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	---
	language:
	- en
	- vi
	license: mit
	library_name: transformers
	tags:
	- ghost
	- TensorBlock
	- GGUF
	pipeline_tag: text-generation
	base_model: ghost-x/ghost-7b-v0.9.0
	widget:
	- text: '<\|system\|>

	You are a helpful assistant.</s>

	<\|user\|>

	Thông tin về Peristernia despecta</s>

	<\|assistant\|>

	'
	output:
	text: Peristernia despecta là một loài ốc biển, là động vật thân mềm chân bụng
	sống ở biển trong họ Fasciolariidae.
	model-index:
	- name: lamhieu/ghost-7b-v0.9.0
	results:
	- task:
	type: text-generation
	dataset:
	name: VMLU
	type: vmlu_v1.5
	metrics:
	- type: avg
	value: 36.06
	name: Average
	verified: true
	- type: stem
	value: 33.54
	name: STEM
	verified: true
	- type: ss
	value: 38.74
	name: Social science
	verified: true
	- type: hm
	value: 37.15
	name: Humanities
	verified: true
	- type: ot
	value: 36.78
	name: Other
	verified: true
	- task:
	type: text-generation
	dataset:
	name: Open LLM Leaderboard
	type: open_llm_leaderboard
	metrics:
	- type: avg
	value: 56.89
	name: Average
	verified: true
	- type: arc
	value: 53.07
	name: ARC
	verified: true
	- type: hs
	value: 77.93
	name: HellaSwag
	verified: true
	- type: hs
	value: 77.93
	name: HellaSwag
	verified: true
	- type: mmlu
	value: 55.09
	name: MMLU
	verified: true
	- type: wg
	value: 73.72
	name: Winogrande
	verified: true
	- type: gsm8k
	value: 33.74
	name: GSM8K
	verified: true
	source:
	url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=lamhieu/ghost-7b-v0.9.0
	name: Open LLM Leaderboard
	- task:
	type: text-generation
	name: Text Generation
	dataset:
	name: AI2 Reasoning Challenge (25-Shot)
	type: ai2_arc
	config: ARC-Challenge
	split: test
	args:
	num_few_shot: 25
	metrics:
	- type: acc_norm
	value: 53.07
	name: normalized accuracy
	source:
	url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=lamhieu/ghost-7b-v0.9.0
	name: Open LLM Leaderboard
	- task:
	type: text-generation
	name: Text Generation
	dataset:
	name: HellaSwag (10-Shot)
	type: hellaswag
	split: validation
	args:
	num_few_shot: 10
	metrics:
	- type: acc_norm
	value: 77.93
	name: normalized accuracy
	source:
	url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=lamhieu/ghost-7b-v0.9.0
	name: Open LLM Leaderboard
	- task:
	type: text-generation
	name: Text Generation
	dataset:
	name: MMLU (5-Shot)
	type: cais/mmlu
	config: all
	split: test
	args:
	num_few_shot: 5
	metrics:
	- type: acc
	value: 55.09
	name: accuracy
	source:
	url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=lamhieu/ghost-7b-v0.9.0
	name: Open LLM Leaderboard
	- task:
	type: text-generation
	name: Text Generation
	dataset:
	name: TruthfulQA (0-shot)
	type: truthful_qa
	config: multiple_choice
	split: validation
	args:
	num_few_shot: 0
	metrics:
	- type: mc2
	value: 47.79
	source:
	url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=lamhieu/ghost-7b-v0.9.0
	name: Open LLM Leaderboard
	- task:
	type: text-generation
	name: Text Generation
	dataset:
	name: Winogrande (5-shot)
	type: winogrande
	config: winogrande_xl
	split: validation
	args:
	num_few_shot: 5
	metrics:
	- type: acc
	value: 73.72
	name: accuracy
	source:
	url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=lamhieu/ghost-7b-v0.9.0
	name: Open LLM Leaderboard
	- task:
	type: text-generation
	name: Text Generation
	dataset:
	name: GSM8k (5-shot)
	type: gsm8k
	config: main
	split: test
	args:
	num_few_shot: 5
	metrics:
	- type: acc
	value: 33.74
	name: accuracy
	source:
	url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=lamhieu/ghost-7b-v0.9.0
	name: Open LLM Leaderboard
	---

	<div style="width: auto; margin-left: auto; margin-right: auto">
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	## ghost-x/ghost-7b-v0.9.0 - GGUF

	This repo contains GGUF format model files for [ghost-x/ghost-7b-v0.9.0](https://huggingface.co/ghost-x/ghost-7b-v0.9.0).

	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>
	</tr>
	<tr>
	<th colspan="2">
	<img src="https://imgur.com/faI5UKh.jpeg" alt="Forge Project" width="900"/>
	</th>
	</tr>
	<tr>
	<th colspan="2">An OpenAI-compatible multi-provider routing layer.</th>
	</tr>
	<tr>
	<th colspan="2">
	<a href="https://github.com/TensorBlock/forge" target="_blank" style="
	display: inline-block;
	padding: 8px 16px;
	background-color: #FF7F50;
	color: white;
	text-decoration: none;
	border-radius: 6px;
	font-weight: bold;
	font-family: sans-serif;
	">🚀 Try it now! 🚀</a>
	</th>
	</tr>

