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"base_token_generation_energy_consumption": 0.00201063437593726,
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"smashed_current_gpu_type": "NVIDIA A100-PCIE-40GB",
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"smashed_current_gpu_total_memory": 40339.3125
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"smashed_memory_inference_first": 164.0,
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"smashed_memory_inference": 206.0,
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"smashed_token_generation_latency_sync": 20.38736572265625,
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"smashed_token_generation_latency_async": 21.09651416540146,
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"smashed_token_generation_throughput_sync": 0.04904998583945111,
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"smashed_token_generation_throughput_async": 0.047401195863912546,
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"smashed_token_generation_CO2_emissions": 6.962162215496025e-06,
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"smashed_token_generation_energy_consumption": 0.001568448312001272
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}
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{
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"base_current_gpu_type": "NVIDIA A100-PCIE-40GB",
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"base_current_gpu_total_memory": 40339.3125,
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"smashed_current_gpu_type": "NVIDIA A100-PCIE-40GB",
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"smashed_current_gpu_total_memory": 40339.3125
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}
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smash_config.json
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"controlnet": "None",
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"unet_dim": 4,
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"device": "cuda",
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"cache_dir": "/ceph/hdd/staff/charpent/.cache/
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"batch_size": 1,
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"tokenizer": "GPT2TokenizerFast(name_or_path='facebook/opt-125m', vocab_size=50265, model_max_length=1000000000000000019884624838656, is_fast=True, padding_side='right', truncation_side='right', special_tokens={'bos_token': '</s>', 'eos_token': '</s>', 'unk_token': '</s>', 'pad_token': '<pad>'}, clean_up_tokenization_spaces=True), added_tokens_decoder={\n\t1: AddedToken(\"<pad>\", rstrip=False, lstrip=False, single_word=False, normalized=True, special=True),\n\t2: AddedToken(\"</s>\", rstrip=False, lstrip=False, single_word=False, normalized=True, special=True),\n}",
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"task": "text_text_generation",
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"controlnet": "None",
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"unet_dim": 4,
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"device": "cuda",
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"cache_dir": "/ceph/hdd/staff/charpent/.cache/modelsx1vv2b7p",
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"batch_size": 1,
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"tokenizer": "GPT2TokenizerFast(name_or_path='facebook/opt-125m', vocab_size=50265, model_max_length=1000000000000000019884624838656, is_fast=True, padding_side='right', truncation_side='right', special_tokens={'bos_token': '</s>', 'eos_token': '</s>', 'unk_token': '</s>', 'pad_token': '<pad>'}, clean_up_tokenization_spaces=True), added_tokens_decoder={\n\t1: AddedToken(\"<pad>\", rstrip=False, lstrip=False, single_word=False, normalized=True, special=True),\n\t2: AddedToken(\"</s>\", rstrip=False, lstrip=False, single_word=False, normalized=True, special=True),\n}",
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"task": "text_text_generation",
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special_tokens_map.json
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{
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"bos_token": {
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"content": "</s>",
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"lstrip": false,
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"normalized": true,
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"rstrip": false,
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"single_word": false
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},
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"eos_token": {
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"content": "</s>",
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"lstrip": false,
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"normalized": true,
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"rstrip": false,
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"single_word": false
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},
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"pad_token": {
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"content": "<pad>",
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"lstrip": false,
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"normalized": true,
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"rstrip": false,
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"single_word": false
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},
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"unk_token": {
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"content": "</s>",
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"lstrip": false,
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"normalized": true,
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"rstrip": false,
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"single_word": false
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}
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}
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tokenizer.json
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tokenizer_config.json
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{
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"add_bos_token": true,
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"add_prefix_space": false,
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"added_tokens_decoder": {
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"1": {
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"content": "<pad>",
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"lstrip": false,
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"normalized": true,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"2": {
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"content": "</s>",
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"lstrip": false,
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"normalized": true,
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"rstrip": false,
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"single_word": false,
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"special": true
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}
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},
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"bos_token": "</s>",
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"clean_up_tokenization_spaces": true,
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"eos_token": "</s>",
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"errors": "replace",
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"legacy": false,
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"model_max_length": 1000000000000000019884624838656,
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"pad_token": "<pad>",
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"tokenizer_class": "GPT2Tokenizer",
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"unk_token": "</s>"
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}
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vocab.json
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