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peft-lora-tagger
Fsg-15/peft-lora-tagger
peft-lora-tagger is a machine learning model from Fsg-15. 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.
- Developed by: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] -…
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At a glance
- Library
- peft
- Access
- Public
- Created
- Nov 10, 2023
- Updated
- Nov 10, 2023
- SHA
- 4c22987b
Base models
Model card
From the Hugging Face model README
Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->Model Details
Model Description
<!-- Provide a longer summary of what this model is. -->- Developed by: [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]
Model Sources [optional]
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- Paper [optional]: [More Information Needed]
- Demo [optional]: [More Information Needed]
Uses
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Out-of-Scope Use
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Bias, Risks, and Limitations
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Recommendations
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How to Get Started with the Model
Use the code below to get started with the model.
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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
- Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
Speeds, Sizes, Times [optional]
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Evaluation
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Factors
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Metrics
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Results
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Summary
Model Examination [optional]
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Environmental Impact
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Technical Specifications [optional]
Model Architecture and Objective
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Compute Infrastructure
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Hardware
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Software
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Citation [optional]
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Training procedure
The following bitsandbytes quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- 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: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
Framework versions
- PEFT 0.6.2.dev0
- Library
- peft
- Created
- Nov 10, 2023
- Updated
- Nov 10, 2023