Instructions to use tensorblock/bloomz-1b7-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tensorblock/bloomz-1b7-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="tensorblock/bloomz-1b7-GGUF",
	filename="bloomz-1b7-Q2_K.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use tensorblock/bloomz-1b7-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf tensorblock/bloomz-1b7-GGUF:Q2_K
# Run inference directly in the terminal:
llama-cli -hf tensorblock/bloomz-1b7-GGUF:Q2_K

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf tensorblock/bloomz-1b7-GGUF:Q2_K
# Run inference directly in the terminal:
llama-cli -hf tensorblock/bloomz-1b7-GGUF:Q2_K

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf tensorblock/bloomz-1b7-GGUF:Q2_K
# Run inference directly in the terminal:
./llama-cli -hf tensorblock/bloomz-1b7-GGUF:Q2_K

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf tensorblock/bloomz-1b7-GGUF:Q2_K
# Run inference directly in the terminal:
./build/bin/llama-cli -hf tensorblock/bloomz-1b7-GGUF:Q2_K

Use Docker

docker model run hf.co/tensorblock/bloomz-1b7-GGUF:Q2_K

LM Studio
Jan

vLLM

How to use tensorblock/bloomz-1b7-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tensorblock/bloomz-1b7-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tensorblock/bloomz-1b7-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/tensorblock/bloomz-1b7-GGUF:Q2_K

Ollama
How to use tensorblock/bloomz-1b7-GGUF with Ollama:
```
ollama run hf.co/tensorblock/bloomz-1b7-GGUF:Q2_K
```

Unsloth Studio new

How to use tensorblock/bloomz-1b7-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for tensorblock/bloomz-1b7-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for tensorblock/bloomz-1b7-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for tensorblock/bloomz-1b7-GGUF to start chatting

Docker Model Runner
How to use tensorblock/bloomz-1b7-GGUF with Docker Model Runner:
```
docker model run hf.co/tensorblock/bloomz-1b7-GGUF:Q2_K
```

Lemonade

How to use tensorblock/bloomz-1b7-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull tensorblock/bloomz-1b7-GGUF:Q2_K

