Update README.md

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	---
	datasets:
	- cognitivecomputations/dolphin
	- jondurbin/airoboros-2.2.1
	- cognitivecomputations/dolphin-coder
	- teknium/openhermes
	- ise-uiuc/Magicoder-OSS-Instruct-75K
	- ise-uiuc/Magicoder-Evol-Instruct-110K
	- m-a-p/Code-Feedback
	- m-a-p/CodeFeedback-Filtered-Instruction
	language:
	- en
	license: bigcode-openrail-m
	base_model: cognitivecomputations/dolphincoder-starcoder2-15b
	tags:
	- TensorBlock
	- GGUF
	---

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	## cognitivecomputations/dolphincoder-starcoder2-15b - GGUF

	This repo contains GGUF format model files for [cognitivecomputations/dolphincoder-starcoder2-15b](https://huggingface.co/cognitivecomputations/dolphincoder-starcoder2-15b).

	The files were quantized using machines provided by [TensorBlock](https://tensorblock.co/), and they are compatible with llama.cpp as of [commit b4242](https://github.com/ggerganov/llama.cpp/commit/a6744e43e80f4be6398fc7733a01642c846dce1d).

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	## Prompt template

	```
	<\|im_start\|>system
	{system_prompt}<\|im_end\|>
	<\|im_start\|>user
	{prompt}<\|im_end\|>
	<\|im_start\|>assistant
	```

	## Model file specification

	\| Filename \| Quant type \| File Size \| Description \|
	\| -------- \| ---------- \| --------- \| ----------- \|
	\| [dolphincoder-starcoder2-15b-Q2_K.gguf](https://huggingface.co/tensorblock/dolphincoder-starcoder2-15b-GGUF/blob/main/dolphincoder-starcoder2-15b-Q2_K.gguf) \| Q2_K \| 6.193 GB \| smallest, significant quality loss - not recommended for most purposes \|
	\| [dolphincoder-starcoder2-15b-Q3_K_S.gguf](https://huggingface.co/tensorblock/dolphincoder-starcoder2-15b-GGUF/blob/main/dolphincoder-starcoder2-15b-Q3_K_S.gguf) \| Q3_K_S \| 6.986 GB \| very small, high quality loss \|
	\| [dolphincoder-starcoder2-15b-Q3_K_M.gguf](https://huggingface.co/tensorblock/dolphincoder-starcoder2-15b-GGUF/blob/main/dolphincoder-starcoder2-15b-Q3_K_M.gguf) \| Q3_K_M \| 8.044 GB \| very small, high quality loss \|
	\| [dolphincoder-starcoder2-15b-Q3_K_L.gguf](https://huggingface.co/tensorblock/dolphincoder-starcoder2-15b-GGUF/blob/main/dolphincoder-starcoder2-15b-Q3_K_L.gguf) \| Q3_K_L \| 8.965 GB \| small, substantial quality loss \|
	\| [dolphincoder-starcoder2-15b-Q4_0.gguf](https://huggingface.co/tensorblock/dolphincoder-starcoder2-15b-GGUF/blob/main/dolphincoder-starcoder2-15b-Q4_0.gguf) \| Q4_0 \| 9.065 GB \| legacy; small, very high quality loss - prefer using Q3_K_M \|
	\| [dolphincoder-starcoder2-15b-Q4_K_S.gguf](https://huggingface.co/tensorblock/dolphincoder-starcoder2-15b-GGUF/blob/main/dolphincoder-starcoder2-15b-Q4_K_S.gguf) \| Q4_K_S \| 9.161 GB \| small, greater quality loss \|
	\| [dolphincoder-starcoder2-15b-Q4_K_M.gguf](https://huggingface.co/tensorblock/dolphincoder-starcoder2-15b-GGUF/blob/main/dolphincoder-starcoder2-15b-Q4_K_M.gguf) \| Q4_K_M \| 9.860 GB \| medium, balanced quality - recommended \|
	\| [dolphincoder-starcoder2-15b-Q5_0.gguf](https://huggingface.co/tensorblock/dolphincoder-starcoder2-15b-GGUF/blob/main/dolphincoder-starcoder2-15b-Q5_0.gguf) \| Q5_0 \| 11.022 GB \| legacy; medium, balanced quality - prefer using Q4_K_M \|
	\| [dolphincoder-starcoder2-15b-Q5_K_S.gguf](https://huggingface.co/tensorblock/dolphincoder-starcoder2-15b-GGUF/blob/main/dolphincoder-starcoder2-15b-Q5_K_S.gguf) \| Q5_K_S \| 11.022 GB \| large, low quality loss - recommended \|
	\| [dolphincoder-starcoder2-15b-Q5_K_M.gguf](https://huggingface.co/tensorblock/dolphincoder-starcoder2-15b-GGUF/blob/main/dolphincoder-starcoder2-15b-Q5_K_M.gguf) \| Q5_K_M \| 11.432 GB \| large, very low quality loss - recommended \|
	\| [dolphincoder-starcoder2-15b-Q6_K.gguf](https://huggingface.co/tensorblock/dolphincoder-starcoder2-15b-GGUF/blob/main/dolphincoder-starcoder2-15b-Q6_K.gguf) \| Q6_K \| 13.101 GB \| very large, extremely low quality loss \|
	\| [dolphincoder-starcoder2-15b-Q8_0.gguf](https://huggingface.co/tensorblock/dolphincoder-starcoder2-15b-GGUF/blob/main/dolphincoder-starcoder2-15b-Q8_0.gguf) \| Q8_0 \| 16.965 GB \| very large, extremely low quality loss - not recommended \|


	## Downloading instruction

	### Command line

	Firstly, install Huggingface Client

	```shell
	pip install -U "huggingface_hub[cli]"
	```

	Then, downoad the individual model file the a local directory

	```shell
	huggingface-cli download tensorblock/dolphincoder-starcoder2-15b-GGUF --include "dolphincoder-starcoder2-15b-Q2_K.gguf" --local-dir MY_LOCAL_DIR
	```

	If you wanna download multiple model files with a pattern (e.g., `Q4_Kgguf`), you can try:

	```shell
	huggingface-cli download tensorblock/dolphincoder-starcoder2-15b-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='Q4_Kgguf'
	```