tinyllama-coder-py-4bit-v7 GGUF download | Hugging Bay

Q: What is this model?

Ramikan-BR/tinyllama-coder-py-4bit-v7 is an open-weights language model for general published by Ramikan-BR, added to Hugging Bay Jun 30, 2026. Model from Hugging Face for llm: Ramikan-BR/tinyllama-coder-py-4bit-v7 · 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?

Hosted here: at least 8 listed files; full count and size unknown. Full repo upstream: 8.44 GB. The recommended download, model.safetensors, is 2.2 GB.

Q: What can I download here?

Hosted here: at least 8 listed files; full count and size unknown. Full repo upstream: 8.44 GB. The original release is at https://huggingface.co/Ramikan-BR/tinyllama-coder-py-4bit-v7.

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-ramikan-br-tinyllama-coder-py-4bit-v7/safety on Hugging Bay.

Q: Will Ramikan-BR/tinyllama-coder-py-4bit-v7 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.

Ramikan-BR

tinyllama-coder-py-4bit-v7

Can I run tinyllama-coder-py-4bit-v7 on my computer?

It depends on your memory, processor, file format and runtime. Hugging Bay offers 8 hosted files; 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.

tinyllama-coder-py-4bit-v7 is an open-weights language model for general. Model weights are learned parameters; a runtime loads them. Hosting files does not prove that a complete model runs.

As of the availability check on 2026-10-22.

Recorded SHA-256 for model.safetensors: b0fb321f7aee9600127536dee8185ee1dfbb7086654a44926c9bc666c5afb9a0. File evidence · Machine-readable facts · Run evidence and any recorded receipts · Proof Lab measurement policy. A checksum verifies byte identity, not safety; this answer asserts no measured runtime result.

Updated 2026-10-21 · Page text: As of 2026-10-21 · Content revision

Model metadata: As of 2026-09-04 at the model source. Popularity counters are source-reported, not measured by Hugging Bay. Availability checked 2026-10-22.

Ramikan-BR/tinyllama-coder-py-4bit-v7

Model details

generalLicense: apache-2.0SHA-256 recorded for 12 of 13 filessafetensors · BIN · GGUF

Content controls: not assessed.

The catalog lists this model for having conversations. Catalog sources: license, cardMetadata.license, baseModel, tags.

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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-ramikan-br-tinyllama-coder-py-4bit-v7/model.safetensors",
    "model.safetensors",
)

Other ways

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

Selected downloadmodel.safetensors2.2 GB · 1 file Download · 2.2 GB (1 file)

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

8 hosted of 13 listed

Hashes recorded. Date not recorded.

File	Tamanho	SHA-256	Origem	Baixar
Weights
`model.safetensors` Default	2.2 GB	`b0fb321f…c5afb9a0` `b0fb321f7aee9600127536dee8185ee1dfbb7086654a44926c9bc666c5afb9a0`	Hosted	Baixar
`pytorch_model.bin`	2.2 GB	`2e987ea8…33982e2d` `2e987ea8d781db92ae91347e9c6607fada7baa89b3cf727f9a1f152933982e2d`	Upstream only	File metadata
`tinyllama-coder-py-4bit-v7-unsloth.F16.gguf`	2.2 GB	`4c1d3b65…ed026408` `4c1d3b656fa3e34bca81c809a7651010178628e00718a22854e1acf2ed026408`	Upstream only	File metadata
`tinyllama-coder-py-4bit-v7-unsloth.Q4_K_M.gguf`	667.8 MB	`f0c6b65d…367dd7a0` `f0c6b65d1a7cbf703eaa3937760fa6af61ee021556bf1b1bee572040367dd7a0`	Upstream only	File metadata
`tinyllama-coder-py-4bit-v7-unsloth.Q8_0.gguf`	1.17 GB	`19ca5586…6a6b432e` `19ca5586628c369f7bca0720ba2c3dff35443f920c1853110613392a6a6b432e`	Upstream only	File metadata
Tokenizer & config
`config.json`	29 bytes	`5007d686…012a5e10` `5007d686e527eae088f05b0d7061d59491416814bc21ee501895416a012a5e10`	Hosted	Baixar
`generation_config.json`	124 bytes	`9797a637…a335b032` `9797a6378b4d0569d278ba6eea55bd23b8bf6c9499e0f565107088a3a335b032`	Hosted	Baixar
`special_tokens_map.json`	552 bytes	`4859e5db…b2166e7f` `4859e5dbde90e059988a0a2136d8df3f2773d4d2fc4c4543690028f0b2166e7f`	Hosted	Baixar
`tokenizer.model`	499.7 KB	`9e556afd…1818d347` `9e556afd44213b6bd1be2b850ebbbd98f5481437a8021afaf58ee7fb1818d347`	Hosted	Baixar
`tokenizer_config.json`	1.32 KB	`31fd6ba2…60fd9b2b` `31fd6ba2d0136fdcdb2aff3b66ec220374a7c970f91dbdc18955686460fd9b2b`	Hosted	Baixar
`tokenizer.json`	1.84 MB	`bf467c9e…b205df83` `bf467c9e0f536bda271283c6ef85eb1a943e3196b621c8a912d64953b205df83`	Hosted	Baixar
Docs
`.gitattributes`	1.76 KB	—	Upstream only	File metadata
`README.md`	591 bytes	`d5b3dbba…47979aaf` `d5b3dbba2620fdbaa10a3d6d5b3ad459d7967f54e47f6962a441fa3947979aaf`	Hosted	Baixar

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tinyllama-coder-py-4bit-v7

Can I run tinyllama-coder-py-4bit-v7 on my computer?

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