CodeRankLLM-mlx safetensors download (4.28 GB) | Hugging Bay

Q: What is this model?

arthurcollet/CodeRankLLM-mlx is an open-weights language model for text generation published by arthurcollet, added to Hugging Bay Sep 30, 2026. Model from Hugging Face for text generation: arthurcollet/CodeRankLLM-mlx · mlx

Q: Can I use it commercially?

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

Q: How big is it?

Hosted here: 8 files, 4.29 GB. Full repo upstream: 4.3 GB. The recommended download, model.safetensors, is 4.28 GB.

Q: What can I download here?

Hosted here: 8 files, 4.29 GB. Full repo upstream: 4.3 GB. The original release is at https://huggingface.co/arthurcollet/CodeRankLLM-mlx.

Q: File pattern scans

Automated file-pattern scans flagged nothing; not a safety guarantee. 8 hosted files carry a published SHA-256 you can re-hash. The machine-readable verdict is at /api/artifacts/hf-model-arthurcollet-coderankllm-mlx/safety on Hugging Bay.

Q: Will arthurcollet/CodeRankLLM-mlx 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.

arthurcollet

CodeRankLLM-mlx

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

arthurcollet/CodeRankLLM-mlx

Model details

text generationLicense: mitSHA-256 recorded for 8 of 13 filessafetensors

Content controls: not assessed.

The catalog lists this model for having conversations. Catalog sources: license, cardMetadata.license, baseModel, tags, 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-arthurcollet-coderankllm-mlx/model.safetensors",
    "model.safetensors",
)

Other ways

This file cannot open in the app. The app supports one GGUF file at a time.

See app requirements

8 hosted of 13 listed · Hashes recorded. Date not recorded.

Selected downloadmodel.safetensors4.28 GB · 1 file Download · 4.28 GB (1 file)

Open-file help

Content controls: not assessed.

Check your download →

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

8 hosted of 13 listed

Hashes recorded. Date not recorded.

File	आकार	SHA-256	स्रोत	डाउनलोड करें
Weights
`model.safetensors` Default	4.28 GB	`84620de4…1cb4baa9` `84620de4bcc2cc862cd2013ac16651eac564fc8c33b87b748f25017a1cb4baa9`	Hosted	डाउनलोड करें
Tokenizer & config
`added_tokens.json`	605 bytes	`58b54bbe…1968060b` `58b54bbe36fc752f79a24a271ef66a0a0830054b4dfad94bde757d851968060b`	Hosted	डाउनलोड करें
`chat_template.jinja`	2.51 KB	`cd8e9439…2330527f` `cd8e9439f0570856fd70470bf8889ebd8b5d1107207f67a5efb46e342330527f`	Hosted	डाउनलोड करें
`config.json`	865 bytes	`c43481a8…ee0e61a4` `c43481a8894a312132ca697186d860c1e58536a758db4628fe561405ee0e61a4`	Hosted	डाउनलोड करें
`generation_config.json`	242 bytes	`84ac2065…be2c2f79` `84ac2065d654f0cdefcbdbeac7e8c0cfb41f965d8523698759cec680be2c2f79`	Hosted	डाउनलोड करें
`merges.txt`	1.67 MB	—	Upstream only	File metadata
`model.safetensors.index.json`	51.8 KB	—	Upstream only	File metadata
`special_tokens_map.json`	613 bytes	`76862e76…dd3a1ecd` `76862e765266b85aa9459767e33cbaf13970f327a0e88d1c65846c2ddd3a1ecd`	Hosted	डाउनलोड करें
`tokenizer.json`	11.4 MB	`a0a96a2b…2234becd` `a0a96a2b2c22a44bee94246254c304abb266f1ef56ff28ec87eb85422234becd`	Hosted	डाउनलोड करें
`tokenizer_config.json`	4.88 KB	`b1a92947…ac9b89cf` `b1a92947af6428e06ecb378a1c9676120c4651363f4aed98e12c2253ac9b89cf`	Hosted	डाउनलोड करें
`vocab.json`	2.78 MB	—	Upstream only	File metadata
Docs
`.gitattributes`	1.57 KB	—	Upstream only	File metadata
`README.md`	806 bytes	—	Upstream only	File metadata

डाउनलोड की जाँच करें

In the folder where you saved model.safetensors, run:

macOS

shasum -a 256 -- 'model.safetensors'

Linux

sha256sum -- 'model.safetensors'

Windows

Get-FileHash -LiteralPath 'model.safetensors' -Algorithm SHA256

Compare the result with the recorded SHA-256.

CodeRankLLM-mlx

डाउनलोड की जाँच करें

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