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NeuralNovel_-_Panda-7B-v0.1-gguf
RichardErkhov/NeuralNovel_-_Panda-7B-v0.1-gguf
NeuralNovel_-_Panda-7B-v0.1-gguf is a machine learning model from RichardErkhov. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
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At a glance
- Access
- Public
- Created
- May 15, 2024
- Updated
- May 15, 2024
- SHA
- 6efac497
Model card
From the Hugging Face model README
Quantization made by Richard Erkhov.
Panda-7B-v0.1 - GGUF
- Model creator: https://huggingface.co/NeuralNovel/
- Original model: https://huggingface.co/NeuralNovel/Panda-7B-v0.1/
| Name | Quant method | Size |
|---|---|---|
| Panda-7B-v0.1.Q2_K.gguf | Q2_K | 2.53GB |
| Panda-7B-v0.1.IQ3_XS.gguf | IQ3_XS | 2.81GB |
| Panda-7B-v0.1.IQ3_S.gguf | IQ3_S | 2.96GB |
| Panda-7B-v0.1.Q3_K_S.gguf | Q3_K_S | 2.95GB |
| Panda-7B-v0.1.IQ3_M.gguf | IQ3_M | 3.06GB |
| Panda-7B-v0.1.Q3_K.gguf | Q3_K | 3.28GB |
| Panda-7B-v0.1.Q3_K_M.gguf | Q3_K_M | 3.28GB |
| Panda-7B-v0.1.Q3_K_L.gguf | Q3_K_L | 3.56GB |
| Panda-7B-v0.1.IQ4_XS.gguf | IQ4_XS | 3.67GB |
| Panda-7B-v0.1.Q4_0.gguf | Q4_0 | 3.83GB |
| Panda-7B-v0.1.IQ4_NL.gguf | IQ4_NL | 3.87GB |
| Panda-7B-v0.1.Q4_K_S.gguf | Q4_K_S | 3.86GB |
| Panda-7B-v0.1.Q4_K.gguf | Q4_K | 4.07GB |
| Panda-7B-v0.1.Q4_K_M.gguf | Q4_K_M | 4.07GB |
| Panda-7B-v0.1.Q4_1.gguf | Q4_1 | 4.24GB |
| Panda-7B-v0.1.Q5_0.gguf | Q5_0 | 4.65GB |
| Panda-7B-v0.1.Q5_K_S.gguf | Q5_K_S | 4.65GB |
| Panda-7B-v0.1.Q5_K.gguf | Q5_K | 4.78GB |
| Panda-7B-v0.1.Q5_K_M.gguf | Q5_K_M | 4.78GB |
| Panda-7B-v0.1.Q5_1.gguf | Q5_1 | 5.07GB |
| Panda-7B-v0.1.Q6_K.gguf | Q6_K | 5.53GB |
| Panda-7B-v0.1.Q8_0.gguf | Q8_0 | 7.17GB |
Original model description:
license: apache-2.0 base_model: mistralai/Mistral-7B-Instruct-v0.2 datasets:
- NeuralNovel/Creative-Logic-v1
- NeuralNovel/Neural-Story-v1 library_name: transformers inference: false
NeuralNovel/Panda-7B-v0.1
The Panda-7B-v0.1 model by NeuralNovel.
This fine-tune has been designed to provide detailed, creative and logical responses in the context of diverse narratives. Optimised for creative writing, roleplay and logical problem solving.
Full-parameter fine-tune (FFT) of Mistral-7B-Instruct-v0.2. Apache-2.0 license, suitable for commercial or non-commercial use.
<a href='https://ko-fi.com/S6S2UH2TC' target='_blank'><img height='38' style='border:0px;height:36px;' src='https://storage.ko-fi.com/cdn/kofi1.png?v=3' border='0' alt='Buy Me a Coffee at ko-fi.com' /></a> <a href='https://discord.gg/KFS229xD' target='_blank'><img width='140' height='500' style='border:0px;height:36px;' src='https://i.ibb.co/tqwznYM/Discord-button.png' border='0' alt='Join Our Discord!' /></a>
Data-set
The model on finetuned using the Panda-v1 dataset.
Summary
Fine-tuned with the intention to generate instructive and narrative text, with a specific focus on combining the elements of versatility, character engagement and nuanced writing capability.
Out-of-Scope Use
The model may not perform well in scenarios unrelated to instructive and narrative text generation. Misuse or applications outside its designed scope may result in suboptimal outcomes.
Bias, Risks, and Limitations
The model may exhibit biases or limitations inherent in the training data. It is essential to consider these factors when deploying the model to avoid unintended consequences.
Users are advised to exercise caution, as there might be some inherent genre or writing bias.
Hardware and Training
n_epochs = 3,
n_checkpoints = 3,
batch_size = 12,
learning_rate = 1e-5,
Sincere appreciation to Techmind for their generous sponsorship.
- Created
- May 15, 2024
- Updated
- May 15, 2024