sizzlebop/Glimmer-1-Base-GGUF

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.

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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

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Prompt Format and Usage

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

The

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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.