Instructions to use QuantFactory/Aspire1.2-8B-TIES-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantFactory/Aspire1.2-8B-TIES-GGUF with Transformers:

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("QuantFactory/Aspire1.2-8B-TIES-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use QuantFactory/Aspire1.2-8B-TIES-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/Aspire1.2-8B-TIES-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf QuantFactory/Aspire1.2-8B-TIES-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/Aspire1.2-8B-TIES-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf QuantFactory/Aspire1.2-8B-TIES-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/Aspire1.2-8B-TIES-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/Aspire1.2-8B-TIES-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/Aspire1.2-8B-TIES-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/Aspire1.2-8B-TIES-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/Aspire1.2-8B-TIES-GGUF:Q4_K_M

Llama new
LM Studio
Jan
Ollama
How to use QuantFactory/Aspire1.2-8B-TIES-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/Aspire1.2-8B-TIES-GGUF:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use QuantFactory/Aspire1.2-8B-TIES-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/Aspire1.2-8B-TIES-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/Aspire1.2-8B-TIES-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/Aspire1.2-8B-TIES-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Aspire1.2-8B-TIES-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

QuantFactory/Aspire1.2-8B-TIES-GGUF

This is quantized version of DreadPoor/Aspire1.2-8B-TIES 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 TIES merge method using NousResearch/Meta-Llama-3-8B 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: Orenguteng/Llama-3.1-8B-Lexi-Uncensored-V2+kloodia/lora-8b-bio
    parameters:
      weight: 1
  - model: arcee-ai/Llama-3.1-SuperNova-Lite+Blackroot/Llama3-RP-Lora
    parameters:
      weight: 1
  - model: NousResearch/Hermes-3-Llama-3.1-8B+kloodia/lora-8b-physic
    parameters:
      weight: 1
  - model: cgato/L3-TheSpice-8b-v0.8.3+kloodia/lora-8b-medic
    parameters:
      weight: 1
  - model: ArliAI/Llama-3.1-8B-ArliAI-RPMax-v1.1+Blackroot/Llama-3-8B-Abomination-LORA
    parameters:
      weight: 1
  - model: DreadPoor/Nothing_to_see_here_-_Move_along+hikikomoriHaven/llama3-8b-hikikomori-v0.4
    parameters:
      weight: 1

merge_method: ties
base_model: NousResearch/Meta-Llama-3-8B
parameters:
  density: 1
  normalize: true
  int8_mask: true
dtype: bfloat16