Instructions to use NANI-Nithin/Ling-3.0-tiny-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 NANI-Nithin/Ling-3.0-tiny-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 NANI-Nithin/Ling-3.0-tiny-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf NANI-Nithin/Ling-3.0-tiny-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 NANI-Nithin/Ling-3.0-tiny-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf NANI-Nithin/Ling-3.0-tiny-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 NANI-Nithin/Ling-3.0-tiny-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf NANI-Nithin/Ling-3.0-tiny-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 NANI-Nithin/Ling-3.0-tiny-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf NANI-Nithin/Ling-3.0-tiny-GGUF:Q4_K_M

Use Docker

docker model run hf.co/NANI-Nithin/Ling-3.0-tiny-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use NANI-Nithin/Ling-3.0-tiny-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "NANI-Nithin/Ling-3.0-tiny-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "NANI-Nithin/Ling-3.0-tiny-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/NANI-Nithin/Ling-3.0-tiny-GGUF:Q4_K_M

Ollama
How to use NANI-Nithin/Ling-3.0-tiny-GGUF with Ollama:
```
ollama run hf.co/NANI-Nithin/Ling-3.0-tiny-GGUF:Q4_K_M
```
Unsloth Desktop

How to use NANI-Nithin/Ling-3.0-tiny-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf NANI-Nithin/Ling-3.0-tiny-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": "NANI-Nithin/Ling-3.0-tiny-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use NANI-Nithin/Ling-3.0-tiny-GGUF with Docker Model Runner:
```
docker model run hf.co/NANI-Nithin/Ling-3.0-tiny-GGUF:Q4_K_M
```

Lemonade

How to use NANI-Nithin/Ling-3.0-tiny-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull NANI-Nithin/Ling-3.0-tiny-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Ling-3.0-tiny-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use NANI-Nithin/Ling-3.0-tiny-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 NANI-Nithin/Ling-3.0-tiny-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 NANI-Nithin/Ling-3.0-tiny-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use NANI-Nithin/Ling-3.0-tiny-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf NANI-Nithin/Ling-3.0-tiny-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 "NANI-Nithin/Ling-3.0-tiny-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"

NANI-Nithin commited on 4 days ago

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@@ -1,84 +1,199 @@
 ---
 base_model: inclusionAI/Ling-3.0-tiny
-library_name: gguf
-pipeline_tag: text-generation
-tags:
-  - gguf
-  - llama.cpp
-  - quantized
-  - imatrix
 ---
-# Ling-3.0-tiny GGUF
-GGUF quantizations of [inclusionAI/Ling-3.0-tiny](https://huggingface.co/inclusionAI/Ling-3.0-tiny), covering 32 files (139.3 GB total).
-## Files
-| File | Quant | Size | Notes |
-|---|---|---:|---|
