Update README.md

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	---
	language:
	- en
	tags:
	- text generation
	- instruct
	- TensorBlock
	- GGUF
	pipeline_tag: text-generation
	inference: false
	license: llama2
	datasets:
	- PygmalionAI/PIPPA
	- Open-Orca/OpenOrca
	- Norquinal/claude_multiround_chat_30k
	- jondurbin/airoboros-gpt4-1.4.1
	- databricks/databricks-dolly-15k
	base_model: PygmalionAI/pygmalion-2-7b
	---

	<div style="width: auto; margin-left: auto; margin-right: auto">
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	## PygmalionAI/pygmalion-2-7b - GGUF

	This repo contains GGUF format model files for [PygmalionAI/pygmalion-2-7b](https://huggingface.co/PygmalionAI/pygmalion-2-7b).

	The files were quantized using machines provided by [TensorBlock](https://tensorblock.co/), and they are compatible with llama.cpp as of [commit b4011](https://github.com/ggerganov/llama.cpp/commit/a6744e43e80f4be6398fc7733a01642c846dce1d).

	## Our projects
	<table border="1" cellspacing="0" cellpadding="10">
	<tr>
	<th colspan="2" style="font-size: 25px;">Forge</th>
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	<tr>
	<th colspan="2">An OpenAI-compatible multi-provider routing layer.</th>
	</tr>
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	<a href="https://github.com/TensorBlock/forge" target="_blank" style="
	display: inline-block;
	padding: 8px 16px;
	background-color: #FF7F50;
	color: white;
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	font-weight: bold;
	font-family: sans-serif;
	">🚀 Try it now! 🚀</a>
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	font-weight: bold;
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	">👀 See what we built 👀</a>
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	display: inline-block;
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	background-color: #FF7F50;
	color: white;
	text-decoration: none;
	border-radius: 6px;
	font-weight: bold;
	font-family: sans-serif;
	">👀 See what we built 👀</a>
	</th>
	</tr>
	</table>
	## Prompt template

	```

	```

	## Model file specification

	\| Filename \| Quant type \| File Size \| Description \|
	\| -------- \| ---------- \| --------- \| ----------- \|
	\| [pygmalion-2-7b-Q2_K.gguf](https://huggingface.co/tensorblock/pygmalion-2-7b-GGUF/blob/main/pygmalion-2-7b-Q2_K.gguf) \| Q2_K \| 2.359 GB \| smallest, significant quality loss - not recommended for most purposes \|
	\| [pygmalion-2-7b-Q3_K_S.gguf](https://huggingface.co/tensorblock/pygmalion-2-7b-GGUF/blob/main/pygmalion-2-7b-Q3_K_S.gguf) \| Q3_K_S \| 2.746 GB \| very small, high quality loss \|
	\| [pygmalion-2-7b-Q3_K_M.gguf](https://huggingface.co/tensorblock/pygmalion-2-7b-GGUF/blob/main/pygmalion-2-7b-Q3_K_M.gguf) \| Q3_K_M \| 3.072 GB \| very small, high quality loss \|
	\| [pygmalion-2-7b-Q3_K_L.gguf](https://huggingface.co/tensorblock/pygmalion-2-7b-GGUF/blob/main/pygmalion-2-7b-Q3_K_L.gguf) \| Q3_K_L \| 3.350 GB \| small, substantial quality loss \|
	\| [pygmalion-2-7b-Q4_0.gguf](https://huggingface.co/tensorblock/pygmalion-2-7b-GGUF/blob/main/pygmalion-2-7b-Q4_0.gguf) \| Q4_0 \| 3.563 GB \| legacy; small, very high quality loss - prefer using Q3_K_M \|
	\| [pygmalion-2-7b-Q4_K_S.gguf](https://huggingface.co/tensorblock/pygmalion-2-7b-GGUF/blob/main/pygmalion-2-7b-Q4_K_S.gguf) \| Q4_K_S \| 3.592 GB \| small, greater quality loss \|
	\| [pygmalion-2-7b-Q4_K_M.gguf](https://huggingface.co/tensorblock/pygmalion-2-7b-GGUF/blob/main/pygmalion-2-7b-Q4_K_M.gguf) \| Q4_K_M \| 3.801 GB \| medium, balanced quality - recommended \|
	\| [pygmalion-2-7b-Q5_0.gguf](https://huggingface.co/tensorblock/pygmalion-2-7b-GGUF/blob/main/pygmalion-2-7b-Q5_0.gguf) \| Q5_0 \| 4.332 GB \| legacy; medium, balanced quality - prefer using Q4_K_M \|
	\| [pygmalion-2-7b-Q5_K_S.gguf](https://huggingface.co/tensorblock/pygmalion-2-7b-GGUF/blob/main/pygmalion-2-7b-Q5_K_S.gguf) \| Q5_K_S \| 4.332 GB \| large, low quality loss - recommended \|
	\| [pygmalion-2-7b-Q5_K_M.gguf](https://huggingface.co/tensorblock/pygmalion-2-7b-GGUF/blob/main/pygmalion-2-7b-Q5_K_M.gguf) \| Q5_K_M \| 4.455 GB \| large, very low quality loss - recommended \|
	\| [pygmalion-2-7b-Q6_K.gguf](https://huggingface.co/tensorblock/pygmalion-2-7b-GGUF/blob/main/pygmalion-2-7b-Q6_K.gguf) \| Q6_K \| 5.149 GB \| very large, extremely low quality loss \|
	\| [pygmalion-2-7b-Q8_0.gguf](https://huggingface.co/tensorblock/pygmalion-2-7b-GGUF/blob/main/pygmalion-2-7b-Q8_0.gguf) \| Q8_0 \| 6.669 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/pygmalion-2-7b-GGUF --include "pygmalion-2-7b-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/pygmalion-2-7b-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='Q4_Kgguf'
	```