Run and chat with the model

lemonade run user.bloomz-1b7-GGUF-Q2_K

List all available models

lemonade list

bloomz-1b7-GGUF / README.md

morriszms

Update README.md

c079a93 verified 11 months ago

preview code

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	---
	datasets:
	- bigscience/xP3
	license: bigscience-bloom-rail-1.0
	language:
	- ak
	- ar
	- as
	- bm
	- bn
	- ca
	- code
	- en
	- es
	- eu
	- fon
	- fr
	- gu
	- hi
	- id
	- ig
	- ki
	- kn
	- lg
	- ln
	- ml
	- mr
	- ne
	- nso
	- ny
	- or
	- pa
	- pt
	- rn
	- rw
	- sn
	- st
	- sw
	- ta
	- te
	- tn
	- ts
	- tum
	- tw
	- ur
	- vi
	- wo
	- xh
	- yo
	- zh
	- zu
	programming_language:
	- C
	- C++
	- C#
	- Go
	- Java
	- JavaScript
	- Lua
	- PHP
	- Python
	- Ruby
	- Rust
	- Scala
	- TypeScript
	pipeline_tag: text-generation
	widget:
	- text: 一个传奇的开端，一个不灭的神话，这不仅仅是一部电影，而是作为一个走进新时代的标签，永远彪炳史册。Would you rate the previous
	review as positive, neutral or negative?
	example_title: zh-en sentiment
	- text: 一个传奇的开端，一个不灭的神话，这不仅仅是一部电影，而是作为一个走进新时代的标签，永远彪炳史册。你认为这句话的立场是赞扬、中立还是批评？
	example_title: zh-zh sentiment
	- text: Suggest at least five related search terms to "Mạng neural nhân tạo".
	example_title: vi-en query
	- text: Proposez au moins cinq mots clés concernant «Réseau de neurones artificiels».
	example_title: fr-fr query
	- text: Explain in a sentence in Telugu what is backpropagation in neural networks.
	example_title: te-en qa
	- text: Why is the sky blue?
	example_title: en-en qa
	- text: 'Write a fairy tale about a troll saving a princess from a dangerous dragon.
	The fairy tale is a masterpiece that has achieved praise worldwide and its moral
	is "Heroes Come in All Shapes and Sizes". Story (in Spanish):'
	example_title: es-en fable
	- text: 'Write a fable about wood elves living in a forest that is suddenly invaded
	by ogres. The fable is a masterpiece that has achieved praise worldwide and its
	moral is "Violence is the last refuge of the incompetent". Fable (in Hindi):'
	example_title: hi-en fable
	base_model: bigscience/bloomz-1b7
	tags:
	- TensorBlock
	- GGUF
	model-index:
	- name: bloomz-1b7
	results:
	- task:
	type: Coreference resolution
	dataset:
	name: Winogrande XL (xl)
	type: winogrande
	config: xl
	split: validation
	revision: a80f460359d1e9a67c006011c94de42a8759430c
	metrics:
	- type: Accuracy
	value: 51.14
	- task:
	type: Coreference resolution
	dataset:
	name: XWinograd (en)
	type: Muennighoff/xwinograd
	config: en
	split: test
	revision: 9dd5ea5505fad86b7bedad667955577815300cee
	metrics:
	- type: Accuracy
	value: 56.34
	- task:
	type: Coreference resolution
	dataset:
	name: XWinograd (fr)
	type: Muennighoff/xwinograd
	config: fr
	split: test
	revision: 9dd5ea5505fad86b7bedad667955577815300cee
	metrics:
	- type: Accuracy
	value: 55.42
	- task:
	type: Coreference resolution
	dataset:
	name: XWinograd (jp)
	type: Muennighoff/xwinograd
	config: jp
	split: test
	revision: 9dd5ea5505fad86b7bedad667955577815300cee
	metrics:
	- type: Accuracy
	value: 52.55
	- task:
	type: Coreference resolution
	dataset:
	name: XWinograd (pt)
	type: Muennighoff/xwinograd
	config: pt
	split: test
	revision: 9dd5ea5505fad86b7bedad667955577815300cee
	metrics:
	- type: Accuracy
	value: 53.23
	- task:
	type: Coreference resolution
	dataset:
	name: XWinograd (ru)
	type: Muennighoff/xwinograd
	config: ru
	split: test
	revision: 9dd5ea5505fad86b7bedad667955577815300cee
	metrics:
	- type: Accuracy
	value: 55.24
	- task:
	type: Coreference resolution
	dataset:
	name: XWinograd (zh)
	type: Muennighoff/xwinograd
	config: zh
	split: test
	revision: 9dd5ea5505fad86b7bedad667955577815300cee
	metrics:
	- type: Accuracy
	value: 56.15
	- task:
	type: Natural language inference
	dataset:
	name: ANLI (r1)
	type: anli
	config: r1
	split: validation
	revision: 9dbd830a06fea8b1c49d6e5ef2004a08d9f45094
	metrics:
	- type: Accuracy
	value: 34.0
	- task:
	type: Natural language inference
	dataset:
	name: ANLI (r2)
	type: anli
	config: r2
	split: validation
	revision: 9dbd830a06fea8b1c49d6e5ef2004a08d9f45094
	metrics:
	- type: Accuracy
	value: 36.1
	- task:
	type: Natural language inference
	dataset:
	name: ANLI (r3)
	type: anli
	config: r3
	split: validation
	revision: 9dbd830a06fea8b1c49d6e5ef2004a08d9f45094
	metrics:
	- type: Accuracy
	value: 37.08
	- task:
	type: Natural language inference
	dataset:
	name: SuperGLUE (cb)
	type: super_glue
	config: cb
	split: validation