-| [Ling-3.0-tiny-BF16.gguf](https://huggingface.co/NANI-Nithin/Ling-3.0-tiny-GGUF/blob/main/Ling-3.0-tiny-BF16.gguf) | `BF16` | 14.72 GB | Full precision source. Every quant below is cut from this file. |
-| [Ling-3.0-tiny-F16.gguf](https://huggingface.co/NANI-Nithin/Ling-3.0-tiny-GGUF/blob/main/Ling-3.0-tiny-F16.gguf) | `F16` | 14.72 GB | Full precision source. |
-| [Ling-3.0-tiny-Q8_0.gguf](https://huggingface.co/NANI-Nithin/Ling-3.0-tiny-GGUF/blob/main/Ling-3.0-tiny-Q8_0.gguf) | `Q8_0` | 7.83 GB | Effectively lossless. Use when disk and RAM are not the constraint. |
-| [Ling-3.0-tiny-Q6_K.gguf](https://huggingface.co/NANI-Nithin/Ling-3.0-tiny-GGUF/blob/main/Ling-3.0-tiny-Q6_K.gguf) | `Q6_K` | 6.05 GB | Near-lossless; the last stop before quality becomes measurable. |
-| [Ling-3.0-tiny-Q5_K_M.gguf](https://huggingface.co/NANI-Nithin/Ling-3.0-tiny-GGUF/blob/main/Ling-3.0-tiny-Q5_K_M.gguf) | `Q5_K_M` | 5.25 GB | Very good quality, noticeably smaller than Q6_K. |
-| [Ling-3.0-tiny-Q5_K_S.gguf](https://huggingface.co/NANI-Nithin/Ling-3.0-tiny-GGUF/blob/main/Ling-3.0-tiny-Q5_K_S.gguf) | `Q5_K_S` | 5.11 GB | Slightly smaller than Q5_K_M for a slight quality cost. |
-| [Ling-3.0-tiny-Q5_1.gguf](https://huggingface.co/NANI-Nithin/Ling-3.0-tiny-GGUF/blob/main/Ling-3.0-tiny-Q5_1.gguf) | `Q5_1` | 5.55 GB | Legacy. Prefer Q5_K_M. |
-| [Ling-3.0-tiny-Q5_0.gguf](https://huggingface.co/NANI-Nithin/Ling-3.0-tiny-GGUF/blob/main/Ling-3.0-tiny-Q5_0.gguf) | `Q5_0` | 5.11 GB | Legacy. Prefer Q5_K_M. |
-| [Ling-3.0-tiny-Q4_K_M.gguf](https://huggingface.co/NANI-Nithin/Ling-3.0-tiny-GGUF/blob/main/Ling-3.0-tiny-Q4_K_M.gguf) | `Q4_K_M` | 4.49 GB | The usual default. Best quality-per-byte for most people. |
-| [Ling-3.0-tiny-Q4_K_S.gguf](https://huggingface.co/NANI-Nithin/Ling-3.0-tiny-GGUF/blob/main/Ling-3.0-tiny-Q4_K_S.gguf) | `Q4_K_S` | 4.24 GB | A little smaller than Q4_K_M, a little worse. |
-| [Ling-3.0-tiny-IQ4_NL.gguf](https://huggingface.co/NANI-Nithin/Ling-3.0-tiny-GGUF/blob/main/Ling-3.0-tiny-IQ4_NL.gguf) | `IQ4_NL` | 4.22 GB | Non-linear 4-bit; good on hardware without fast K-quant kernels. |
-| [Ling-3.0-tiny-IQ4_XS.gguf](https://huggingface.co/NANI-Nithin/Ling-3.0-tiny-GGUF/blob/main/Ling-3.0-tiny-IQ4_XS.gguf) | `IQ4_XS` | 3.99 GB | Best sub-4.5bpw option; usually beats Q4_K_S at a smaller size. |
-| [Ling-3.0-tiny-Q4_1.gguf](https://huggingface.co/NANI-Nithin/Ling-3.0-tiny-GGUF/blob/main/Ling-3.0-tiny-Q4_1.gguf) | `Q4_1` | 4.66 GB | Legacy. Prefer Q4_K_M. |
-| [Ling-3.0-tiny-Q4_0.gguf](https://huggingface.co/NANI-Nithin/Ling-3.0-tiny-GGUF/blob/main/Ling-3.0-tiny-Q4_0.gguf) | `Q4_0` | 4.22 GB | Legacy round-to-nearest. Prefer Q4_K_M unless a runtime needs this. |
-| [Ling-3.0-tiny-MXFP4_MOE.gguf](https://huggingface.co/NANI-Nithin/Ling-3.0-tiny-GGUF/blob/main/Ling-3.0-tiny-MXFP4_MOE.gguf) | `MXFP4_MOE` | 4.39 GB | MoE-only 4-bit microscaling format for the expert tensors. |
-| [Ling-3.0-tiny-Q3_K_L.gguf](https://huggingface.co/NANI-Nithin/Ling-3.0-tiny-GGUF/blob/main/Ling-3.0-tiny-Q3_K_L.gguf) | `Q3_K_L` | 3.86 GB | Small, with real quality loss. Usable when RAM is tight. |
