base_model: theprint/phi-3-mini-4k-python
datasets:
- iamtarun/python_code_instructions_18k_alpaca
- ajibawa-2023/Python-Code-23k-ShareGPT
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- trl
- sft
About
static quants of https://huggingface.co/theprint/phi-3-mini-4k-python
weighted/imatrix quants are available at https://huggingface.co/mradermacher/phi-3-mini-4k-python-i1-GGUF
Usage
If you are unsure how to use GGUF files, refer to one of TheBloke's READMEs for more details, including on how to concatenate multi-part files.
Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|---|---|---|---|
| GGUF | Q2_K | 1.5 | |
| GGUF | Q3_K_S | 1.8 | |
| GGUF | Q3_K_M | 2.0 | lower quality |
| GGUF | Q3_K_L | 2.1 | |
| GGUF | IQ4_XS | 2.2 | |
| GGUF | Q4_K_S | 2.3 | fast, recommended |
| GGUF | Q4_K_M | 2.4 | fast, recommended |
| GGUF | Q5_K_S | 2.7 | |
| GGUF | Q5_K_M | 2.8 | |
| GGUF | Q6_K | 3.2 | very good quality |
| GGUF | Q8_0 | 4.2 | fast, best quality |
| GGUF | f16 | 7.7 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):
And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized.
Thanks
I thank my company, nethype GmbH, for letting me use its servers and providing upgrades to my workstation to enable this work in my free time.