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Lora-Meta-Llama2-7b-hf-QandA_2g_v01-v03
daochf/Lora-Meta-Llama2-7b-hf-QandA_2g_v01-v03
Lora-Meta-Llama2-7b-hf-QandA_2g_v01-v03 is a machine learning model from daochf. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for peft.
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At a glance
- Library
- peft
- Access
- Public
- Created
- Jan 4, 2024
- Updated
- Jan 5, 2024
- SHA
- 8174f405
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From the Hugging Face model README
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<!-- Provide a quick summary of what the model is/does. -->DATASET_FILE_NAME = 'QandA_2g_v01.json'
adapter_name = "Lora-Meta-Llama2-7b-hf-QandA_2g_v01-v03"
Full dataset size: 2011
Eval dataset size: 403
Epochs: 20
Steps: 40220
Total training time: 3.26 horas
Start training at: 2024-01-04 14:34:08.062743
0%| | 0/40220 [00:00<?, ?it/s]
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100%|██████████████████████████████████████████████████████████████████████████| 40220/40220 [3:16:07<00:00, 3.42it/s]
Training done at: 2024-01-04 17:50:15.380360
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Model Details
Model Description
<!-- Provide a longer summary of what this model is. -->- Developed by: [More Information Needed]
- Funded by [optional]: [More Information Needed]
- Shared by [optional]: [More Information Needed]
- Model type: [More Information Needed]
- Language(s) (NLP): [More Information Needed]
- License: [More Information Needed]
- Finetuned from model [optional]: [More Information Needed]
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- Demo [optional]: [More Information Needed]
Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->[More Information Needed]
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->[More Information Needed]
Out-of-Scope Use
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Recommendations
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Training Details
Training Data
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Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->Preprocessing [optional]
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Training Hyperparameters
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Summary
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<!-- Relevant interpretability work for the model goes here -->[More Information Needed]
Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
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Training procedure
The following bitsandbytes quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: float16
Framework versions
- PEFT 0.6.2
Training procedure
The following bitsandbytes quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: float16
Framework versions
- PEFT 0.6.2
Training procedure
The following bitsandbytes quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: float16
Framework versions
- PEFT 0.6.2
- Library
- peft
- Created
- Jan 4, 2024
- Updated
- Jan 5, 2024