Instructions to use v000000/L3.1-Niitorm-8B-LATCOSx2-Version-GGUFs-IMATRIX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use v000000/L3.1-Niitorm-8B-LATCOSx2-Version-GGUFs-IMATRIX 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 v000000/L3.1-Niitorm-8B-LATCOSx2-Version-GGUFs-IMATRIX:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf v000000/L3.1-Niitorm-8B-LATCOSx2-Version-GGUFs-IMATRIX:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf v000000/L3.1-Niitorm-8B-LATCOSx2-Version-GGUFs-IMATRIX:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf v000000/L3.1-Niitorm-8B-LATCOSx2-Version-GGUFs-IMATRIX:Q4_K_M

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf v000000/L3.1-Niitorm-8B-LATCOSx2-Version-GGUFs-IMATRIX:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama cli -hf v000000/L3.1-Niitorm-8B-LATCOSx2-Version-GGUFs-IMATRIX:Q4_K_M

Use Docker

docker model run hf.co/v000000/L3.1-Niitorm-8B-LATCOSx2-Version-GGUFs-IMATRIX:Q4_K_M

Llama new
LM Studio
Jan
Ollama
How to use v000000/L3.1-Niitorm-8B-LATCOSx2-Version-GGUFs-IMATRIX with Ollama:
```
ollama run hf.co/v000000/L3.1-Niitorm-8B-LATCOSx2-Version-GGUFs-IMATRIX:Q4_K_M
```
Unsloth Desktop

How to use v000000/L3.1-Niitorm-8B-LATCOSx2-Version-GGUFs-IMATRIX with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf v000000/L3.1-Niitorm-8B-LATCOSx2-Version-GGUFs-IMATRIX:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "v000000/L3.1-Niitorm-8B-LATCOSx2-Version-GGUFs-IMATRIX:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use v000000/L3.1-Niitorm-8B-LATCOSx2-Version-GGUFs-IMATRIX with Docker Model Runner:
```
docker model run hf.co/v000000/L3.1-Niitorm-8B-LATCOSx2-Version-GGUFs-IMATRIX:Q4_K_M
```

Lemonade

How to use v000000/L3.1-Niitorm-8B-LATCOSx2-Version-GGUFs-IMATRIX with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull v000000/L3.1-Niitorm-8B-LATCOSx2-Version-GGUFs-IMATRIX:Q4_K_M

Run and chat with the model

lemonade run user.L3.1-Niitorm-8B-LATCOSx2-Version-GGUFs-IMATRIX-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use v000000/L3.1-Niitorm-8B-LATCOSx2-Version-GGUFs-IMATRIX with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf v000000/L3.1-Niitorm-8B-LATCOSx2-Version-GGUFs-IMATRIX:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default v000000/L3.1-Niitorm-8B-LATCOSx2-Version-GGUFs-IMATRIX:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use v000000/L3.1-Niitorm-8B-LATCOSx2-Version-GGUFs-IMATRIX with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf v000000/L3.1-Niitorm-8B-LATCOSx2-Version-GGUFs-IMATRIX:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "v000000/L3.1-Niitorm-8B-LATCOSx2-Version-GGUFs-IMATRIX:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Llama-3.1-Niitorm-8B-LATCOSx2

Ordered by quality:

q8_0 imatrix
q8_0
q6_k imatrix
q6_k
q5_k_m imatrix
q5_k_s imatrix
q4_k_m imatrix
q4_k_s imatrix
iq4_xs imatrix
q4_0_4_8 imatrix arm
q4_0_4_4 imatrix arm

This is a test RP model, "v000000/L3.1-Niitorm-8B-t0.0001" but merged one extra time with "akjindal53244/Llama-3.1-Storm-8B". Using a new merging algorithm I wrote "LATCOS", which is non linear interpolation and cosine vector similarity between tensors in both magnitude and direction. This attempts to find the smoothest possible interpolation and make them work more seamlessly together by taking into account the vector direction where both models agree. The model seems a lot smarter even though it's just a bit more of storm, but also more compliant which could be a negative since it's less "dynamic".

imatrix data randomized bartowski, kalomeze, rp snippets, working gpt4 code, human messaging, story

Downloads last month: 279

GGUF

Model size

8B params

Architecture

llama

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