Instructions to use darthceltic85/determinex-observer-llama-3.2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use darthceltic85/determinex-observer-llama-3.2 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 darthceltic85/determinex-observer-llama-3.2
# Run inference directly in the terminal:
llama cli -hf darthceltic85/determinex-observer-llama-3.2

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf darthceltic85/determinex-observer-llama-3.2
# Run inference directly in the terminal:
llama cli -hf darthceltic85/determinex-observer-llama-3.2

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 darthceltic85/determinex-observer-llama-3.2
# Run inference directly in the terminal:
./llama-cli -hf darthceltic85/determinex-observer-llama-3.2

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 darthceltic85/determinex-observer-llama-3.2
# Run inference directly in the terminal:
./build/bin/llama-cli -hf darthceltic85/determinex-observer-llama-3.2

Use Docker

docker model run hf.co/darthceltic85/determinex-observer-llama-3.2

LM Studio
Jan
Ollama
How to use darthceltic85/determinex-observer-llama-3.2 with Ollama:
```
ollama run hf.co/darthceltic85/determinex-observer-llama-3.2
```

Unsloth Studio

How to use darthceltic85/determinex-observer-llama-3.2 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 darthceltic85/determinex-observer-llama-3.2 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 darthceltic85/determinex-observer-llama-3.2 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for darthceltic85/determinex-observer-llama-3.2 to start chatting

How to use darthceltic85/determinex-observer-llama-3.2 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf darthceltic85/determinex-observer-llama-3.2

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "darthceltic85/determinex-observer-llama-3.2"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use darthceltic85/determinex-observer-llama-3.2 with Docker Model Runner:
```
docker model run hf.co/darthceltic85/determinex-observer-llama-3.2
```

Lemonade

How to use darthceltic85/determinex-observer-llama-3.2 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull darthceltic85/determinex-observer-llama-3.2

Run and chat with the model

lemonade run user.determinex-observer-llama-3.2-{{QUANT_TAG}}

List all available models

lemonade list

Hermes Agent

How to use darthceltic85/determinex-observer-llama-3.2 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf darthceltic85/determinex-observer-llama-3.2

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 darthceltic85/determinex-observer-llama-3.2

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use darthceltic85/determinex-observer-llama-3.2 with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf darthceltic85/determinex-observer-llama-3.2

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 "darthceltic85/determinex-observer-llama-3.2" \
  --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"

determinex-observer-llama-3.2

C3 Observer — Monitor / error diagnosis in the Determinex compiler-verified multi-agent coding system.

Base model: Llama-3.2-3B-Instruct (3B)
Fine-tune: LoRA on the Determinex Semantic DSL corpus, compiler-validated
Ollama tag: determinex-observer-v6-dsl
Size: 3.06 GB (GGUF, quantized)

Determinex never trusts a model's output directly — every generation is verified against a real compiler/test oracle before it's accepted. This model is one of three specialists (Engineer / Observer / Sentinel) that coordinate through a shared latent space (the Rosetta Stone).

Usage

ollama create determinex-observer-v6-dsl -f Modelfile
ollama run determinex-observer-v6-dsl

License — Llama 3.2 Community License (important)

This model is a fine-tune of meta-llama/Llama-3.2-3B-Instruct and is governed by the Llama 3.2 Community License, not Apache 2.0. By downloading or using these weights you agree to that license and to Meta's Acceptable Use Policy.

Built with Llama.

Full license text: https://github.com/meta-llama/llama-models/blob/main/models/llama3_2/LICENSE
Acceptable Use Policy: https://github.com/meta-llama/llama-models/blob/main/models/llama3_2/USE_POLICY.md
This model's name includes "Llama" per the license's naming requirement for any distributed derivative.

Downloads last month: 9

GGUF

Model size

3B params

Architecture

qwen2

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

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