rao-studios/Qwen2.5-Coder-14B-Instruct-mlx-4bit

Rao Studios' mirror of mlx-community/Qwen2.5-Coder-14B-Instruct-4bit at commit 29efdbab55a161237ab1e432a3abaf6c7ae2b477. The weights, tokenizer, chat template and configuration are byte-for-byte that repository's; nothing was re-quantized or edited. This card and LICENSE (the Apache License 2.0, verbatim from Qwen/Qwen2.5-Coder-14B-Instruct at aedcc2d42b622764e023cf882b6652e646b95671) were added.

It exists so every install of Craft fetches the same bytes from a repository Rao controls: Craft's onboarding recommends it by this Mac's memory, and pins this repository at one commit.

Lineage

• Qwen/Qwen2.5-Coder-14B-Instruct, by the Qwen team (Apache-2.0).
• mlx-community/Qwen2.5-Coder-14B-Instruct-4bit: that model converted to MLX at 4 bits — the files here.

Shape (from config.json)

Architecture	qwen2, 48 layers
Quantization	4-bit, group size 64
Weights	8.3 GB of safetensors, 1,255 tensors
Context	32,768 tokens
Tools	Hermes JSON: the chat template lists every tool's JSON schema inside <tools>, and the model answers with one JSON object per call inside <tool_call> tags

A call, as the model writes it:

<tool_call>
{"name": "edit_file", "arguments": {"path": "Sources/App/main.swift", "old_string": "print(\"hi\")", "new_string": "print(\"hello\")"}}
</tool_call>

Safety pass

Run by Craft's scripts/mirror-model.py on 2026-10-26, before upload:

• Every file is one an MLX model needs; no pickle format and no native code was in the source.
• Every weight file is safetensors, its header read by hand: known dtypes, tensors that tile the file exactly, the same tensor names as the index.
• Every file matches the SHA-256 the Hub recorded for it at 29efdbab55a161237ab1e432a3abaf6c7ae2b477.
• No auto_map (remote code) in the config or the tokenizer config; a chat template is present.

File	Bytes	SHA-256
added_tokens.json	605	58b54bbe36fc752f79a24a271ef66a0a0830054b4dfad94bde757d851968060b
config.json	867	7a2e85de5e0e115e6aaea7cb71091c5833aa29786fc748ef1d7e607e3a3a18f2
merges.txt	1,671,853	8831e4f1a044471340f7c0a83d7bd71306a5b867e95fd870f74d0c5308a904d5
model-00001-of-00002.safetensors	5,353,840,273	784b0c89d3e006f49cb8547aba1f2f4e99b365ab4380cf202655ca04c87d7ac3
model-00002-of-00002.safetensors	2,955,653,960	a1b7516f1926070177601698661494dfc68c2de1d98e7c43ba910a0d1d7dfbbe
model.safetensors.index.json	107,336	635d985ebf00b6a25daba71b7eba4d5ec8be7902c15104a79312fe947f44b121
special_tokens_map.json	613	76862e765266b85aa9459767e33cbaf13970f327a0e88d1c65846c2ddd3a1ecd
tokenizer.json	7,031,673	a8506e7111b80c6d8635951a02eab0f4e1a8e4e5772da83846579e97b16f61bf
tokenizer_config.json	7,306	7e88129d9769a0b14b1587a7d5e829fe93ac0e1511636471fdfc0811951418e6
vocab.json	2,776,833	ca10d7e9fb3ed18575dd1e277a2579c16d108e32f27439684afa0e10b1440910

Use with mlx

pip install mlx-lm
mlx_lm.generate --model rao-studios/Qwen2.5-Coder-14B-Instruct-mlx-4bit --prompt "Write a binary search in Swift."