Instructions to use tensorblock/bloomz-3b-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tensorblock/bloomz-3b-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="tensorblock/bloomz-3b-GGUF",
	filename="bloomz-3b-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-3b-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-3b-GGUF:Q2_K
# Run inference directly in the terminal:
llama-cli -hf tensorblock/bloomz-3b-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-3b-GGUF:Q2_K
# Run inference directly in the terminal:
llama-cli -hf tensorblock/bloomz-3b-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-3b-GGUF:Q2_K
# Run inference directly in the terminal:
./llama-cli -hf tensorblock/bloomz-3b-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-3b-GGUF:Q2_K
# Run inference directly in the terminal:
./build/bin/llama-cli -hf tensorblock/bloomz-3b-GGUF:Q2_K

Use Docker

docker model run hf.co/tensorblock/bloomz-3b-GGUF:Q2_K

LM Studio
Jan

vLLM

How to use tensorblock/bloomz-3b-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tensorblock/bloomz-3b-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-3b-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/tensorblock/bloomz-3b-GGUF:Q2_K

Ollama
How to use tensorblock/bloomz-3b-GGUF with Ollama:
```
ollama run hf.co/tensorblock/bloomz-3b-GGUF:Q2_K
```

Unsloth Studio new

How to use tensorblock/bloomz-3b-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-3b-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-3b-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-3b-GGUF to start chatting

Docker Model Runner
How to use tensorblock/bloomz-3b-GGUF with Docker Model Runner:
```
docker model run hf.co/tensorblock/bloomz-3b-GGUF:Q2_K
```

Lemonade

How to use tensorblock/bloomz-3b-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull tensorblock/bloomz-3b-GGUF:Q2_K

Run and chat with the model

lemonade run user.bloomz-3b-GGUF-Q2_K

List all available models

lemonade list

bigscience/bloomz-3b - GGUF

This repo contains GGUF format model files for bigscience/bloomz-3b.

The files were quantized using machines provided by TensorBlock, and they are compatible with llama.cpp as of commit b4011.

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## Prompt template

Model file specification

Filename	Quant type	File Size	Description
bloomz-3b-Q2_K.gguf	Q2_K	1.516 GB	smallest, significant quality loss - not recommended for most purposes
bloomz-3b-Q3_K_S.gguf	Q3_K_S	1.707 GB	very small, high quality loss
bloomz-3b-Q3_K_M.gguf	Q3_K_M	1.905 GB	very small, high quality loss
bloomz-3b-Q3_K_L.gguf	Q3_K_L	2.016 GB	small, substantial quality loss
bloomz-3b-Q4_0.gguf	Q4_0	2.079 GB	legacy; small, very high quality loss - prefer using Q3_K_M
bloomz-3b-Q4_K_S.gguf	Q4_K_S	2.088 GB	small, greater quality loss
bloomz-3b-Q4_K_M.gguf	Q4_K_M	2.235 GB	medium, balanced quality - recommended
bloomz-3b-Q5_0.gguf	Q5_0	2.428 GB	legacy; medium, balanced quality - prefer using Q4_K_M
bloomz-3b-Q5_K_S.gguf	Q5_K_S	2.428 GB	large, low quality loss - recommended
bloomz-3b-Q5_K_M.gguf	Q5_K_M	2.546 GB	large, very low quality loss - recommended
bloomz-3b-Q6_K.gguf	Q6_K	2.799 GB	very large, extremely low quality loss
bloomz-3b-Q8_0.gguf	Q8_0	3.621 GB	very large, extremely low quality loss - not recommended

Downloading instruction

Command line

Firstly, install Huggingface Client

pip install -U "huggingface_hub[cli]"

Then, downoad the individual model file the a local directory

huggingface-cli download tensorblock/bloomz-3b-GGUF --include "bloomz-3b-Q2_K.gguf" --local-dir MY_LOCAL_DIR

If you wanna download multiple model files with a pattern (e.g., *Q4_K*gguf), you can try:

huggingface-cli download tensorblock/bloomz-3b-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'

Downloads last month: 149

GGUF

Model size

4B params

Architecture

bloom

Hardware compatibility

2-bit

3-bit

Model tree for tensorblock/bloomz-3b-GGUF

Base model

bigscience/bloomz-3b

Quantized

(6)

this model

Dataset used to train tensorblock/bloomz-3b-GGUF

Evaluation results

Accuracy on Winogrande XL (xl)
validation set self-reported

53.670
Accuracy on XWinograd (en)
test set self-reported

59.230
Accuracy on XWinograd (fr)
test set self-reported

53.010
Accuracy on XWinograd (jp)
test set self-reported

52.450
Accuracy on XWinograd (pt)
test set self-reported

53.610
Accuracy on XWinograd (ru)
test set self-reported

53.970
Accuracy on XWinograd (zh)
test set self-reported

60.910
Accuracy on ANLI (r1)
validation set self-reported

40.100
Accuracy on ANLI (r2)
validation set self-reported

36.800
Accuracy on ANLI (r3)
validation set self-reported

40.000
Accuracy on SuperGLUE (cb)
validation set self-reported

75.000
Accuracy on SuperGLUE (rte)
validation set self-reported

76.170
Accuracy on XNLI (ar)
validation set self-reported

53.290
Accuracy on XNLI (bg)
validation set self-reported

43.820
Accuracy on XNLI (de)
validation set self-reported

45.260
Accuracy on XNLI (el)
validation set self-reported

42.610