Chatting 2023-07-21
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FreeWilly2 is an open-source AI model developed by Stability AI and fine-tuned with the Llama2 70B dataset. It is a language model that uses auto-regressive techniques to generate text.

The model is trained using supervised fine-tuning on an internal Orca-style dataset.Users can interact with FreeWilly2 by starting a conversation using a specific prompt format, which includes a system prompt, a user prompt, and an assistant response.

The model can generate responses based on the given prompts.FreeWilly2 is implemented using the HuggingFace Transformers library and is available under the Non-Commercial Creative Commons license (CC BY-NC-4.0).

It is intended for research purposes only.However, it's important to note that while the Llama2 dataset helps mitigate biases and toxicity in the generated text, not all biases can be eliminated through fine-tuning.

Users should be aware of potential issues and not treat the model outputs as a substitute for human judgment or a source of truth. Responsible usage is strongly encouraged.For further information or inquiries about the model, users can contact Stability AI via email at [email protected].

Citations for the Llama2 dataset and the Orca-style training methodology are provided for academic references.


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FreeWilly2 was manually vetted by our editorial team and was first featured on July 24th 2023.
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Pros and Cons


Fine-tuned with Llama2 70B
Auto-regressive techniques
Supervised training
Uses HuggingFace Transformers
Non-Commercial Creative Commons license
Biases mitigation
Email support available
Explicit prompt-based interaction
Easy integration
Low CPU memory usage
Supports mixed precision training
Optimized with AdamW
Trained on Orca dataset
Clear documentation for training procedure
Clear citation provided
Has usage example
Internal dataset training
Mitigated biases and toxicity
Draws from diverse datasets
Dataset usage transparency
Contact support provided
Academic usage supportive
On-demand loading on Inference API
Hosted inference API
PyTorch implemented
Causal Language Model
Understands English language
Multiple training phases
Inference output customization
Encourages responsible usage
Detailed model description
Easy model deployment
Training procedure transparency
Supports research purposes
High community engagement
Text generation capabilities
Hard bias warnings
Community editable model card
Highly parameterized API
Large batch size training
Strong hyperparameters
Version tracking
Datasets for model training listed
Use in Transformers option
JSON output support
Can output text narratives
Easy-to-use with code snippets
Explicit limitations and bias handling
Addresses potential issues
Special care about safety


Specific prompt format required
Potential language biases
Not a source of truth
Intended for research only
No commercial use due to CC BY-NC 4.0 license
Only in English


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