-| [Ling-3.0-tiny-Q3_K_M.gguf](https://huggingface.co/NANI-Nithin/Ling-3.0-tiny-GGUF/blob/main/Ling-3.0-tiny-Q3_K_M.gguf) | `Q3_K_M` | 3.58 GB | Smaller again; noticeable degradation. |
-| [Ling-3.0-tiny-IQ3_M.gguf](https://huggingface.co/NANI-Nithin/Ling-3.0-tiny-GGUF/blob/main/Ling-3.0-tiny-IQ3_M.gguf) | `IQ3_M` | 3.31 GB | Strong at ~3.7bpw, clearly better than Q3_K_M. |
-| [Ling-3.0-tiny-IQ3_S.gguf](https://huggingface.co/NANI-Nithin/Ling-3.0-tiny-GGUF/blob/main/Ling-3.0-tiny-IQ3_S.gguf) | `IQ3_S` | 3.27 GB | Slightly smaller than IQ3_M. |
-| [Ling-3.0-tiny-Q3_K_S.gguf](https://huggingface.co/NANI-Nithin/Ling-3.0-tiny-GGUF/blob/main/Ling-3.0-tiny-Q3_K_S.gguf) | `Q3_K_S` | 3.27 GB | Aggressive. Prefer IQ3_M at a similar size. |
-| [Ling-3.0-tiny-IQ3_XS.gguf](https://huggingface.co/NANI-Nithin/Ling-3.0-tiny-GGUF/blob/main/Ling-3.0-tiny-IQ3_XS.gguf) | `IQ3_XS` | 3.11 GB | Aggressive but coherent. |
-| [Ling-3.0-tiny-IQ3_XXS.gguf](https://huggingface.co/NANI-Nithin/Ling-3.0-tiny-GGUF/blob/main/Ling-3.0-tiny-IQ3_XXS.gguf) | `IQ3_XXS` | 2.91 GB | Very aggressive; imatrix carries it. |
-| [Ling-3.0-tiny-Q2_K.gguf](https://huggingface.co/NANI-Nithin/Ling-3.0-tiny-GGUF/blob/main/Ling-3.0-tiny-Q2_K.gguf) | `Q2_K` | 2.78 GB | Very small, heavily degraded. For experimentation. |
-| [Ling-3.0-tiny-IQ2_M.gguf](https://huggingface.co/NANI-Nithin/Ling-3.0-tiny-GGUF/blob/main/Ling-3.0-tiny-IQ2_M.gguf) | `IQ2_M` | 2.52 GB | The smallest size most people find usable. |
-| [Ling-3.0-tiny-Q2_K_S.gguf](https://huggingface.co/NANI-Nithin/Ling-3.0-tiny-GGUF/blob/main/Ling-3.0-tiny-Q2_K_S.gguf) | `Q2_K_S` | 2.59 GB | Smaller than Q2_K, requires the imatrix. |
-| [Ling-3.0-tiny-IQ2_S.gguf](https://huggingface.co/NANI-Nithin/Ling-3.0-tiny-GGUF/blob/main/Ling-3.0-tiny-IQ2_S.gguf) | `IQ2_S` | 2.31 GB | Below the usual usability line. |
-| [Ling-3.0-tiny-IQ2_XS.gguf](https://huggingface.co/NANI-Nithin/Ling-3.0-tiny-GGUF/blob/main/Ling-3.0-tiny-IQ2_XS.gguf) | `IQ2_XS` | 2.27 GB | Experimental. |
-| [Ling-3.0-tiny-IQ2_XXS.gguf](https://huggingface.co/NANI-Nithin/Ling-3.0-tiny-GGUF/blob/main/Ling-3.0-tiny-IQ2_XXS.gguf) | `IQ2_XXS` | 2.06 GB | Experimental. |
-| [Ling-3.0-tiny-Q2_0.gguf](https://huggingface.co/NANI-Nithin/Ling-3.0-tiny-GGUF/blob/main/Ling-3.0-tiny-Q2_0.gguf) | `Q2_0` | 2.28 GB | Extreme, group-64. Included for completeness. |
-| [Ling-3.0-tiny-IQ1_M.gguf](https://huggingface.co/NANI-Nithin/Ling-3.0-tiny-GGUF/blob/main/Ling-3.0-tiny-IQ1_M.gguf) | `IQ1_M` | 1.80 GB | Extreme. Expect substantial degradation. |
-| [Ling-3.0-tiny-IQ1_S.gguf](https://huggingface.co/NANI-Nithin/Ling-3.0-tiny-GGUF/blob/main/Ling-3.0-tiny-IQ1_S.gguf) | `IQ1_S` | 1.64 GB | Extreme. Expect substantial degradation. |
-| [Ling-3.0-tiny-Q1_0.gguf](https://huggingface.co/NANI-Nithin/Ling-3.0-tiny-GGUF/blob/main/Ling-3.0-tiny-Q1_0.gguf) | `Q1_0` | 1.21 GB | Extreme. Included for completeness. |
-## Which one should I download?