	revision: 9e12063561e7e6c79099feb6d5a493142584e9e2
	metrics:
	- type: Accuracy
	value: 71.43
	- task:
	type: Natural language inference
	dataset:
	name: SuperGLUE (rte)
	type: super_glue
	config: rte
	split: validation
	revision: 9e12063561e7e6c79099feb6d5a493142584e9e2
	metrics:
	- type: Accuracy
	value: 76.17
	- task:
	type: Natural language inference
	dataset:
	name: XNLI (ar)
	type: xnli
	config: ar
	split: validation
	revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
	metrics:
	- type: Accuracy
	value: 50.04
	- task:
	type: Natural language inference
	dataset:
	name: XNLI (bg)
	type: xnli
	config: bg
	split: validation
	revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
	metrics:
	- type: Accuracy
	value: 42.17
	- task:
	type: Natural language inference
	dataset:
	name: XNLI (de)
	type: xnli
	config: de
	split: validation
	revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
	metrics:
	- type: Accuracy
	value: 42.73
	- task:
	type: Natural language inference
	dataset:
	name: XNLI (el)
	type: xnli
	config: el
	split: validation
	revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
	metrics:
	- type: Accuracy
	value: 41.81
	- task:
	type: Natural language inference
	dataset:
	name: XNLI (en)
	type: xnli
	config: en
	split: validation
	revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
	metrics:
	- type: Accuracy
	value: 55.02
	- task:
	type: Natural language inference
	dataset:
	name: XNLI (es)
	type: xnli
	config: es
	split: validation
	revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
	metrics:
	- type: Accuracy
	value: 52.97
	- task:
	type: Natural language inference
	dataset:
	name: XNLI (fr)
	type: xnli
	config: fr
	split: validation
	revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
	metrics:
	- type: Accuracy
	value: 52.21
	- task:
	type: Natural language inference
	dataset:
	name: XNLI (hi)
	type: xnli
	config: hi
	split: validation
	revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
	metrics:
	- type: Accuracy
	value: 48.07
	- task:
	type: Natural language inference
	dataset:
	name: XNLI (ru)
	type: xnli
	config: ru
	split: validation
	revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
	metrics:
	- type: Accuracy
	value: 45.1
	- task:
	type: Natural language inference
	dataset:
	name: XNLI (sw)
	type: xnli
	config: sw
	split: validation
	revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
	metrics:
	- type: Accuracy
	value: 44.34
	- task:
	type: Natural language inference
	dataset:
	name: XNLI (th)
	type: xnli
	config: th
	split: validation
	revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
	metrics:
	- type: Accuracy
	value: 40.36
	- task:
	type: Natural language inference
	dataset:
	name: XNLI (tr)
	type: xnli
	config: tr
	split: validation
	revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
	metrics:
	- type: Accuracy
	value: 37.15
	- task:
	type: Natural language inference
	dataset:
	name: XNLI (ur)
	type: xnli
	config: ur
	split: validation
	revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
	metrics:
	- type: Accuracy
	value: 44.38
	- task:
	type: Natural language inference
	dataset:
	name: XNLI (vi)
	type: xnli
	config: vi
	split: validation
	revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
	metrics:
	- type: Accuracy
	value: 51.08
	- task:
	type: Natural language inference
	dataset:
	name: XNLI (zh)
	type: xnli
	config: zh
	split: validation
	revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
	metrics:
	- type: Accuracy
	value: 51.12
	- task:
	type: Program synthesis
	dataset:
	name: HumanEval
	type: openai_humaneval
	config: None
	split: test
	revision: e8dc562f5de170c54b5481011dd9f4fa04845771
	metrics:
	- type: Pass@1
	value: 4.38
	- type: Pass@10
	value: 8.73
	- type: Pass@100
	value: 16.09
	- task:
	type: Sentence completion
	dataset:
	name: StoryCloze (2016)
	type: story_cloze
	config: '2016'
	split: validation
	revision: e724c6f8cdf7c7a2fb229d862226e15b023ee4db
	metrics:
	- type: Accuracy
	value: 82.9
	- task:
	type: Sentence completion
	dataset:
	name: SuperGLUE (copa)
	type: super_glue
	config: copa
	split: validation
	revision: 9e12063561e7e6c79099feb6d5a493142584e9e2
	metrics:
	- type: Accuracy
	value: 69.0
	- task:
	type: Sentence completion
	dataset:
	name: XCOPA (et)
	type: xcopa
	config: et
	split: validation
	revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
	metrics:
