MachineLearningLM-7B-v1-GGUF GGUF download | Hugging Bay

Q: What is this model?

QuantFactory/MachineLearningLM-7B-v1-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/MachineLearningLM-7B-v1-GGUF · transformers

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/MachineLearningLM-7B-v1-GGUF has 7B parameters. Hosted here: 1 file, 4.68 GB. Full repo upstream: 69 GB. The recommended download, MachineLearningLM-7B-v1.Q4_K_M.gguf (Q4_K_M), is 4.68 GB.

Q: What can I download here?

Hosted here: 1 file, 4.68 GB. Full repo upstream: 69 GB. The original release is at https://huggingface.co/QuantFactory/MachineLearningLM-7B-v1-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-machinelearninglm-7b-v1-gguf/safety on Hugging Bay.

Q: Will QuantFactory/MachineLearningLM-7B-v1-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

MachineLearningLM-7B-v1-GGUF

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

QuantFactory/MachineLearningLM-7B-v1-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.

Can I run it?View as JSON →

Quick answer: compatibility and sources

Can I run MachineLearningLM-7B-v1-GGUF on my computer?

It depends on your memory, processor, file format and runtime. Hugging Bay offers 1 hosted file; check the file list for the exact selection. A file-size fit is an estimate, not a successful run or speed promise. Read the license before use.

MachineLearningLM-7B-v1-GGUF is an open-weights language model for text generation. Model weights are learned parameters; a runtime loads them. A hosted file is an individual file Hugging Bay can serve. SHA-256 identifies exact file bytes. The availability date above applies to this file record, not the upstream metadata update date.

As of the recorded file availability check on 2026-10-25.

Recorded SHA-256 for MachineLearningLM-7B-v1.Q4_K_M.gguf: 42739093804fdaaa2f86c9c8f18ff2e57adbeabf59092d19dc9111e75562247d. Exact file evidence · Machine-readable source facts · Run evidence and any recorded receipts. A checksum fingerprints only the named file bytes; it does not prove safety, quality, a complete multi-file selection or a successful run.

Updated 2026-10-21 · Page-copy source revision 8871bab84e1cf80aea1686a944ec335078a6c036 · Source files: apps/web/lib/model-page-template.mjs, apps/web/lib/model-page-v3.mjs, apps/web/lib/aeo-page.mjs · Full revision record

Model metadata: As of 2026-08-28 at the model source. Popularity counters are source-reported, not measured by Hugging Bay. Availability checked 2026-10-25.

Key facts

Catalog record: Summarizes upstream metadata; it is not itself a download or run receipt.
File evidence: Lists exact filenames, availability and any recorded SHA-256 for individual files.
Run evidence: Reports measured results only when a receipt exists for named file bytes and a setup; otherwise unmeasured.
Checksum scope: SHA-256 fingerprints one file’s bytes; it does not prove safety, quality or a multi-file bundle.

Evidence you can inspect (plain text)

This page’s quick answer names the file record for hf-model-quantfactory-machinelearninglm-7b-v1-gguf.
Open /artifact/hf-model-quantfactory-machinelearninglm-7b-v1-gguf/files for this exact filenames, availability and any recorded SHA-256.
Open /evidence/artifacts/hf-model-quantfactory-machinelearninglm-7b-v1-gguf for run-readiness evidence tied to that record; absent receipts stay unmeasured.
Open /api/artifacts/hf-model-quantfactory-machinelearninglm-7b-v1-gguf/card.md for machine-readable source facts (not a runtime test).
A matching checksum on this page fingerprints only the named bytes shown in file evidence.

Unmeasured states are stated explicitly; this page does not invent run scores or compatibility guarantees.

Which date applies to which fact

File availability check: 2026-10-25
Committed page-copy sources: 2026-10-21
Upstream model metadata: Dates on artifact records when shown; not implied by page-copy revision
Run receipts: Only on /evidence pages when recorded; otherwise unmeasured

Page-copy revision (Updated line above) identifies committed text sources, not upstream model edits or test outcomes.

