library_name: peft
base_model: tiiuae/falcon-7b
Model Card for Model ID
Model Details
Model Description
The Falcon-7b model has been fine-tuned on a dataset of car repairs and maintenance questions and answers. It is designed to provide detailed and informative responses to queries related to car maintenance and repair.
- Developed by: Osas Usen
- 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]: tiiuae/falcon-7b
Model Sources [optional]
- Repository: [More Information Needed]
- Paper [optional]: [More Information Needed]
- Demo [optional]: [More Information Needed]
Uses
Direct Use
This model can be used directly to answer car maintenance and repair questions with detailed and informative responses.
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Downstream Use [optional]
This model can also be fine-tuned for specific tasks or integrated into applications related to automotive maintenance and support.
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Out-of-Scope Use
Misuse or malicious use of the model is out of scope, and it may not perform well for tasks unrelated to car repairs and maintenance.
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Bias, Risks, and Limitations
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Recommendations
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
To get started with the model, use the provided code and guidelines. Customizations for specific applications may be necessary.
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Training Details
Training Data
The model was fine-tuned on a dataset of car repairs and maintenance questions and answers. Additional information on the training data is available in the Data Card.
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Training Procedure
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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- Hours used: [More Information Needed]
- Cloud Provider: [More Information Needed]
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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: 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: bfloat16
Framework versions
- PEFT 0.6.0.dev0