Instructions to use maternion/ling3-tiny-coder-ckpts with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use maternion/ling3-tiny-coder-ckpts 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 maternion/ling3-tiny-coder-ckpts:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf maternion/ling3-tiny-coder-ckpts:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf maternion/ling3-tiny-coder-ckpts:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf maternion/ling3-tiny-coder-ckpts: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 maternion/ling3-tiny-coder-ckpts:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama cli -hf maternion/ling3-tiny-coder-ckpts:Q4_K_M

Use Docker

docker model run hf.co/maternion/ling3-tiny-coder-ckpts:Q4_K_M

Llama new
LM Studio
Jan
Ollama
How to use maternion/ling3-tiny-coder-ckpts with Ollama:
```
ollama run hf.co/maternion/ling3-tiny-coder-ckpts:Q4_K_M
```
Unsloth Desktop

How to use maternion/ling3-tiny-coder-ckpts with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf maternion/ling3-tiny-coder-ckpts: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": "maternion/ling3-tiny-coder-ckpts:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use maternion/ling3-tiny-coder-ckpts with Docker Model Runner:
```
docker model run hf.co/maternion/ling3-tiny-coder-ckpts:Q4_K_M
```

Lemonade

How to use maternion/ling3-tiny-coder-ckpts with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull maternion/ling3-tiny-coder-ckpts:Q4_K_M

Run and chat with the model

lemonade run user.ling3-tiny-coder-ckpts-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use maternion/ling3-tiny-coder-ckpts with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf maternion/ling3-tiny-coder-ckpts: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 maternion/ling3-tiny-coder-ckpts:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use maternion/ling3-tiny-coder-ckpts with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf maternion/ling3-tiny-coder-ckpts: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 "maternion/ling3-tiny-coder-ckpts: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"

maternion commited on 1 day ago

Commit

71d5c9a

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@@ -12,13 +12,8 @@ library_name: safetensors
 > Kaggle TPU v5e-8 with LoRA adapters. Development is inactive — the items here are
 > training artifacts, not a finished model. Layout may change if work resumes.
-## What is in this repo (and what is NOT)
-- **Not a mergeable/checkpoint-loadable model.** The `checkpoints/*.safetensors`
-  are flat-key LoRA adapter + optimizer-state dumps written by a custom JAX
-  training loop (attention LoRA r=32 + per-expert `down_proj` LoRA r=16).
-  They are only readable by the project's training/eval notebooks — they will
-  NOT load with `transformers`/`peft` as-is.
 - `models/` contains the only directly usable artifacts: two early-SFT merged
   GGUF exports (Q4_K_M / Q8_0 / bf16 + chat template). These are from an early
   mid-SFT point and are noticeably weaker than later checkpoints (see evals).

 > Kaggle TPU v5e-8 with LoRA adapters. Development is inactive — the items here are
 > training artifacts, not a finished model. Layout may change if work resumes.
+## What is in this repo
 - `models/` contains the only directly usable artifacts: two early-SFT merged
   GGUF exports (Q4_K_M / Q8_0 / bf16 + chat template). These are from an early
   mid-SFT point and are noticeably weaker than later checkpoints (see evals).