Instructions to use TendieLabs/Fred-35B-A3B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use TendieLabs/Fred-35B-A3B-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 TendieLabs/Fred-35B-A3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf TendieLabs/Fred-35B-A3B-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 TendieLabs/Fred-35B-A3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf TendieLabs/Fred-35B-A3B-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 TendieLabs/Fred-35B-A3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf TendieLabs/Fred-35B-A3B-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 TendieLabs/Fred-35B-A3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf TendieLabs/Fred-35B-A3B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/TendieLabs/Fred-35B-A3B-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use TendieLabs/Fred-35B-A3B-GGUF with Ollama:
```
ollama run hf.co/TendieLabs/Fred-35B-A3B-GGUF:Q4_K_M
```
Unsloth Desktop

How to use TendieLabs/Fred-35B-A3B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf TendieLabs/Fred-35B-A3B-GGUF: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": "TendieLabs/Fred-35B-A3B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use TendieLabs/Fred-35B-A3B-GGUF with Docker Model Runner:
```
docker model run hf.co/TendieLabs/Fred-35B-A3B-GGUF:Q4_K_M
```

Lemonade

How to use TendieLabs/Fred-35B-A3B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull TendieLabs/Fred-35B-A3B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Fred-35B-A3B-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use TendieLabs/Fred-35B-A3B-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf TendieLabs/Fred-35B-A3B-GGUF: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 TendieLabs/Fred-35B-A3B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use TendieLabs/Fred-35B-A3B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf TendieLabs/Fred-35B-A3B-GGUF: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 "TendieLabs/Fred-35B-A3B-GGUF: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"

Fred-35B A3B

Fred-35B A3B is a Mermaid diagram-focused fine-tune built on top of Qwen/Qwen3.5-35B-A3B), trained primarily for generating accurate, well-structured diagrams in academic and STEM contexts.

Intended Use

Fred-35B A3B was developed to assist students and professionals in producing Mermaid-syntax diagrams, with a particular focus on Entity-Relationship (ER) diagrams for database design, as well as flowcharts, sequence diagrams, and class diagrams commonly used in computer science and informatics coursework.

The model is designed to integrate naturally into note-taking and knowledge management workflows, including tools like Obsidian via its native Mermaid rendering support. This makes it well-suited as an in-context diagram assistant for STEM-related academic tasks.

Training Data

The model was fine-tuned on two datasets:

crownelius/Opus-4.6-Reasoning-2100x-formatted: High-reasoning instruction data used to reinforce structured analytical output.
TendieLabs/Tender_Mermaid_Training_V1: Mermaid diagram-specific training examples covering a range of diagram types and complexity levels.

Capabilities

ER diagram generation from natural language descriptions or schema definitions
General Mermaid diagram generation (flowcharts, sequence, class, and state diagrams)
STEM academic task assistance with structured, diagram-augmented responses
Obsidian-compatible output formatting