	- type: Accuracy
	value: 50.0
	- task:
	type: Sentence completion
	dataset:
	name: XCOPA (ht)
	type: xcopa
	config: ht
	split: validation
	revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
	metrics:
	- type: Accuracy
	value: 54.0
	- task:
	type: Sentence completion
	dataset:
	name: XCOPA (id)
	type: xcopa
	config: id
	split: validation
	revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
	metrics:
	- type: Accuracy
	value: 61.0
	- task:
	type: Sentence completion
	dataset:
	name: XCOPA (it)
	type: xcopa
	config: it
	split: validation
	revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
	metrics:
	- type: Accuracy
	value: 49.0
	- task:
	type: Sentence completion
	dataset:
	name: XCOPA (qu)
	type: xcopa
	config: qu
	split: validation
	revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
	metrics:
	- type: Accuracy
	value: 56.0
	- task:
	type: Sentence completion
	dataset:
	name: XCOPA (sw)
	type: xcopa
	config: sw
	split: validation
	revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
	metrics:
	- type: Accuracy
	value: 57.0
	- task:
	type: Sentence completion
	dataset:
	name: XCOPA (ta)
	type: xcopa
	config: ta
	split: validation
	revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
	metrics:
	- type: Accuracy
	value: 56.0
	- task:
	type: Sentence completion
	dataset:
	name: XCOPA (th)
	type: xcopa
	config: th
	split: validation
	revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
	metrics:
	- type: Accuracy
	value: 60.0
	- task:
	type: Sentence completion
	dataset:
	name: XCOPA (tr)
	type: xcopa
	config: tr
	split: validation
	revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
	metrics:
	- type: Accuracy
	value: 59.0
	- task:
	type: Sentence completion
	dataset:
	name: XCOPA (vi)
	type: xcopa
	config: vi
	split: validation
	revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
	metrics:
	- type: Accuracy
	value: 70.0
	- task:
	type: Sentence completion
	dataset:
	name: XCOPA (zh)
	type: xcopa
	config: zh
	split: validation
	revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
	metrics:
	- type: Accuracy
	value: 67.0
	- task:
	type: Sentence completion
	dataset:
	name: XStoryCloze (ar)
	type: Muennighoff/xstory_cloze
	config: ar
	split: validation
	revision: 8bb76e594b68147f1a430e86829d07189622b90d
	metrics:
	- type: Accuracy
	value: 73.33
	- task:
	type: Sentence completion
	dataset:
	name: XStoryCloze (es)
	type: Muennighoff/xstory_cloze
	config: es
	split: validation
	revision: 8bb76e594b68147f1a430e86829d07189622b90d
	metrics:
	- type: Accuracy
	value: 77.96
	- task:
	type: Sentence completion
	dataset:
	name: XStoryCloze (eu)
	type: Muennighoff/xstory_cloze
	config: eu
	split: validation
	revision: 8bb76e594b68147f1a430e86829d07189622b90d
	metrics:
	- type: Accuracy
	value: 60.49
	- task:
	type: Sentence completion
	dataset:
	name: XStoryCloze (hi)
	type: Muennighoff/xstory_cloze
	config: hi
	split: validation
	revision: 8bb76e594b68147f1a430e86829d07189622b90d
	metrics:
	- type: Accuracy
	value: 72.87
	- task:
	type: Sentence completion
	dataset:
	name: XStoryCloze (id)
	type: Muennighoff/xstory_cloze
	config: id
	split: validation
	revision: 8bb76e594b68147f1a430e86829d07189622b90d
	metrics:
	- type: Accuracy
	value: 74.92
	- task:
	type: Sentence completion
	dataset:
	name: XStoryCloze (my)
	type: Muennighoff/xstory_cloze
	config: my
	split: validation
	revision: 8bb76e594b68147f1a430e86829d07189622b90d
	metrics:
	- type: Accuracy
	value: 51.09
	- task:
	type: Sentence completion
	dataset:
	name: XStoryCloze (ru)
	type: Muennighoff/xstory_cloze
	config: ru
	split: validation
	revision: 8bb76e594b68147f1a430e86829d07189622b90d
	metrics:
	- type: Accuracy
	value: 56.39
	- task:
	type: Sentence completion
	dataset:
	name: XStoryCloze (sw)
	type: Muennighoff/xstory_cloze
	config: sw
	split: validation
	revision: 8bb76e594b68147f1a430e86829d07189622b90d
	metrics:
	- type: Accuracy
	value: 61.28
	- task:
	type: Sentence completion
	dataset:
	name: XStoryCloze (te)
	type: Muennighoff/xstory_cloze
	config: te
	split: validation
	revision: 8bb76e594b68147f1a430e86829d07189622b90d
	metrics:
	- type: Accuracy
	value: 66.25
	- task:
	type: Sentence completion
	dataset:
	name: XStoryCloze (zh)
	type: Muennighoff/xstory_cloze
	config: zh
	split: validation
	revision: 8bb76e594b68147f1a430e86829d07189622b90d
	metrics:
	- type: Accuracy
	value: 78.69
	---

