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NANI-Nithin/
Ling-3.0-tiny-GGUF

Name: Ling-3.0-tiny-GGUF
Availability: InStock

inclusionAI/Ling-3.0-tiny

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Ling-3.0-tiny GGUF (llama.cpp)

Backend

Quantized GGUF files for Ling-3.0-tiny, IBM's lightweight hybrid reasoning MoE model optimized for deployment with 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

Disk Space: Download one quantization at a time. Each file ranges from 1.21 GB (Q1_0) to 14.72 GB (BF16 source).
Memory Requirements: Varies by quantization. Q4_K_M requires ~4 GB VRAM, Q8_0 requires ~8 GB VRAM.
MoE Optimization: The sparse MoE architecture provides efficient inference while maintaining broad capabilities.
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, 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.

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.