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 3 days ago

Commit

c70c8ec

verified ·

1 Parent(s): 8c951c4

Add README: WIP status, training lineage, eval snapshots, artifact warnings

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README.md +63 -0

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	@@ -0,0 +1,63 @@

+---
+license: cc-by-4.0
+base_model: inclusionAI/Ling-3.0-tiny
+library_name: safetensors
+---
+# ling3-tiny-coder-ckpts — work-in-progress (PAUSED)
+> **Status: paused / unfinished.** This repo stores intermediate artifacts from a
+> personal experiment: fine-tuning [Ling-3.0-tiny](https://huggingface.co/inclusionAI/Ling-3.0-tiny)
+> (7.9B Bailing-hybrid MoE, KDA+MLA) into a C++ competitive-programming coder on
+> 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).
+- `evals/` stores raw LiveCodeBench-v6 (60-problem sample) + OJBench result JSONs.
+- `latest.json` / `routing_counts.npz` / `llama-bin-cache.tar.gz` are pipeline
+  bookkeeping (resume pointer, MoE expert-usage counters, llama.cpp build cache).
+## Training lineage
+| Stage | Checkpoint | Steps | Data | Notes |
+|---|---|---|---|---|
+| SFT | `sft-21750` | 21,750 | R1 reasoning traces (codeforces-cots) + Magicoder thinking-off, ~178M tok, 2 epochs | completed |
+| CPT | `cpt-2903` | 2,903 | raw C++ (Stack-v2) acclimation, 24M tok | completed; **best eval to date** |
+| agentic (v22) | `agentic-600` | 600 | SWE-smith C++ patches | partial |
+| calib | `calib-19460` | 19,460 | convergence-calibrated SFT (branch-filtered traces, loop removal), 4 epochs from `cpt-2903` weights-only | completed 2026-09 |
+The `calib` stage was an attempt to fix the dominant failure mode — on hard
+problems the model reasons correctly but never emits code (runs out of tokens
+inside reasoning loops). Data curation per arXiv:2604.01702 (branching-keyword
+filter, loop removal, step deletion) + 4× more epochs.
+## Eval snapshots (LLM-generated, exec-checked; LiveCodeBench v6 sample)
+| Checkpoint | pass@1 | case pass rate | GGUF quant |
+|---|---|---|---|
+| sft-14000 | 43.3% | 41.1% | Q4_K_M |
+| sft-14000 | 26.7% | 26.7% | Q8_0 (early broken run) |
+| cpt-2903 | 51.7% | 39.7% | Q4_K_M |
+| calib-19460 | 48.3% | 36.4% | Q4_K_M |
+Honest bottom line: easy problems ≈ solved (100% at cpt-2903), but the
+convergence-calibration pass **did not lift overall pass@1** — hard-problem
+generation-length failures persisted. The experiment was paused here rather
+than extended (next planned step was a small DPO stage targeting looped
+generations).
+## Resuming / reusing
+- Base model: [inclusionAI/Ling-3.0-tiny](https://huggingface.co/inclusionAI/Ling-3.0-tiny)
+- Adapter checkpoints pair 1:1 with `optstate_*` files for exact resume.
+- The two `models/` GGUFs are the only turnkey artifacts; a merged GGUF of the
+  later checkpoints was never produced.