-Pick the largest file that leaves a couple of gigabytes of headroom on the device you will run it on — the model has to fit in RAM (or VRAM, if you are offloading) alongside the KV cache and the OS.
-- Plenty of memory: **Q6_K** or **Q8_0**.
-- The usual choice: **Q4_K_M**.
-- Tight on memory: **IQ4_XS**, then **IQ3_M**, then **IQ2_M**.
-- The `IQ*` files are imatrix-guided and generally beat a `Q*` file of similar size, at the cost of slightly slower inference on some hardware.
-## Quantization details
-- Importance matrix computed with `llama-imatrix` over 500 rows of [Salesforce/wikitext](https://huggingface.co/datasets/Salesforce/wikitext) (`wikitext-2-raw-v1`).
-- The matrix was computed on the **BF16** weights.
-- K-quants below 6 bit and the whole `IQ` set are imatrix-guided. `Q4_0`/`Q4_1`/`Q5_0`/`Q5_1` are legacy round-to-nearest and ignore it; `Q6_K`/`Q8_0` are near-lossless and do not need it.
-- All files are cut from the same BF16 GGUF, so differences between them are quantization only.
-## Usage
 ```bash
 llama-cli -hf NANI-Nithin/Ling-3.0-tiny-GGUF:Q4_K_M -p "Hello"
 ```
-Or download one file and point at it directly:
 ```bash
-huggingface-cli download NANI-Nithin/Ling-3.0-tiny-GGUF Ling-3.0-tiny-Q4_K_M.gguf --local-dir .
-llama-cli -m Ling-3.0-tiny-Q4_K_M.gguf -p "Hello"
 ```
 ---
-Quantized with [llama.cpp](https://github.com/ggml-org/llama.cpp) by AgentQuantix on 2026-09-05. Licensing follows the base model.

 ---
+title: Ling-3.0-tiny-GGUF
+library_name: llama.cpp
+model_type: quantized
 base_model: inclusionAI/Ling-3.0-tiny
+datasets: []
+tags: [gguf, quantized, text-generation, moe, bailing-hybrid, reasoning, thinking, agentic, llama.cpp]
 ---
+# Ling-3.0-tiny GGUF (llama.cpp)
+[![Model Type](https://img.shields.io/badge/Model%20Type-Quantized%20GGUF-3B82F6?style=flat-square)](https://github.com/ggerganov/llama.cpp)
+[![Backend](https://img.shields.io/badge/Backend-llama.cpp-FF7800?style=flat-square&logo=cpp)](https://github.com/ggerganov/llama.cpp)
+[![Size](https://img.shields.io/badge/Total%20Size-139.31%20GB-FA8532?style=flat-square)](https://huggingface.co/NANI-Nithin/Ling-3.0-tiny-GGUF)
+Quantized GGUF files for **Ling-3.0-tiny**, IBM's lightweight hybrid reasoning MoE model optimized for deployment with [llama.cpp](https://github.com/ggerganov/llama.cpp). This model delivers strong reasoning and agentic capabilities at low inference cost through an efficient hybrid architecture combining KDA and MLA attention with a sparse MoE FFN.
