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sizzlebop/Glimmer-1-Base-GGUF overview

Glimmer 1 Base GGUF This repository provides GGUF quantizations for Glint Research/Glimmer 1 Base https://huggingface.co/Glint Research/Glimmer 1 Base . Glimme…

transformersggufllama-cpptext-generationtinyexperimentalsmall-language-modelcompactaiglintresearchenbase_model:Glint-Research/Glimmer-1-Basebase_model:quantized:Glint-Research/Glimmer-1-Baselicense:mitendpoints_compatibleregion:us

Runs locally from ~0.0 MB disk (4 GB VRAM class GPUs with llama.cpp / guIDE).

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text-generation
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Repository Files & Downloads

7 GGUF files detected
Direct downloads for local inference
FileTypeQuantizationSizeLink
Glimmer-1-Base-F16.ggufGGUFF160.0 MBDownload
Glimmer-1-Base-Q2_K.ggufGGUFQ2_K0.0 MBDownload
Glimmer-1-Base-Q3_K_M.ggufGGUFQ3_K_M0.0 MBDownload
Glimmer-1-Base-Q4_K_M.ggufGGUFQ4_K_M0.0 MBDownload
Glimmer-1-Base-Q5_K_M.ggufGGUFQ5_K_M0.0 MBDownload
Glimmer-1-Base-Q6_K.ggufGGUFQ6_K0.0 MBDownload
Glimmer-1-Base-Q8_0.ggufGGUFQ8_00.0 MBDownload

Model Details

Model IDsizzlebop/Glimmer-1-Base-GGUF
Authorsizzlebop
Pipelinetext-generation
Licensemit
Base modelGlint-Research/Glimmer-1-Base
Last modified2026-09-10T01:52:27.000Z

Model README

---

base_model: Glint-Research/Glimmer-1-Base

library_name: transformers

pipeline_tag: text-generation

license: mit

language:

  • en

tags:

  • gguf
  • llama-cpp
  • text-generation
  • tiny
  • experimental
  • small-language-model
  • compactai
  • glintresearch

---

Glimmer-1 Base GGUF

This repository provides GGUF quantizations for Glint-Research/Glimmer-1-Base.

Glimmer-1 is an extreme Small Language Model (SLM) exploring the lower bounds of language model scale. Featuring approximately 11,900 parameters across 2 layers with a hidden dimension of 16, it was trained on 500K tokens of FineWeb-Edu. It serves as an experimental artifact for architectural exploration, micro-benchmarking, and educational deployment on ultra-minimal microcontrollers.

All GGUF files were converted from the original safetensors weights using llama.cpp at native F16 precision, followed by standard quantization passes.

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Available Files and Quantizations

| File | Quant Type | Size | Description / Recommendation |

| :--- | :--- | :--- | :--- |

| Glimmer-1-Base-F16.gguf | F16 | 38.22 KB | Full precision base conversion. Highest fidelity reference weights. |

| Glimmer-1-Base-Q8_0.gguf | Q8_0 | 38.22 KB | Near-lossless quantization (retains F16 tensors where dimensions < block size). |

| Glimmer-1-Base-Q6_K.gguf | Q6_K | 38.22 KB | High quality retention variant. |

| Glimmer-1-Base-Q5_K_M.gguf | Q5_K_M | 38.22 KB | Compact representation. |

| Glimmer-1-Base-Q4_K_M.gguf | Q4_K_M | 38.22 KB | Recommended default for microcontrollers, embedded hardware, and edge toys. |

| Glimmer-1-Base-Q3_K_M.gguf | Q3_K_M | 38.22 KB | Compact footprint. |

| Glimmer-1-Base-Q2_K.gguf | Q2_K | 38.22 KB | Maximum compression variant. |

Note: Due to the micro-dimensions of this model (hidden size 16, intermediate size 24), tensor columns are smaller than standard quantization block boundaries (32/256), resulting in automatic fallback to high-precision storage across quantizations.

---

Model Architecture Details

  • Base Architecture: LlamaForCausalLM (llama)
  • Parameters: ~11,900
  • Layers: 2
  • Hidden Size: 16
  • Intermediate Size: 24
  • Attention Heads: 4 query heads / 1 KV head (GQA), head dimension 4
  • Context Length: 512 tokens
  • Vocabulary Size: 512 (compact custom BPE)
  • RoPE Theta: 10,000.0

---

Prompt Format and Usage

Glimmer-1-Base is a raw pretrained base model without instruction fine-tuning:

The

---

Quickstart Guide

1. llama.cpp

Run text completion with llama-cli:

llama-cli -m ./Glimmer-1-Base-Q4_K_M.gguf \
    -p "The story began when" \
    -n 64 \
    --temp 0.8

Launch a local inference server with llama-server:

llama-server -m ./Glimmer-1-Base-Q4_K_M.gguf \
    --port 8080 \
    -c 512

2. Ollama

Create a Modelfile:

FROM ./Glimmer-1-Base-Q4_K_M.gguf
PARAMETER temperature 0.8

Build and run:

ollama create glimmer-base -f Modelfile
ollama run glimmer-base "Once upon"

3. LM Studio

  1. Copy the .gguf file to your LM Studio models directory.
  2. Select Glimmer-1-Base-Q4_K_M.gguf.
  3. Set prompt template to raw text completion.

Run sizzlebop/Glimmer-1-Base-GGUF with guIDE

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