MathCoder2-CodeLlama-7B-GGUF GGUF download | Hugging Bay

Q: What is this model?

QuantFactory/MathCoder2-CodeLlama-7B-GGUF is an open-weights language model for text generation published by QuantFactory, added to Hugging Bay Jun 30, 2026. Model from Hugging Face for text generation: QuantFactory/MathCoder2-CodeLlama-7B-GGUF · unknown

Q: Can I use it commercially?

It is released under the apache-2.0 license. Commercial use is allowed. Permissive open license: commercial use, modification, and redistribution are permitted.

Q: How big is it?

QuantFactory/MathCoder2-CodeLlama-7B-GGUF has 7B parameters. Hosted here: 1 file, 4.08 GB. Full repo upstream: 60.2 GB. The recommended download, MathCoder2-CodeLlama-7B.Q4_K_M.gguf (Q4_K_M), is 4.08 GB.

Q: What can I download here?

Hosted here: 1 file, 4.08 GB. Full repo upstream: 60.2 GB. The original release is at https://huggingface.co/QuantFactory/MathCoder2-CodeLlama-7B-GGUF.

Q: File pattern scans

Automated file-pattern scans flagged nothing; not a safety guarantee. 1 hosted file carries a published SHA-256 you can re-hash. The machine-readable verdict is at /api/artifacts/hf-model-quantfactory-mathcoder2-codellama-7b-gguf/safety on Hugging Bay.

Q: Will QuantFactory/MathCoder2-CodeLlama-7B-GGUF run on my computer?

We can’t check the fit; we don’t know your machine. We don’t have your machine’s specs, so no check ran and no verdict is shown. Answer two quick questions and we’ll estimate, from the memory you tell us, whether it fits your machine. Or skip it; you can still download; fit isn’t checked.

QuantFactory

MathCoder2-CodeLlama-7B-GGUF

Try browser chat with a small demo model (a separate model; device support is checked before loading).

QuantFactory/MathCoder2-CodeLlama-7B-GGUF

Model details

text generationLicense: apache-2.0SHA-256 recorded for 14 of 16 filesGGUF

Content controls: not assessed.

The catalog lists this model for generating text. Catalog sources: license, cardMetadata.license, task, cardMetadata.pipelineTag.

View as JSON →

Your next step

Use it from code

This Python snippet downloads the selected file. Running the model needs its own library and setup.

from urllib.request import urlretrieve

urlretrieve(
    "https://huggingbay.xyz/api/downloads/hf-model-quantfactory-mathcoder2-codellama-7b-gguf/MathCoder2-CodeLlama-7B.Q4_K_M.gguf",
    "MathCoder2-CodeLlama-7B.Q4_K_M.gguf",
)

Other ways

Run it on your computerOpen in Hugging Bay appSend to a computerMac (Apple silicon) and Linux x86_64 installs are listed on the download page. Windows and Intel Mac are not yet. The app checks this model before you choose to download.

1 hosted of 16 listed · Hashes recorded. Date not recorded.

Selected downloadMathCoder2-CodeLlama-7B.Q4_K_M.gguf4.08 GB · 1 file Download · 4.08 GB (1 file)

Open-file help

Content controls: not assessed.

Check your download →

Compare your file’s SHA-256 with the recorded hash.

1 hosted of 16 listed

Hashes recorded. Date not recorded.