+## Model Overview
+**Ling-3.0-tiny** is a 7.9B parameter model with only 1.3B activated parameters per token, designed for efficient local and edge deployment. It features:
+- **Efficient Hybrid-Linear Architecture:** 3:1 alternating stacking of KDA and MLA (3 KDA layers followed by 1 MLA layer per 4-layer block) with a sparse MoE FFN comprising 128 routed experts
+- **Native Hybrid Reasoning and Agentic Capabilities:** Supports both fast responses and multi-step reasoning through configurable thinking mode
+- **Local and Edge Deployment:** Validated on NVIDIA DGX Spark, Apple Silicon MacBook, and Mac mini for capable reasoning without datacenter-class GPUs
+### Key Capabilities
+- **Parameter-Efficient MoE:** Only 1.3B of 7.9B parameters activated per token for balanced performance and efficiency
+- **Hybrid Attention:** Combines KDA (Kimi Delta Attention) and MLA (Multi-Head Latent Attention) for efficient long-context processing
+- **Fast Inference:** Reaches ~100-105 tokens/s on DGX Spark and 86-90 tokens/s on M4 Pro MacBook with FP8
+- **Memory Efficient:** ~8.34 GB peak memory usage at 8K context length
+- **Thinking Mode:** Native chain-of-thought reasoning with per-request configurability
+## Available Quantizations
+| File | Size | Quality | Recommended Use |
+|------|------|---------|-----------------|
+| **Ling-3.0-tiny-BF16.gguf** | 14.72 GB | Full precision source. Every quant below is cut from this file. | Original model, maximum quality |
+| **Ling-3.0-tiny-F16.gguf** | 14.72 GB | Full precision source. | Alternative full precision |
+| **Ling-3.0-tiny-Q8_0.gguf** | 7.83 GB | Effectively lossless. Use when disk and RAM are not the constraint. | Highest quality, less compression |
+| **Ling-3.0-tiny-Q6_K.gguf** | 6.05 GB | Near-lossless; the last stop before quality becomes measurable. | Balanced quality/size |
+| **Ling-3.0-tiny-Q5_K_M.gguf** | 5.25 GB | Very good quality, noticeably smaller than Q6_K. | Good trade-off |
+| **Ling-3.0-tiny-Q5_K_S.gguf** | 5.11 GB | Slightly smaller than Q5_K_M for a slight quality cost. | Smaller footprint |
+| **Ling-3.0-tiny-Q5_1.gguf** | 5.55 GB | Legacy. Prefer Q5_K_M. | Historical compatibility |
+| **Ling-3.0-tiny-Q5_0.gguf** | 5.11 GB | Legacy. Prefer Q5_K_M. | Historical compatibility |
+| **Ling-3.0-tiny-Q4_K_M.gguf** | 4.49 GB | The usual default. Best quality-per-byte for most people. | Default choice |
+| **Ling-3.0-tiny-Q4_K_S.gguf** | 4.24 GB | A little smaller than Q4_K_M, a little worse. | Smaller footprint |
+| **Ling-3.0-tiny-IQ4_NL.gguf** | 4.22 GB | Non-linear 4-bit; good on hardware without fast K-quant kernels. | Specialized hardware |
+| **Ling-3.0-tiny-IQ4_XS.gguf** | 3.99 GB | Best sub-4.5bpw option; usually beats Q4_K_S at a smaller size. | Size-critical |
+| **Ling-3.0-tiny-Q4_1.gguf** | 4.66 GB | Legacy. Prefer Q4_K_M. | Historical compatibility |
+| **Ling-3.0-tiny-Q4_0.gguf** | 4.22 GB | Legacy round-to-nearest. Prefer Q4_K_M unless a runtime needs this. | Historical compatibility |
+| **Ling-3.0-tiny-MXFP4_MOE.gguf** | 4.39 GB | MoE-only 4-bit microscaling format for the expert tensors. | Specialized MoE deployment |
+| **Ling-3.0-tiny-Q3_K_L.gguf** | 3.86 GB | Small, with real quality loss. Usable when RAM is tight. | Tight RAM constraints |
+| **Ling-3.0-tiny-Q3_K_M.gguf** | 3.58 GB | Smaller again; noticeable degradation. | Memory-constrained |
+| **Ling-3.0-tiny-IQ3_M.gguf** | 3.31 GB | Strong at ~3.7bpw, clearly better than Q3_K_M. | Quality-conscious sizing |
+| **Ling-3.0-tiny-IQ3_S.gguf** | 3.27 GB | Slightly smaller than IQ3_M. | Compact version |
