Instructions to use tensorblock/Llama3-Aloe-8B-Alpha-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tensorblock/Llama3-Aloe-8B-Alpha-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("question-answering", model="tensorblock/Llama3-Aloe-8B-Alpha-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("tensorblock/Llama3-Aloe-8B-Alpha-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use tensorblock/Llama3-Aloe-8B-Alpha-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 tensorblock/Llama3-Aloe-8B-Alpha-GGUF:Q2_K
# Run inference directly in the terminal:
llama cli -hf tensorblock/Llama3-Aloe-8B-Alpha-GGUF:Q2_K

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf tensorblock/Llama3-Aloe-8B-Alpha-GGUF:Q2_K
# Run inference directly in the terminal:
llama cli -hf tensorblock/Llama3-Aloe-8B-Alpha-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/Llama3-Aloe-8B-Alpha-GGUF:Q2_K
# Run inference directly in the terminal:
./llama-cli -hf tensorblock/Llama3-Aloe-8B-Alpha-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/Llama3-Aloe-8B-Alpha-GGUF:Q2_K
# Run inference directly in the terminal:
./build/bin/llama-cli -hf tensorblock/Llama3-Aloe-8B-Alpha-GGUF:Q2_K

Use Docker

docker model run hf.co/tensorblock/Llama3-Aloe-8B-Alpha-GGUF:Q2_K

LM Studio
Jan
Ollama
How to use tensorblock/Llama3-Aloe-8B-Alpha-GGUF with Ollama:
```
ollama run hf.co/tensorblock/Llama3-Aloe-8B-Alpha-GGUF:Q2_K
```
Unsloth Desktop
Docker Model Runner
How to use tensorblock/Llama3-Aloe-8B-Alpha-GGUF with Docker Model Runner:
```
docker model run hf.co/tensorblock/Llama3-Aloe-8B-Alpha-GGUF:Q2_K
```

Lemonade

How to use tensorblock/Llama3-Aloe-8B-Alpha-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull tensorblock/Llama3-Aloe-8B-Alpha-GGUF:Q2_K

Run and chat with the model

lemonade run user.Llama3-Aloe-8B-Alpha-GGUF-Q2_K

List all available models

lemonade list

Atomic Chat

HPAI-BSC/Llama3-Aloe-8B-Alpha - GGUF

This repo contains GGUF format model files for HPAI-BSC/Llama3-Aloe-8B-Alpha.

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

<|begin_of_text|><|start_header_id|>system<|end_header_id|>

{system_prompt}<|eot_id|><|start_header_id|>user<|end_header_id|>

{prompt}<|eot_id|><|start_header_id|>assistant<|end_header_id|>

Model file specification

Filename	Quant type	File Size	Description
Llama3-Aloe-8B-Alpha-Q2_K.gguf	Q2_K	2.961 GB	smallest, significant quality loss - not recommended for most purposes
Llama3-Aloe-8B-Alpha-Q3_K_S.gguf	Q3_K_S	3.413 GB	very small, high quality loss
Llama3-Aloe-8B-Alpha-Q3_K_M.gguf	Q3_K_M	3.743 GB	very small, high quality loss
Llama3-Aloe-8B-Alpha-Q3_K_L.gguf	Q3_K_L	4.025 GB	small, substantial quality loss
Llama3-Aloe-8B-Alpha-Q4_0.gguf	Q4_0	4.341 GB	legacy; small, very high quality loss - prefer using Q3_K_M
Llama3-Aloe-8B-Alpha-Q4_K_S.gguf	Q4_K_S	4.370 GB	small, greater quality loss
Llama3-Aloe-8B-Alpha-Q4_K_M.gguf	Q4_K_M	4.583 GB	medium, balanced quality - recommended
Llama3-Aloe-8B-Alpha-Q5_0.gguf	Q5_0	5.215 GB	legacy; medium, balanced quality - prefer using Q4_K_M
Llama3-Aloe-8B-Alpha-Q5_K_S.gguf	Q5_K_S	5.215 GB	large, low quality loss - recommended
Llama3-Aloe-8B-Alpha-Q5_K_M.gguf	Q5_K_M	5.339 GB	large, very low quality loss - recommended
Llama3-Aloe-8B-Alpha-Q6_K.gguf	Q6_K	6.143 GB	very large, extremely low quality loss
Llama3-Aloe-8B-Alpha-Q8_0.gguf	Q8_0	7.954 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/Llama3-Aloe-8B-Alpha-GGUF --include "Llama3-Aloe-8B-Alpha-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/Llama3-Aloe-8B-Alpha-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'

Downloads last month: 54

GGUF

Model size

8B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

Model tree for tensorblock/Llama3-Aloe-8B-Alpha-GGUF

Base model

HPAI-BSC/Llama3-Aloe-8B-Alpha

Quantized

(4)

this model

tensorblock
/

Llama3-Aloe-8B-Alpha-GGUF