File	आकार	SHA-256	स्रोत	डाउनलोड करें
Weights
`MathCoder2-CodeLlama-7B.Q4_K_M.gguf` Default	4.08 GB	`3b72122b…0c75ea86` `3b72122bdf0ce4ff0e5998e65cdef5f65c97a21f6b3ddc7809a536150c75ea86`	Hosted	डाउनलोड करें
`MathCoder2-CodeLlama-7B.Q2_K.gguf`	2.53 GB	`65349002…d064d59a` `65349002a808cf3251a6b521b1490037aec677948faf185b83885401d064d59a`	Upstream only	File metadata
`MathCoder2-CodeLlama-7B.Q3_K_L.gguf`	3.6 GB	`d50f9f0d…00025832` `d50f9f0dd09de2f521213e91495d42ff1e19bc5b3148da00ebb4e9b100025832`	Upstream only	File metadata
`MathCoder2-CodeLlama-7B.Q3_K_M.gguf`	3.3 GB	`cef3d4dc…1b65c40c` `cef3d4dc92e9390a84f78c89fd8fce22374b0b2a9aacb4078064ca971b65c40c`	Upstream only	File metadata
`MathCoder2-CodeLlama-7B.Q3_K_S.gguf`	2.95 GB	`ebd2e61f…a0c57a47` `ebd2e61fc0437762e23db9e7b8cc077519e318ba3a4e8905b8984094a0c57a47`	Upstream only	File metadata
`MathCoder2-CodeLlama-7B.Q4_0.gguf`	3.83 GB	`735e2bb8…456ba8c8` `735e2bb83bd66abe221a1936b59920999c47be05196a49e83a0515b1456ba8c8`	Upstream only	File metadata
`MathCoder2-CodeLlama-7B.Q4_1.gguf`	4.24 GB	`ebb29182…239a2fbc` `ebb2918244c018d8c69ab1c63bd557d1044113da9a438e86832a1be7239a2fbc`	Upstream only	File metadata
`MathCoder2-CodeLlama-7B.Q4_K_S.gguf`	3.86 GB	`437752f6…021c2d5d` `437752f61583a4ed61cf23d2aec3161437244189c512a3a285e96ff5021c2d5d`	Upstream only	File metadata
`MathCoder2-CodeLlama-7B.Q5_0.gguf`	4.65 GB	`c365fcdb…d7034894` `c365fcdbad61213d32eb07e2285357bf5d2268a1064e845f685796d1d7034894`	Upstream only	File metadata
`MathCoder2-CodeLlama-7B.Q5_1.gguf`	5.06 GB	`0c605fdf…e5eee9b1` `0c605fdf53908e5d3718d80c4c44c299127dc5fcee62edc0ab028480e5eee9b1`	Upstream only	File metadata
`MathCoder2-CodeLlama-7B.Q5_K_M.gguf`	4.78 GB	`d9b259c3…e01327c5` `d9b259c344c89a7fd0972566d3fd049cf986c2e8d9417ede6a8061b1e01327c5`	Upstream only	File metadata
`MathCoder2-CodeLlama-7B.Q5_K_S.gguf`	4.65 GB	`08e394b1…a1992271` `08e394b11b96218daf6c6134f0b184fdf748e71b7bd003044cc42bcfa1992271`	Upstream only	File metadata
`MathCoder2-CodeLlama-7B.Q6_K.gguf`	5.53 GB	`8d49e6cb…7d1a1692` `8d49e6cb9faa7e330529860d7f109c3913f6f2cf48001153340dce3d7d1a1692`	Upstream only	File metadata
`MathCoder2-CodeLlama-7B.Q8_0.gguf`	7.16 GB	`8c38707c…dd204eee` `8c38707c477691aa0d137856fc9981dcdaa0781d2f5f30322cf36f4fdd204eee`	Upstream only	File metadata
Docs
`.gitattributes`	2.51 KB	—	Upstream only	File metadata
`README.md`	2.35 KB	—	Upstream only	File metadata

डाउनलोड की जाँच करें

In the folder where you saved MathCoder2-CodeLlama-7B.Q4_K_M.gguf, run:

macOS

shasum -a 256 -- 'MathCoder2-CodeLlama-7B.Q4_K_M.gguf'

Linux

sha256sum -- 'MathCoder2-CodeLlama-7B.Q4_K_M.gguf'

Windows

Get-FileHash -LiteralPath 'MathCoder2-CodeLlama-7B.Q4_K_M.gguf' -Algorithm SHA256

Compare the result with the recorded SHA-256.

MathCoder2-CodeLlama-7B-GGUF

डाउनलोड की जाँच करें

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