+| **Ling-3.0-tiny-Q3_K_S.gguf** | 3.27 GB | Aggressive. Prefer IQ3_M at a similar size. | Maximum compression |
+| **Ling-3.0-tiny-IQ3_XS.gguf** | 3.11 GB | Aggressive but coherent. | Extreme compression |
+| **Ling-3.0-tiny-IQ3_XXS.gguf** | 2.91 GB | Very aggressive; the last coherent step down. | Extreme compression |
+| **Ling-3.0-tiny-Q2_K.gguf** | 2.78 GB | Very small, heavily degraded. For experimentation. | Experimental only |
+| **Ling-3.0-tiny-IQ2_M.gguf** | 2.52 GB | The smallest size most people find usable. | Memory-constrained |
+| **Ling-3.0-tiny-Q2_K_S.gguf** | 2.59 GB | Smaller than Q2_K, at a further quality cost. | Even smaller |
+| **Ling-3.0-tiny-IQ2_S.gguf** | 2.31 GB | Below the usual usability line. | Extreme compression |
+| **Ling-3.0-tiny-IQ2_XS.gguf** | 2.27 GB | Experimental. | Experimental only |
+| **Ling-3.0-tiny-IQ2_XXS.gguf** | 2.06 GB | Experimental. | Experimental only |
+| **Ling-3.0-tiny-Q2_0.gguf** | 2.28 GB | Extreme, group-64. Included for completeness. | Historical compatibility |
+| **Ling-3.0-tiny-IQ1_M.gguf** | 1.80 GB | Extreme. Expect substantial degradation. | Maximum compression |
+| **Ling-3.0-tiny-IQ1_S.gguf** | 1.64 GB | Extreme. Expect substantial degradation. | Maximum compression |
+| **Ling-3.0-tiny-Q1_0.gguf** | 1.21 GB | Extreme. Included for completeness. | Historical compatibility |
+*All files are cut from the BF16 source (14.72 GB). Each quant is independently uploaded and deleted immediately after successful upload to minimize peak disk usage.*
+## Model Architecture
+Ling-3.0-tiny features a unique hybrid architecture combining KDA/MLA attention with a sparse MoE FFN:
+### Attention Mechanism
+- **KDA (Kimi Delta Attention):** 3 layers per 4-layer block
+- **MLA (Multi-Head Latent Attention):** 1 layer per 4-layer block
+- **Hybrid Stacking:** 3:1 ratio for efficient long-context processing
+### Feed-Forward Network
+- **MoE (MultiplE Experts):** 128 total experts
+- **Routed Experts:** 8 activated per token
+- **Shared Expert:** 1 additional expert
+- **Activation Efficiency:** Only 1.3B of 7.9B parameters activated per token
+### Core Components
+- **Layers:** 24 total
+- **Hidden Size:** 1536
+- **Vocab Size:** 157184
+- **Position Embedding:** Rotary Position Embedding (RoPE)
+- **Precision:** bfloat16 (source)
+## Inference
+### Generation Parameters
+> **Important:** Use `temperature=1.0` and `top_p=0.95` across **all tasks and serving backends**, including general chat, reasoning, and tool calling.
+| Parameter | Value | Notes |
+|:----------|:------|:------|
+| `temperature` | `1.0` | Required for all modes |
+| `top_p` | `0.95` | Nucleus sampling threshold |
+| `top_k` | `20` | Recommended for stable generation |
+| `max_new_tokens` | `8192` | Thinking mode (increase for complex reasoning) |
+| `max_new_tokens` | `2048` | Non-thinking mode |
+| `do_sample` | `True` | Required when temperature > 0 |
+### Thinking Modes
+| Mode | Template Parameters | Behavior |
+|:-----|:-------------------|:---------|
+| **Thinking** (default) | `enable_thinking=True` | Full chain-of-thought reasoning inside `<think>...</think>` |
+| **Non-thinking** | `enable_thinking=False` | Direct answer with no reasoning overhead |
+| **Low-effort** | `enable_thinking=True, low_effort=True` | Brief reasoning for simpler queries |
+## Serving with llama.cpp
+### Basic Usage
 ```bash
+# Download a quantization (recommended: Q4_K_M)
+huggingface-cli download NANI-Nithin/Ling-3.0-tiny-GGUF Ling-3.0-tiny-Q4_K_M.gguf --local-dir .