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-machinelearninglm-7b-v1-gguf/MachineLearningLM-7B-v1.Q4_K_M.gguf",
    "MachineLearningLM-7B-v1.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 downloadMachineLearningLM-7B-v1.Q4_K_M.gguf4.68 GB · 1 file Download · 4.68 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	Tamanho	SHA-256	Origem	Baixar
Weights
`MachineLearningLM-7B-v1.Q4_K_M.gguf` Default	4.68 GB	`42739093…5562247d` `42739093804fdaaa2f86c9c8f18ff2e57adbeabf59092d19dc9111e75562247d`	Hosted	Baixar
`MachineLearningLM-7B-v1.Q2_K.gguf`	3.02 GB	`f3281528…2f15ad14` `f328152879880fd16e5cc51a2fd1d52e8098aea029e74376476e590b2f15ad14`	Upstream only	File metadata
`MachineLearningLM-7B-v1.Q3_K_L.gguf`	4.09 GB	`9b636d9c…ca22925f` `9b636d9c2c743615fc583b94b198fc6732e78e6f9cf900dc1a138d9fca22925f`	Upstream only	File metadata
`MachineLearningLM-7B-v1.Q3_K_M.gguf`	3.81 GB	`4c402413…c204ca9a` `4c402413490a262a09c30471f298115e28a265d24638780132e1ce5cc204ca9a`	Upstream only	File metadata
`MachineLearningLM-7B-v1.Q3_K_S.gguf`	3.49 GB	`f15b9456…1328d4d5` `f15b9456be905ba18aa9622ec8d46d606943cc410c0abaa1040acbfc1328d4d5`	Upstream only	File metadata
`MachineLearningLM-7B-v1.Q4_0.gguf`	4.43 GB	`e93f774e…bb79498c` `e93f774e3e5dab94426e99e30d843695c29fe297bad2a16fdc7e9b94bb79498c`	Upstream only	File metadata
`MachineLearningLM-7B-v1.Q4_1.gguf`	4.87 GB	`0f0518d8…bc533c50` `0f0518d8a98209cf497cdc9778698da705bda894d6a6296cafef7ccdbc533c50`	Upstream only	File metadata
`MachineLearningLM-7B-v1.Q4_K_S.gguf`	4.46 GB	`b5e00460…9735671d` `b5e0046046a09e8fc2aadd7425f63a4996ac23e0727845196ae015df9735671d`	Upstream only	File metadata
`MachineLearningLM-7B-v1.Q5_0.gguf`	5.32 GB	`42b6b4df…3ba126f7` `42b6b4dfd8cf69cb155a5ce11b3a61a37028677097bc56cd53a1cb493ba126f7`	Upstream only	File metadata
`MachineLearningLM-7B-v1.Q5_1.gguf`	5.76 GB	`f29cd340…3b6fd17b` `f29cd3402520bc4bac743f09eddd21be96eaea0585426c01de8206023b6fd17b`	Upstream only	File metadata
`MachineLearningLM-7B-v1.Q5_K_M.gguf`	5.44 GB	`315dd530…1ffdf280` `315dd5305f53fbb83f00234a32744574426fe4f0154efd1fbb96c6ae1ffdf280`	Upstream only	File metadata
`MachineLearningLM-7B-v1.Q5_K_S.gguf`	5.32 GB	`37bd53fd…fd9c6709` `37bd53fd56cca97a896862823b100c33d46672dfb26ebf82117e7e58fd9c6709`	Upstream only	File metadata
`MachineLearningLM-7B-v1.Q6_K.gguf`	6.25 GB	`06a47aa6…16152373` `06a47aa6da29d6eee2cd3241c50e658d61f1318d9ca8e6bbf5f8684f16152373`	Upstream only	File metadata
`MachineLearningLM-7B-v1.Q8_0.gguf`	8.1 GB	`6cc7ce6b…2641e007` `6cc7ce6b7fc70465e4e86c4a4bc1e1e117140b208b5426361a245f5f2641e007`	Upstream only	File metadata
Docs
`.gitattributes`	2.51 KB	—	Upstream only	File metadata
`README.md`	8.64 KB	—	Upstream only	File metadata

Verificar um download

In the folder where you saved MachineLearningLM-7B-v1.Q4_K_M.gguf, run:

macOS

shasum -a 256 -- 'MachineLearningLM-7B-v1.Q4_K_M.gguf'

Linux

sha256sum -- 'MachineLearningLM-7B-v1.Q4_K_M.gguf'

Windows

Get-FileHash -LiteralPath 'MachineLearningLM-7B-v1.Q4_K_M.gguf' -Algorithm SHA256

Compare the result with the recorded SHA-256.