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specklabs/Speck1.1-140M-Instruct-GGUF overview

SpeckLabs ./assets/specklabs banner.png Speck1.1 140M Instruct GGUF llama.cpp compatible GGUF builds of specklabs/Speck1.1 140M Instruct https://huggingface.co…

llama.cppggufconversationaltext-generationbase_model:specklabs/Speck1.1-140M-Instructbase_model:quantized:specklabs/Speck1.1-140M-Instructlicense:mitendpoints_compatibleregion:us

Runs locally from ~107.7 MB disk (4 GB VRAM class GPUs with llama.cpp / guIDE).

Downloads
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Pipeline
text-generation
Author

Repository Files & Downloads

4 GGUF files detected
Direct downloads for local inference
FileTypeQuantizationSizeLink
Speck1.1-140M-Instruct-BF16.ggufGGUFBF16344.5 MBDownload
Speck1.1-140M-Instruct-Q4_K_M.ggufGGUFQ4_K_M107.7 MBDownload
Speck1.1-140M-Instruct-Q5_K_M.ggufGGUFQ5_K_M124.2 MBDownload
Speck1.1-140M-Instruct-Q8_0.ggufGGUFQ8_0183.4 MBDownload

Model Details

Model IDspecklabs/Speck1.1-140M-Instruct-GGUF
Authorspecklabs
Pipelinetext-generation
Licensemit
Base modelspecklabs/Speck1.1-140M-Instruct
Last modified2026-09-15T15:20:00.000Z

Model README

---

license: mit

base_model: specklabs/Speck1.1-140M-Instruct

library_name: llama.cpp

pipeline_tag: text-generation

tags:

- gguf

- conversational

---

!SpeckLabs

Speck1.1-140M-Instruct GGUF

llama.cpp-compatible GGUF builds of specklabs/Speck1.1-140M-Instruct, pinned to

source revision 8370432f0f36a6d0b829a50eabfad5476ca37643.

| File | Quantization | Size |

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

| Speck1.1-140M-Instruct-BF16.gguf | BF16 | 361.2 MB |

| Speck1.1-140M-Instruct-Q4_K_M.gguf | Q4_K_M | 112.9 MB |

| Speck1.1-140M-Instruct-Q5_K_M.gguf | Q5_K_M | 130.3 MB |

| Speck1.1-140M-Instruct-Q8_0.gguf | Q8_0 | 192.4 MB |

Usage

llama-cli -hf specklabs/Speck1.1-140M-Instruct-GGUF:Q4_K_M -cnv

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 180,165,376 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.

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