+# Run inference
+llama-cli -m Ling-3.0-tiny-Q4_K_M.gguf -p "Hello, how are you?"
+```
+Or use the Hugging Face Hub integration:
+```bash
+# Serve with llama-server
+llama-server -hf NANI-Nithin/Ling-3.0-tiny-GGUF:Q4_K_M
+# Run with model path
 llama-cli -hf NANI-Nithin/Ling-3.0-tiny-GGUF:Q4_K_M -p "Hello"
 ```
+### API Usage
 ```bash
+# Start the server
+curl -s http://localhost:8080/v1/chat/completions \
+  -H "Content-Type: application/json" \
+  -d '{
+    "model": "granite-4.2-3b-Q4_K_M",
+    "messages": [{"role": "user", "content": "Explain quantum computing in simple terms"}],
+    "temperature": 1.0,
+    "top_p": 0.95,
+    "max_tokens": 8192
+  }'
+```
+## Quick Start Example
+```bash
+# Download the model
+huggingface-cli download NANI-Nithin/Ling-3.0-tiny-GGUF Ling-3.0-tiny-Q4_K_M.gguf
+# Run inference
+./llama-cli -m Ling-3.0-tiny-Q4_K_M.gguf -p "What is the Riemann hypothesis?"
 ```
+## Technical Details
+- **Source Model:** inclusionAI/Ling-3.0-tiny
+- **Author:** inclusionAI
+- **License:** MIT
+- **Architecture:** BailingMoeV3ForCausalLM (hybrid KDA/MLA + sparse MoE)
+- **Parameters:** 7.9B total, 1.3B activated per token
+- **Experts:** 128 total (8 routed + 1 shared per token)
+- **Context Length:** 131K tokens (natively supports 128K)
+- **Created:** August 10, 2026
+- **Languages:** Multiple languages supported
+## Usage Notes
+1. **Disk Space:** Download one quantization at a time. Each file ranges from 1.21 GB (Q1_0) to 14.72 GB (BF16 source).
+2. **Memory Requirements:** Varies by quantization. Q4_K_M requires ~4 GB VRAM, Q8_0 requires ~8 GB VRAM.
+3. **MoE Optimization:** The sparse MoE architecture provides efficient inference while maintaining broad capabilities.
+4. **Thinking Mode:** Enable `enable_thinking=True` to get chain-of-thought reasoning. Set to `False` for faster, direct answers.
+## Performance Characteristics
+Ling-3.0-tiny achieves impressive efficiency:
+- **FP8 Performance:** ~100-105 tokens/s on DGX Spark, 86-90 tokens/s on M4 Pro MacBook
+- **Memory Usage:** ~8.34 GB peak at 8K context length
+- **Agentic Performance:** Score of 25 on Artificial Analysis Intelligence Index v4.1.1
+- **End-to-End Latency:** ~18 seconds for 500-token response including reasoning
+## Model Card Information
+This GGUF repo contains quantized versions of [Ling-3.0-tiny](https://huggingface.co/inclusionAI/Ling-3.0-tiny), featuring a unique hybrid architecture combining KDA/MLA attention with sparse MoE for efficient reasoning and agentic capabilities. The quantization was performed using llama.cpp's quantization pipeline, preserving the model's MoE efficiency while reducing size for deployment.
+For the full source model documentation, including detailed training methodology, evaluation benchmarks, and advanced deployment recipes (SGLang, vLLM, Ollama), refer to the source repo: [inclusionAI/Ling-3.0-tiny](https://huggingface.co/inclusionAI/Ling-3.0-tiny).
 ---
+**Note:** These files are optimized for llama.cpp and are not compatible with vLLM, SGLang, or the Transformers library in their current format. For those frameworks, use the source model `inclusionAI/Ling-3.0-tiny`.