Instructions to use TensorVizion/Loi-LLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use TensorVizion/Loi-LLM 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 TensorVizion/Loi-LLM
# Run inference directly in the terminal:
llama cli -hf TensorVizion/Loi-LLM

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf TensorVizion/Loi-LLM
# Run inference directly in the terminal:
llama cli -hf TensorVizion/Loi-LLM

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 TensorVizion/Loi-LLM
# Run inference directly in the terminal:
./llama-cli -hf TensorVizion/Loi-LLM

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 TensorVizion/Loi-LLM
# Run inference directly in the terminal:
./build/bin/llama-cli -hf TensorVizion/Loi-LLM

Use Docker

docker model run hf.co/TensorVizion/Loi-LLM

LM Studio
Jan
Ollama
How to use TensorVizion/Loi-LLM with Ollama:
```
ollama run hf.co/TensorVizion/Loi-LLM
```
Unsloth Desktop

How to use TensorVizion/Loi-LLM with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf TensorVizion/Loi-LLM

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": "TensorVizion/Loi-LLM"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use TensorVizion/Loi-LLM with Docker Model Runner:
```
docker model run hf.co/TensorVizion/Loi-LLM
```

Lemonade

How to use TensorVizion/Loi-LLM with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull TensorVizion/Loi-LLM

Run and chat with the model

lemonade run user.Loi-LLM-{{QUANT_TAG}}

List all available models

lemonade list

Hermes Agent

How to use TensorVizion/Loi-LLM with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf TensorVizion/Loi-LLM

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 TensorVizion/Loi-LLM

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use TensorVizion/Loi-LLM with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf TensorVizion/Loi-LLM

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 "TensorVizion/Loi-LLM" \
  --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"

Loi-LLM

File size: 716 Bytes

a01e851

{
  "_serialized_key": "NxDNeuronConfig",
  "batch_size": 4,
  "capacity_factor": null,
  "checkpoint_id": "TensorVizion/Loi-LLM",
  "checkpoint_revision": "08551e259be6cc7aa351aa05af997d309e7ffaa2",
  "continuous_batching": true,
  "ep_degree": 1,
  "fused_qkv": true,
  "glu_mlp": true,
  "local_ranks_size": 4,
  "max_batch_size": 4,
  "max_context_length": 4096,
  "max_topk": 256,
  "n_active_tokens": 4096,
  "neuronxcc_version": "2.21.18209.0+043b1bf7",
  "on_device_sampling": true,
  "optimum_neuron_version": "0.4.1",
  "output_logits": false,
  "pp_degree": 1,
  "sequence_length": 4096,
  "speculation_length": 0,
  "start_rank_id": 0,
  "target": "inf2",
  "torch_dtype": "bfloat16",
  "tp_degree": 4
}