Instructions to use QuantFactory/StockLLama-3.1-8B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantFactory/StockLLama-3.1-8B-GGUF with Transformers:

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("QuantFactory/StockLLama-3.1-8B-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use QuantFactory/StockLLama-3.1-8B-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 QuantFactory/StockLLama-3.1-8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf QuantFactory/StockLLama-3.1-8B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf QuantFactory/StockLLama-3.1-8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf QuantFactory/StockLLama-3.1-8B-GGUF:Q4_K_M

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 QuantFactory/StockLLama-3.1-8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/StockLLama-3.1-8B-GGUF:Q4_K_M

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 QuantFactory/StockLLama-3.1-8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/StockLLama-3.1-8B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/StockLLama-3.1-8B-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use QuantFactory/StockLLama-3.1-8B-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/StockLLama-3.1-8B-GGUF:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use QuantFactory/StockLLama-3.1-8B-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/StockLLama-3.1-8B-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/StockLLama-3.1-8B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/StockLLama-3.1-8B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.StockLLama-3.1-8B-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

QuantFactory/StockLLama-3.1-8B-GGUF

This is quantized version of bunnycore/StockLLama-3.1-8B created using llama.cpp

Original Model Card

merge

This is a merge of pre-trained language models created using mergekit.

Merge Details

Merge Method

This model was merged using the Model Stock merge method using unsloth/Meta-Llama-3.1-8B as a base.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:


models:
 - model: vicgalle/Configurable-Llama-3.1-8B-Instruct
 - model: mlabonne/Hermes-3-Llama-3.1-8B-lorablated
 - model: Orenguteng/Llama-3.1-8B-Lexi-Uncensored-V2
 - model: bunnycore/LLama-3.1-Hyper-Stock
 - model: bunnycore/HyperLexi-8B-breadcrumbs
 - model: Replete-AI/Replete-LLM-V2-Llama-3.1-8b
 - model: rityak/L3.1-IFEvalStock-8B
merge_method: model_stock
base_model: unsloth/Meta-Llama-3.1-8B
dtype: bfloat16