Instructions to use marcelone/Llama-3.1-Swallow-8B-Instruct-v0.5-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use marcelone/Llama-3.1-Swallow-8B-Instruct-v0.5-gguf with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf marcelone/Llama-3.1-Swallow-8B-Instruct-v0.5-gguf:BF16
# Run inference directly in the terminal:
llama cli -hf marcelone/Llama-3.1-Swallow-8B-Instruct-v0.5-gguf:BF16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf marcelone/Llama-3.1-Swallow-8B-Instruct-v0.5-gguf:BF16
# Run inference directly in the terminal:
llama cli -hf marcelone/Llama-3.1-Swallow-8B-Instruct-v0.5-gguf:BF16

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 marcelone/Llama-3.1-Swallow-8B-Instruct-v0.5-gguf:BF16
# Run inference directly in the terminal:
./llama-cli -hf marcelone/Llama-3.1-Swallow-8B-Instruct-v0.5-gguf:BF16

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 marcelone/Llama-3.1-Swallow-8B-Instruct-v0.5-gguf:BF16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf marcelone/Llama-3.1-Swallow-8B-Instruct-v0.5-gguf:BF16

Use Docker

docker model run hf.co/marcelone/Llama-3.1-Swallow-8B-Instruct-v0.5-gguf:BF16

LM Studio
Jan
Ollama
How to use marcelone/Llama-3.1-Swallow-8B-Instruct-v0.5-gguf with Ollama:
```
ollama run hf.co/marcelone/Llama-3.1-Swallow-8B-Instruct-v0.5-gguf:BF16
```
Unsloth Desktop
Docker Model Runner
How to use marcelone/Llama-3.1-Swallow-8B-Instruct-v0.5-gguf with Docker Model Runner:
```
docker model run hf.co/marcelone/Llama-3.1-Swallow-8B-Instruct-v0.5-gguf:BF16
```

Lemonade

How to use marcelone/Llama-3.1-Swallow-8B-Instruct-v0.5-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull marcelone/Llama-3.1-Swallow-8B-Instruct-v0.5-gguf:BF16

Run and chat with the model

lemonade run user.Llama-3.1-Swallow-8B-Instruct-v0.5-gguf-BF16

List all available models

lemonade list

Atomic Chat

Swallow-8B-it-v05-gguf-q6_k-mixed-v1

Quantization Type: Mixed Precision (q5_K, q6_K, q8_0)
Bits Per Weight (BPW): 7.13

Swallow-8B-it-v05-gguf-q6_k-mixed-v2

Quantization Type: Mixed Precision (q6_K, q8_0)
Bits Per Weight (BPW): 7.50

Swallow-8B-it-v05-gguf-q8_0-mixed-v1

Quantization Type: Mixed Precision (bf16, q4_K, q5_K, q6_K, q8_0)
Bits Per Weight (BPW): 8.01

Swallow-8B-it-v05-gguf-q8_0-mixed-v2

Quantization Type: Mixed Precision (bf16, q5_K, q6_K, q8_0)
Bits Per Weight (BPW): 9.31

Swallow-8B-it-v05-gguf-q8_0-mixed-v3

Quantization Type: Mixed Precision (bf16, q6_K, q8_0)
Bits Per Weight (BPW): 11.44

Swallow-8B-it-v05-gguf-q8_0-mixed-v4

Quantization Type: Mixed Precision (bf16, q8_0)
Bits Per Weight (BPW): 13.38

Downloads last month: 117

GGUF

Model size

8B params

Architecture

llama

Hardware compatibility

6-bit

8-bit

16-bit

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Model tree for marcelone/Llama-3.1-Swallow-8B-Instruct-v0.5-gguf

Base model

meta-llama/Llama-3.1-8B

Finetuned

meta-llama/Llama-3.1-8B-Instruct

Finetuned

tokyotech-llm/Llama-3.1-Swallow-8B-v0.5

Finetuned

tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.5

Quantized

(14)

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