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Walmart-the-bag_-_Quintellect-10.7B-gguf
RichardErkhov/Walmart-the-bag_-_Quintellect-10.7B-gguf
Walmart-the-bag_-_Quintellect-10.7B-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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532
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122 GB
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.gguf141 GB · 100%
At a glance
- Access
- Public
- Created
- Jul 28, 2024
- Updated
- Jul 28, 2024
- SHA
- 11143412
Model card
From the Hugging Face model README
Quantization made by Richard Erkhov.
Quintellect-10.7B - GGUF
- Model creator: https://huggingface.co/Walmart-the-bag/
- Original model: https://huggingface.co/Walmart-the-bag/Quintellect-10.7B/
| Name | Quant method | Size |
|---|---|---|
| Quintellect-10.7B.Q2_K.gguf | Q2_K | 3.73GB |
| Quintellect-10.7B.IQ3_XS.gguf | IQ3_XS | 4.14GB |
| Quintellect-10.7B.IQ3_S.gguf | IQ3_S | 4.37GB |
| Quintellect-10.7B.Q3_K_S.gguf | Q3_K_S | 4.34GB |
| Quintellect-10.7B.IQ3_M.gguf | IQ3_M | 4.51GB |
| Quintellect-10.7B.Q3_K.gguf | Q3_K | 4.84GB |
| Quintellect-10.7B.Q3_K_M.gguf | Q3_K_M | 4.84GB |
| Quintellect-10.7B.Q3_K_L.gguf | Q3_K_L | 5.26GB |
| Quintellect-10.7B.IQ4_XS.gguf | IQ4_XS | 5.43GB |
| Quintellect-10.7B.Q4_0.gguf | Q4_0 | 5.66GB |
| Quintellect-10.7B.IQ4_NL.gguf | IQ4_NL | 5.72GB |
| Quintellect-10.7B.Q4_K_S.gguf | Q4_K_S | 5.7GB |
| Quintellect-10.7B.Q4_K.gguf | Q4_K | 6.02GB |
| Quintellect-10.7B.Q4_K_M.gguf | Q4_K_M | 6.02GB |
| Quintellect-10.7B.Q4_1.gguf | Q4_1 | 6.27GB |
| Quintellect-10.7B.Q5_0.gguf | Q5_0 | 6.89GB |
| Quintellect-10.7B.Q5_K_S.gguf | Q5_K_S | 6.89GB |
| Quintellect-10.7B.Q5_K.gguf | Q5_K | 7.08GB |
| Quintellect-10.7B.Q5_K_M.gguf | Q5_K_M | 7.08GB |
| Quintellect-10.7B.Q5_1.gguf | Q5_1 | 7.51GB |
| Quintellect-10.7B.Q6_K.gguf | Q6_K | 8.2GB |
| Quintellect-10.7B.Q8_0.gguf | Q8_0 | 10.62GB |
Original model description:
license: apache-2.0 tags:
- code datasets:
- sahil2801/CodeAlpaca-20k language:
- en inference: false
Quintellect-10.7B
The Quintellect-10.7B AI model was created to help achieve greater accessibility to coding knowledge and expertise, empowering the community to overcome technological challenges and foster innovation. I developed the model because I believe in democratizing access to coding skills, which are essential for bridging the digital divide and unleashing creativity worldwide.
This model excels in coding tasks, proficiently handling languages like Python and JavaScript. Whether you need assistance with standard programming tasks or require code creation from scratch its a good model to use. With its capabilities, it can guide you through challenging coding problems, offer solutions, or even generate code tailored to your specifications.
The prompt format for the Quintellect-10.7B LLM model is Alpaca
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Response:
- Created
- Jul 28, 2024
- Updated
- Jul 28, 2024