specklabs/Speck1.5-140M-GGUF overview
SpeckLabs ./assets/specklabs banner.png Speck1.5 140M GGUF llama.cpp compatible GGUF builds of specklabs/Speck1.5 140M https://huggingface.co/specklabs/Speck1.…
Runs locally from ~107.7 MB disk (4 GB VRAM class GPUs with llama.cpp / guIDE).
Repository Files & Downloads
Model Details
| Model ID | specklabs/Speck1.5-140M-GGUF |
|---|---|
| Author | specklabs |
| Pipeline | text-generation |
| License | mit |
| Base model | specklabs/Speck1.5-140M |
| Last modified | 2026-09-15T15:20:08.000Z |
Model README
---
license: mit
base_model: specklabs/Speck1.5-140M
library_name: llama.cpp
pipeline_tag: text-generation
tags:
- gguf
---
Speck1.5-140M GGUF
llama.cpp-compatible GGUF builds of specklabs/Speck1.5-140M, pinned to
source revision 449cc369329556fbd1e0143ca01c5b40ec4082f9.
| File | Quantization | Size |
| --- | --- | ---: |
| Speck1.5-140M-BF16.gguf | BF16 | 361.2 MB |
| Speck1.5-140M-Q4_K_M.gguf | Q4_K_M | 112.9 MB |
| Speck1.5-140M-Q5_K_M.gguf | Q5_K_M | 130.3 MB |
| Speck1.5-140M-Q8_0.gguf | Q8_0 | 192.3 MB |
Usage
llama-completion -hf specklabs/Speck1.5-140M-GGUF:Q4_K_M -p "The meaning of life is" -n 64
The source Speck architecture and llama.cpp's LFM2 runtime implement the same alternating
attention/short-convolution operators. Conversion folds the 640-to-768 input and 768-to-640
output adapters into the embeddings, zero-pads the 384-wide convolution channels to 768, and
left-pads 3-tap causal kernels to 5 taps. These transformations preserve the model function
apart from normal floating-point and quantization rounding.
The GGUF graph stores 180,160,768 parameters because the source's tied 640-wide embedding and
two adapters become separate 768-wide input and output matrices. This compatibility transform
does not add layers or model capacity.
The conversion was built with llama.cpp revision 2e88c49c90f0add8796f633fea8c3d65b975f295. Exact checksums and
conversion provenance are in conversion.json.
Run specklabs/Speck1.5-140M-GGUF with guIDE
Download guIDE — the AI-native code editor with local LLM inference and 69 built-in tools.
Source: Hugging Face · Compare models