Instructions to use 893790183LZW/bloom-1b7-Q3_K_S-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use 893790183LZW/bloom-1b7-Q3_K_S-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="893790183LZW/bloom-1b7-Q3_K_S-GGUF",
	filename="bloom-1b7-q3_k_s.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use 893790183LZW/bloom-1b7-Q3_K_S-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf 893790183LZW/bloom-1b7-Q3_K_S-GGUF:Q3_K_S
# Run inference directly in the terminal:
llama-cli -hf 893790183LZW/bloom-1b7-Q3_K_S-GGUF:Q3_K_S

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf 893790183LZW/bloom-1b7-Q3_K_S-GGUF:Q3_K_S
# Run inference directly in the terminal:
llama-cli -hf 893790183LZW/bloom-1b7-Q3_K_S-GGUF:Q3_K_S

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 893790183LZW/bloom-1b7-Q3_K_S-GGUF:Q3_K_S
# Run inference directly in the terminal:
./llama-cli -hf 893790183LZW/bloom-1b7-Q3_K_S-GGUF:Q3_K_S

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 893790183LZW/bloom-1b7-Q3_K_S-GGUF:Q3_K_S
# Run inference directly in the terminal:
./build/bin/llama-cli -hf 893790183LZW/bloom-1b7-Q3_K_S-GGUF:Q3_K_S

Use Docker

docker model run hf.co/893790183LZW/bloom-1b7-Q3_K_S-GGUF:Q3_K_S

LM Studio
Jan

vLLM

How to use 893790183LZW/bloom-1b7-Q3_K_S-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "893790183LZW/bloom-1b7-Q3_K_S-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "893790183LZW/bloom-1b7-Q3_K_S-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/893790183LZW/bloom-1b7-Q3_K_S-GGUF:Q3_K_S

Ollama
How to use 893790183LZW/bloom-1b7-Q3_K_S-GGUF with Ollama:
```
ollama run hf.co/893790183LZW/bloom-1b7-Q3_K_S-GGUF:Q3_K_S
```

Unsloth Studio

How to use 893790183LZW/bloom-1b7-Q3_K_S-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 893790183LZW/bloom-1b7-Q3_K_S-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 893790183LZW/bloom-1b7-Q3_K_S-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for 893790183LZW/bloom-1b7-Q3_K_S-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use 893790183LZW/bloom-1b7-Q3_K_S-GGUF with Docker Model Runner:
```
docker model run hf.co/893790183LZW/bloom-1b7-Q3_K_S-GGUF:Q3_K_S
```

Lemonade

How to use 893790183LZW/bloom-1b7-Q3_K_S-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull 893790183LZW/bloom-1b7-Q3_K_S-GGUF:Q3_K_S

Run and chat with the model

lemonade run user.bloom-1b7-Q3_K_S-GGUF-Q3_K_S

List all available models

lemonade list

bloom-1b7-Q3_K_S-GGUF / README.md

893790183LZW

Upload README.md with huggingface_hub

dba97f7 verified about 1 year ago

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	---
	base_model: bigscience/bloom-1b7
	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
	- zhs
	- zht
	- zu
	license: bigscience-bloom-rail-1.0
	pipeline_tag: text-generation
	tags:
	- llama-cpp
	- gguf-my-repo
	---

	# 893790183LZW/bloom-1b7-Q3_K_S-GGUF
	This model was converted to GGUF format from [`bigscience/bloom-1b7`](https://huggingface.co/bigscience/bloom-1b7) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
	Refer to the [original model card](https://huggingface.co/bigscience/bloom-1b7) for more details on the model.

	## Use with llama.cpp
	Install llama.cpp through brew (works on Mac and Linux)

	```bash
	brew install llama.cpp

	```
	Invoke the llama.cpp server or the CLI.

	### CLI:
	```bash
	llama-cli --hf-repo 893790183LZW/bloom-1b7-Q3_K_S-GGUF --hf-file bloom-1b7-q3_k_s.gguf -p "The meaning to life and the universe is"
	```

	### Server:
	```bash
	llama-server --hf-repo 893790183LZW/bloom-1b7-Q3_K_S-GGUF --hf-file bloom-1b7-q3_k_s.gguf -c 2048
	```

	Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.

	Step 1: Clone llama.cpp from GitHub.
	```
	git clone https://github.com/ggerganov/llama.cpp
	```

	Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
	```
	cd llama.cpp && LLAMA_CURL=1 make
	```

	Step 3: Run inference through the main binary.
	```
	./llama-cli --hf-repo 893790183LZW/bloom-1b7-Q3_K_S-GGUF --hf-file bloom-1b7-q3_k_s.gguf -p "The meaning to life and the universe is"
	```
	or
	```
	./llama-server --hf-repo 893790183LZW/bloom-1b7-Q3_K_S-GGUF --hf-file bloom-1b7-q3_k_s.gguf -c 2048
	```