	<tr>
	<th style="font-size: 25px;">Awesome MCP Servers</th>
	<th style="font-size: 25px;">TensorBlock Studio</th>
	</tr>
	<tr>
	<th><img src="https://imgur.com/2Xov7B7.jpeg" alt="MCP Servers" width="450"/></th>
	<th><img src="https://imgur.com/pJcmF5u.jpeg" alt="Studio" width="450"/></th>
	</tr>
	<tr>
	<th>A comprehensive collection of Model Context Protocol (MCP) servers.</th>
	<th>A lightweight, open, and extensible multi-LLM interaction studio.</th>
	</tr>
	<tr>
	<th>
	<a href="https://github.com/TensorBlock/awesome-mcp-servers" target="_blank" style="
	display: inline-block;
	padding: 8px 16px;
	background-color: #FF7F50;
	color: white;
	text-decoration: none;
	border-radius: 6px;
	font-weight: bold;
	font-family: sans-serif;
	">👀 See what we built 👀</a>
	</th>
	<th>
	<a href="https://github.com/TensorBlock/TensorBlock-Studio" target="_blank" style="
	display: inline-block;
	padding: 8px 16px;
	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

	```
	<\|system\|>
	{system_prompt}</s>
	<\|user\|>
	{prompt}</s>
	<\|assistant\|>
	```

	## Model file specification

	\| Filename \| Quant type \| File Size \| Description \|
	\| -------- \| ---------- \| --------- \| ----------- \|
	\| [ghost-7b-v0.9.0-Q2_K.gguf](https://huggingface.co/tensorblock/ghost-7b-v0.9.0-GGUF/blob/main/ghost-7b-v0.9.0-Q2_K.gguf) \| Q2_K \| 2.532 GB \| smallest, significant quality loss - not recommended for most purposes \|
	\| [ghost-7b-v0.9.0-Q3_K_S.gguf](https://huggingface.co/tensorblock/ghost-7b-v0.9.0-GGUF/blob/main/ghost-7b-v0.9.0-Q3_K_S.gguf) \| Q3_K_S \| 2.947 GB \| very small, high quality loss \|
	\| [ghost-7b-v0.9.0-Q3_K_M.gguf](https://huggingface.co/tensorblock/ghost-7b-v0.9.0-GGUF/blob/main/ghost-7b-v0.9.0-Q3_K_M.gguf) \| Q3_K_M \| 3.277 GB \| very small, high quality loss \|
	\| [ghost-7b-v0.9.0-Q3_K_L.gguf](https://huggingface.co/tensorblock/ghost-7b-v0.9.0-GGUF/blob/main/ghost-7b-v0.9.0-Q3_K_L.gguf) \| Q3_K_L \| 3.560 GB \| small, substantial quality loss \|
	\| [ghost-7b-v0.9.0-Q4_0.gguf](https://huggingface.co/tensorblock/ghost-7b-v0.9.0-GGUF/blob/main/ghost-7b-v0.9.0-Q4_0.gguf) \| Q4_0 \| 3.827 GB \| legacy; small, very high quality loss - prefer using Q3_K_M \|
	\| [ghost-7b-v0.9.0-Q4_K_S.gguf](https://huggingface.co/tensorblock/ghost-7b-v0.9.0-GGUF/blob/main/ghost-7b-v0.9.0-Q4_K_S.gguf) \| Q4_K_S \| 3.856 GB \| small, greater quality loss \|
	\| [ghost-7b-v0.9.0-Q4_K_M.gguf](https://huggingface.co/tensorblock/ghost-7b-v0.9.0-GGUF/blob/main/ghost-7b-v0.9.0-Q4_K_M.gguf) \| Q4_K_M \| 4.068 GB \| medium, balanced quality - recommended \|
	\| [ghost-7b-v0.9.0-Q5_0.gguf](https://huggingface.co/tensorblock/ghost-7b-v0.9.0-GGUF/blob/main/ghost-7b-v0.9.0-Q5_0.gguf) \| Q5_0 \| 4.654 GB \| legacy; medium, balanced quality - prefer using Q4_K_M \|
	\| [ghost-7b-v0.9.0-Q5_K_S.gguf](https://huggingface.co/tensorblock/ghost-7b-v0.9.0-GGUF/blob/main/ghost-7b-v0.9.0-Q5_K_S.gguf) \| Q5_K_S \| 4.654 GB \| large, low quality loss - recommended \|
	\| [ghost-7b-v0.9.0-Q5_K_M.gguf](https://huggingface.co/tensorblock/ghost-7b-v0.9.0-GGUF/blob/main/ghost-7b-v0.9.0-Q5_K_M.gguf) \| Q5_K_M \| 4.779 GB \| large, very low quality loss - recommended \|
	\| [ghost-7b-v0.9.0-Q6_K.gguf](https://huggingface.co/tensorblock/ghost-7b-v0.9.0-GGUF/blob/main/ghost-7b-v0.9.0-Q6_K.gguf) \| Q6_K \| 5.534 GB \| very large, extremely low quality loss \|
	\| [ghost-7b-v0.9.0-Q8_0.gguf](https://huggingface.co/tensorblock/ghost-7b-v0.9.0-GGUF/blob/main/ghost-7b-v0.9.0-Q8_0.gguf) \| Q8_0 \| 7.167 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/ghost-7b-v0.9.0-GGUF --include "ghost-7b-v0.9.0-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/ghost-7b-v0.9.0-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='Q4_Kgguf'
	```