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Open-weight model

qwen-2.5-1.5b-ft-Q8_0-GGUF

by Jesse andquant/qwen-2.5-1.5b-ft-Q8_0-GGUF

qwen-2.5-1.5b-ft-Q8_0-GGUF is an open-weight model from Jesse. Its published files total 1.6 GB.

This model was converted to GGUF format from andquant/qwen-2.5-1.5b-ft using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model.

Parameters—
Context—
Weights1.6 GB
License—
AccessOpen weights
Monthly Downloads—

Model Card

This model was converted to GGUF format from andquant/qwen-2.5-1.5b-ft using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model. Install llama.cpp through brew (works on Mac and Linux) Invoke the llama.cpp server or the CLI. Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well. Step 2: Move into the llama.cpp folder and build it with LLAMACURL=1 flag along with other hardware-specific flags (for ex: LLAMACUDA=1 for Nvidia GPUs on Linux).

Excerpt from the card by Jesse.

Identity and Version

Repository
andquant/qwen-2.5-1.5b-ft-Q8_0-GGUF
Publisher
Jesse
Task
Not stated by the source
Modality
Other
Library
transformers
Parameters
Not stated by the source
Languages
Not stated by the source
Revision
050bf35115f9a3a24eae18e413a1baae6d048271
First published
2026-10-16
Last updated
2026-10-16

Files and Weights

3 files, 1.6 GB in total. The weights are 1 file totalling 1.6 GB in gguf.

Weights1 file · 1.6 GB
Documentation1 file · 1.7 KB
Repository1 file · 1.6 KB
Every file
FileTypeSizeSHA-256
qwen-2.5-1.5b-ft-q8_0.ggufWeights1.6 GB 992606d74dbe
README.mdDocumentation1.7 KB —
.gitattributesRepository1.6 KB —

License and Download

License
Not stated by the source
Access
Open weights, no gate
Download size
1.6 GB
Download from Jesse

Released by Jesse through its official repository on Hugging Face.

Built From

Memory Requirements

PrecisionWeights in memory
As published1.6 GB

Weights only, from the published parameter count; the key-value cache and runtime add to this.