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	## bigscience/bloomz-1b7 - GGUF

	This repo contains GGUF format model files for [bigscience/bloomz-1b7](https://huggingface.co/bigscience/bloomz-1b7).

	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).


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	## Prompt template


	```

	```

	## Model file specification

	\| Filename \| Quant type \| File Size \| Description \|
	\| -------- \| ---------- \| --------- \| ----------- \|
	\| [bloomz-1b7-Q2_K.gguf](https://huggingface.co/tensorblock/bloomz-1b7-GGUF/blob/main/bloomz-1b7-Q2_K.gguf) \| Q2_K \| 0.980 GB \| smallest, significant quality loss - not recommended for most purposes \|
	\| [bloomz-1b7-Q3_K_S.gguf](https://huggingface.co/tensorblock/bloomz-1b7-GGUF/blob/main/bloomz-1b7-Q3_K_S.gguf) \| Q3_K_S \| 1.096 GB \| very small, high quality loss \|
	\| [bloomz-1b7-Q3_K_M.gguf](https://huggingface.co/tensorblock/bloomz-1b7-GGUF/blob/main/bloomz-1b7-Q3_K_M.gguf) \| Q3_K_M \| 1.197 GB \| very small, high quality loss \|
	\| [bloomz-1b7-Q3_K_L.gguf](https://huggingface.co/tensorblock/bloomz-1b7-GGUF/blob/main/bloomz-1b7-Q3_K_L.gguf) \| Q3_K_L \| 1.254 GB \| small, substantial quality loss \|
	\| [bloomz-1b7-Q4_0.gguf](https://huggingface.co/tensorblock/bloomz-1b7-GGUF/blob/main/bloomz-1b7-Q4_0.gguf) \| Q4_0 \| 1.309 GB \| legacy; small, very high quality loss - prefer using Q3_K_M \|
	\| [bloomz-1b7-Q4_K_S.gguf](https://huggingface.co/tensorblock/bloomz-1b7-GGUF/blob/main/bloomz-1b7-Q4_K_S.gguf) \| Q4_K_S \| 1.315 GB \| small, greater quality loss \|
	\| [bloomz-1b7-Q4_K_M.gguf](https://huggingface.co/tensorblock/bloomz-1b7-GGUF/blob/main/bloomz-1b7-Q4_K_M.gguf) \| Q4_K_M \| 1.392 GB \| medium, balanced quality - recommended \|
	\| [bloomz-1b7-Q5_0.gguf](https://huggingface.co/tensorblock/bloomz-1b7-GGUF/blob/main/bloomz-1b7-Q5_0.gguf) \| Q5_0 \| 1.509 GB \| legacy; medium, balanced quality - prefer using Q4_K_M \|
	\| [bloomz-1b7-Q5_K_S.gguf](https://huggingface.co/tensorblock/bloomz-1b7-GGUF/blob/main/bloomz-1b7-Q5_K_S.gguf) \| Q5_K_S \| 1.509 GB \| large, low quality loss - recommended \|
	\| [bloomz-1b7-Q5_K_M.gguf](https://huggingface.co/tensorblock/bloomz-1b7-GGUF/blob/main/bloomz-1b7-Q5_K_M.gguf) \| Q5_K_M \| 1.571 GB \| large, very low quality loss - recommended \|
	\| [bloomz-1b7-Q6_K.gguf](https://huggingface.co/tensorblock/bloomz-1b7-GGUF/blob/main/bloomz-1b7-Q6_K.gguf) \| Q6_K \| 1.722 GB \| very large, extremely low quality loss \|
	\| [bloomz-1b7-Q8_0.gguf](https://huggingface.co/tensorblock/bloomz-1b7-GGUF/blob/main/bloomz-1b7-Q8_0.gguf) \| Q8_0 \| 2.226 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/bloomz-1b7-GGUF --include "bloomz-1b7-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/bloomz-1b7-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='Q4_Kgguf'
	```