library_name: peft
base_model: HuggingFaceM4/idefics-9b
datasets:
- TheFusion21/PokemonCards
language:
- en
Model Card for Model ID
Model Details
Model Description
The model is a fine-tuned derivative of the large-scale HuggingFaceM4/idefics-9b, leveraging the PEFT. It is specialized in tasks related to Pokémon card data, capable of various NLP tasks like text generation, classification, and analysis within the context of Pokémon trading cards.
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- 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]: HuggingFaceM4/idefics-9b
Model Sources [optional]
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Uses
Direct Use
The model is designed for direct use in applications involving Pokémon card data analysis, generation, and enrichment tasks.
Downstream Use
Further fine-tuning on other niche domains could enable the model to perform specialized tasks in additional contexts beyond Pokémon cards.
Out-of-Scope Use
The model may not perform well on general NLP tasks unrelated to Pokémon data or vastly different domains without further fine-tuning.
Bias, Risks, and Limitations
The model may inherit biases present in the Pokémon card dataset and may not generalize well to other contexts. Use in sensitive areas should be with caution.
Recommendations
Users should be aware of the model's specialized nature and its limitations when applied outside its intended domain. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
Training Details
Training Data
The model was trained on the TheFusion21/PokemonCards dataset from HuggingFace. Initially, the dataset underwent a cleaning process to remove any invalid URLs that could disrupt the training pipeline. Subsequently, the data was preprocessed to align with the model's input requirements, ensuring efficient and error-free training.
Training Procedure
The dataset was preprocessed to fit the model's input requirements, with special attention to tokenization and formatting.
Preprocessing [optional]
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Training Hyperparameters
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Speeds, Sizes, Times [optional]
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Evaluation
Testing Data, Factors & Metrics
Testing Data
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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
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
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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]
BibTeX:
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APA:
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Glossary [optional]
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Model Card Authors [optional]
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Model Card Contact
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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: ['lm_head', 'embed_tokens']
- 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.dev0