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CompilerX-LLM
Pasula/CompilerX-LLM
CompilerX-LLM is a machine learning model from Pasula. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
CompilerX is a compiled B2B database dedicated to helping fetch accurate business information and qualified marketing contacts fast! Let’s increase the efficiency of your sales prospecting with CompilerX. Millions of…
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Updated Dec 22, 2023
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At a glance
- Access
- Public
- Created
- Dec 6, 2023
- Updated
- Dec 22, 2023
- SHA
- 2c2dd735
Model card
From the Hugging Face model README
Model Card for Model ID
CompilerX is a compiled B2B database dedicated to helping fetch accurate business information and qualified marketing contacts fast! Let’s increase the efficiency of your sales prospecting with CompilerX. Millions of Companies At Your Fingertips. A premium Compiled B2B database to tap into the right contacts with buying power! We help you work smarter.
<!-- Provide a quick summary of what the model is/does. -->This modelcard aims to be a base template for new models. It has been generated using this raw template.
Model Details
Model Description
<!-- Provide a longer summary of what this model is. -->- Developed by: Pausla Maheshwar
- Funded by [optional]: Joseph Ricard
- Shared by [optional]: Klizo Solutions Private Ltd.
- Model type: text-generation
- Language(s) (NLP): [More Information Needed]
- License: [More Information Needed]
- Finetuned from model [optional]: EleutherAI/gpt-neo-2.7B
Model Sources [optional]
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- Paper [optional]: [More Information Needed]
- Demo [optional]: [More Information Needed]
Uses
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Downstream Use [optional]
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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]
[More Information Needed]
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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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
<!-- 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).
- Hardware Type: [More Information Needed]
- Hours used: [More Information Needed]
- Cloud Provider: [More Information Needed]
- Compute Region: [More Information Needed]
- Carbon Emitted: [More Information Needed]
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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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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- Created
- Dec 6, 2023
- Updated
- Dec 22, 2023