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Browse files- README.md +9 -0
- adapter_config.json +21 -0
- adapter_model.bin +3 -0
- checkpoint-114/README.md +32 -0
- checkpoint-114/adapter_config.json +21 -0
- checkpoint-114/adapter_model.bin +3 -0
- checkpoint-114/optimizer.pt +3 -0
- checkpoint-114/pytorch_model.bin +3 -0
- checkpoint-114/rng_state.pth +3 -0
- checkpoint-114/scheduler.pt +3 -0
- checkpoint-114/special_tokens_map.json +24 -0
- checkpoint-114/tokenizer.json +0 -0
- checkpoint-114/tokenizer.model +3 -0
- checkpoint-114/tokenizer_config.json +32 -0
- checkpoint-114/trainer_state.json +112 -0
- checkpoint-114/training_args.bin +3 -0
- config.json +26 -0
- generation_config.json +9 -0
- pytorch_model-00001-of-00002.bin +3 -0
- pytorch_model-00002-of-00002.bin +3 -0
- pytorch_model.bin.index.json +330 -0
- runs/Jul25_08-15-09_66e8430a540f/events.out.tfevents.1690272909.66e8430a540f.253176.0 +3 -0
- runs/Jul25_10-24-31_66e8430a540f/events.out.tfevents.1690280671.66e8430a540f.253712.0 +3 -0
- runs/Jul25_10-54-54_66e8430a540f/events.out.tfevents.1690282494.66e8430a540f.254253.0 +3 -0
- special_tokens_map.json +23 -0
- tokenizer.json +0 -0
- tokenizer.model +3 -0
- tokenizer_config.json +32 -0
- training_args.bin +3 -0
README.md
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---
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tags:
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- autotrain
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- text-generation
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widget:
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- text: "I love AutoTrain because "
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---
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# Model Trained Using AutoTrain
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adapter_config.json
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{
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"auto_mapping": null,
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"base_model_name_or_path": "meta-llama/Llama-2-7b-hf",
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"bias": "none",
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"fan_in_fan_out": false,
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"inference_mode": true,
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"init_lora_weights": true,
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"layers_pattern": null,
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"layers_to_transform": null,
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"lora_alpha": 32,
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"lora_dropout": 0.05,
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"modules_to_save": null,
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"peft_type": "LORA",
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"r": 16,
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"revision": null,
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"target_modules": [
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"q_proj",
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"v_proj"
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],
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"task_type": "CAUSAL_LM"
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}
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adapter_model.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:3b3128fd263741cda4a7ff2a4ca5c65fde7f2cc251109b3ff3adc40dfe380eb5
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size 33600461
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checkpoint-114/README.md
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---
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library_name: peft
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---
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## Training procedure
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The following `bitsandbytes` quantization config was used during training:
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- load_in_8bit: False
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- load_in_4bit: True
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- llm_int8_threshold: 6.0
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- llm_int8_skip_modules: None
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- llm_int8_enable_fp32_cpu_offload: False
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- llm_int8_has_fp16_weight: False
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- bnb_4bit_quant_type: nf4
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- bnb_4bit_use_double_quant: False
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- bnb_4bit_compute_dtype: float16
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The following `bitsandbytes` quantization config was used during training:
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- load_in_8bit: False
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- load_in_4bit: True
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- llm_int8_threshold: 6.0
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- llm_int8_skip_modules: None
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- llm_int8_enable_fp32_cpu_offload: False
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- llm_int8_has_fp16_weight: False
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- bnb_4bit_quant_type: nf4
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- bnb_4bit_use_double_quant: False
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- bnb_4bit_compute_dtype: float16
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### Framework versions
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- PEFT 0.4.0
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- PEFT 0.4.0
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checkpoint-114/adapter_config.json
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"base_model_name_or_path": "meta-llama/Llama-2-7b-hf",
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"bias": "none",
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"fan_in_fan_out": false,
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"inference_mode": true,
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"init_lora_weights": true,
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"layers_pattern": null,
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"lora_alpha": 32,
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"lora_dropout": 0.05,
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"modules_to_save": null,
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"peft_type": "LORA",
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"r": 16,
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"revision": null,
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"target_modules": [
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"q_proj",
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"v_proj"
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],
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"task_type": "CAUSAL_LM"
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}
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checkpoint-114/adapter_model.bin
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checkpoint-114/optimizer.pt
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checkpoint-114/pytorch_model.bin
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checkpoint-114/rng_state.pth
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checkpoint-114/scheduler.pt
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checkpoint-114/special_tokens_map.json
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checkpoint-114/tokenizer.json
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checkpoint-114/tokenizer.model
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checkpoint-114/tokenizer_config.json
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}
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