---

license: other

base_model: ShaunGves/FastContext-1.0-4B-SFT

base_model_relation: quantized

pipeline_tag: text-generation

library_name: gguf

tags:

• gguf
• ollama
• local-llm
• llama.cpp
• lm-studio
• quantized
• imatrix
• sub-4-bit
• qwen3
• base_model:Qwen/Qwen3-4B-Instruct-2507

quantized_by: liodon-ai

---

FastContext-1.0-4B-SFT — iMatrix GGUF

GGUF quantizations of ShaunGves/FastContext-1.0-4B-SFT, published by Liodon AI.

Quick Start

llama.cpp

llama-cli -hf liodon-ai/FastContext-1.0-4B-SFT-imatrix-GGUF:Q4_K_M

Ollama

ollama run hf.co/liodon-ai/FastContext-1.0-4B-SFT-imatrix-GGUF:Q4_K_M

LM Studio / Jan — search liodon-ai/FastContext-1.0-4B-SFT-imatrix-GGUF and pick your quant.

Quants

| Quant | Size | VRAM est. | Notes |

|-------|------|-----------|-------|

| IQ2_M | 1.51 GB | ~2 GB | 2-bit, iMatrix — smallest usable |

| IQ3_M | 1.96 GB | ~2 GB | 3-bit, iMatrix — great quality/size tradeoff |

| IQ4_XS | 2.27 GB | ~3 GB | 4-bit extra-small, iMatrix |

| Q4_K_M | 2.50 GB | ~3 GB | 4-bit, iMatrix-calibrated (recommended) |

| Q5_K_M | 2.89 GB | ~3 GB | 5-bit, iMatrix-calibrated |

| Q6_K | 3.31 GB | ~4 GB | 6-bit, iMatrix-calibrated, near-lossless |

| Q8_0 | 4.28 GB | ~5 GB | 8-bit, essentially lossless |

What is iMatrix?

Standard quantization treats all weights equally. iMatrix runs 128 calibration chunks through

the full-precision model to find which weights matter most, then allocates more precision where

it counts. At Q2/Q3/Q4 this means noticeably better coherence and instruction-following —

same file size, better output.

Calibration: 2M tokens of WikiText-103.

> Also see plain (non-iMatrix) quants: liodon-ai/FastContext-1.0-4B-SFT-GGUF

Source

• Model: ShaunGves/FastContext-1.0-4B-SFT
• License: other

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Quantized by Liodon AI