Instructions to use QuantFactory/L3.1-Storniitova-8B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantFactory/L3.1-Storniitova-8B-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("QuantFactory/L3.1-Storniitova-8B-GGUF", dtype="auto", device_map="auto")

llama-cpp-python

How to use QuantFactory/L3.1-Storniitova-8B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="QuantFactory/L3.1-Storniitova-8B-GGUF",
	filename="L3.1-Storniitova-8B.Q2_K.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use QuantFactory/L3.1-Storniitova-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/L3.1-Storniitova-8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf QuantFactory/L3.1-Storniitova-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/L3.1-Storniitova-8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf QuantFactory/L3.1-Storniitova-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/L3.1-Storniitova-8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/L3.1-Storniitova-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/L3.1-Storniitova-8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/L3.1-Storniitova-8B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/L3.1-Storniitova-8B-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use QuantFactory/L3.1-Storniitova-8B-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/L3.1-Storniitova-8B-GGUF:Q4_K_M
```

Unsloth Studio

How to use QuantFactory/L3.1-Storniitova-8B-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 QuantFactory/L3.1-Storniitova-8B-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 QuantFactory/L3.1-Storniitova-8B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for QuantFactory/L3.1-Storniitova-8B-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use QuantFactory/L3.1-Storniitova-8B-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/L3.1-Storniitova-8B-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/L3.1-Storniitova-8B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/L3.1-Storniitova-8B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.L3.1-Storniitova-8B-GGUF-Q4_K_M

List all available models

lemonade list

QuantFactory/L3.1-Storniitova-8B-GGUF

This is quantized version of v000000/L3.1-Storniitova-8B created using llama.cpp

Original Model Card

Llama-3.1-Storniitova-8B

Storniitova-8B is a RP/Instruct model built on the foundation of Llama-3.1-SuperNova-Lite, which is distilled from the 405B parameter variant of Llama-3.1

By only changing the vector tasks, I attempt to retain the full 405B distillation while learning roleplaying capabilties.

(GGUF) mradermacher quants:

merge

This is a merge of pre-trained language models created using mergekit and other proprietary tools.

Merge Details

Merge Method

This model was merged using the SLERP, Task_Arithmetic and NEARSWAP merge method.

Models Merged

The following models were included in the merge:

Recipe

The following YAML configuration was used to produce this model:

#Step1 - Add smarts to Niitama with alchemonaut's algorithm.

slices:
  - sources:
      - model: Sao10K/L3.1-8B-Niitama-v1.1+grimjim/Llama-3-Instruct-abliteration-LoRA-8B
        layer_range: [0, 32]
      - model: akjindal53244/Llama-3.1-Storm-8B
        layer_range: [0, 32]
merge_method: nearswap
base_model: Sao10K/L3.1-8B-Niitama-v1.1+grimjim/Llama-3-Instruct-abliteration-LoRA-8B
parameters:
  t:
    - value: 0.0001
dtype: bfloat16
out_type: float16

#Step 2 - Learn vectors onto Supernova 0.4(Niitorm)

models:
  - model: arcee-ai/Llama-3.1-SuperNova-Lite
    parameters:
      weight: 1.0
  - model: v000000/L3.1-Niitorm-8B-t0.0001
    parameters:
      weight: 0.4
merge_method: task_arithmetic
base_model: arcee-ai/Llama-3.1-SuperNova-Lite
parameters:
    normalize: false
dtype: float16

#Step 3 - Fully learn vectors onto Supernova 1.25(Niitorm)

models:
  - model: arcee-ai/Llama-3.1-SuperNova-Lite
    parameters:
      weight: 0.0
  - model: v000000/L3.1-Niitorm-8B-t0.0001
    parameters:
      weight: 1.25
merge_method: task_arithmetic
base_model: arcee-ai/Llama-3.1-SuperNova-Lite
parameters:
    normalize: false
dtype: float16

#Step 4 - Merge checkpoints and keep output/input Supernova heavy
#Merge with a triangular slerp from sophosympatheia.

models:
  - model: v000000/L3.1-8B-RP-Test-003-Task_Arithmetic
merge_method: slerp
base_model: v000000/L3.1-8B-RP-Test-002-Task_Arithmetic+grimjim/Llama-3-Instruct-abliteration-LoRA-8B
# This model needed some abliteration^
parameters:
  t:
    - value: [0, 0, 0.3, 0.4, 0.5, 0.6, 0.5, 0.4, 0.3, 0, 0]
dtype: float16