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RichardErkhov/parsak_-_phi-2-code-instruct-gguf
parsak_-_phi-2-code-instruct-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.
Downloads · 30 days
396
19% of all-time downloads
All-time downloads
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.gguf37.4 GB · 100%
From the Hugging Face model README
Quantization made by Richard Erkhov.
phi-2-code-instruct - GGUF
| Name | Quant method | Size |
|---|---|---|
| phi-2-code-instruct.Q2_K.gguf | Q2_K | 1.03GB |
| phi-2-code-instruct.IQ3_XS.gguf | IQ3_XS | 1.12GB |
| phi-2-code-instruct.IQ3_S.gguf | IQ3_S | 1.16GB |
| phi-2-code-instruct.Q3_K_S.gguf | Q3_K_S | 1.16GB |
| phi-2-code-instruct.IQ3_M.gguf | IQ3_M | 1.23GB |
| phi-2-code-instruct.Q3_K.gguf | Q3_K | 1.33GB |
| phi-2-code-instruct.Q3_K_M.gguf | Q3_K_M | 1.33GB |
| phi-2-code-instruct.Q3_K_L.gguf | Q3_K_L | 1.47GB |
| phi-2-code-instruct.IQ4_XS.gguf | IQ4_XS | 1.43GB |
| phi-2-code-instruct.Q4_0.gguf | Q4_0 | 1.49GB |
| phi-2-code-instruct.IQ4_NL.gguf | IQ4_NL | 1.5GB |
| phi-2-code-instruct.Q4_K_S.gguf | Q4_K_S | 1.51GB |
| phi-2-code-instruct.Q4_K.gguf | Q4_K | 1.62GB |
| phi-2-code-instruct.Q4_K_M.gguf | Q4_K_M | 1.62GB |
| phi-2-code-instruct.Q4_1.gguf | Q4_1 | 1.65GB |
| phi-2-code-instruct.Q5_0.gguf | Q5_0 | 1.8GB |
| phi-2-code-instruct.Q5_K_S.gguf | Q5_K_S | 1.8GB |
| phi-2-code-instruct.Q5_K.gguf | Q5_K | 1.87GB |
| phi-2-code-instruct.Q5_K_M.gguf | Q5_K_M | 1.87GB |
| phi-2-code-instruct.Q5_1.gguf | Q5_1 | 1.95GB |
| phi-2-code-instruct.Q6_K.gguf | Q6_K | 2.13GB |
| phi-2-code-instruct.Q8_0.gguf | Q8_0 | 2.75GB |
license: mit datasets:
This modelcard aims to be a base template for new models. It has been generated using this raw template.
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
Use the code below to get started with the model.
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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
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