VertexAGI/codeinswift-1-small

CodeInSwift 1 Small

A Swift coding specialist, distilled from real instruction/response data into Qwen3-8B

Part of the CodeIn family: where Aquamarine is a generalist across languages, CodeIn models go the other direction -- each one distilled for exactly one programming language, and nothing else. CodeInSwift is the second: Apple's Swift.

Overview

CodeInSwift 1 Small is fine-tuned via LoRA on Qwen3-8B to act as a Swift coding assistant -- given a problem description, it explains its approach and writes complete, correct Swift code, as a real chat model (not raw code completion, unlike its CodeInLuau sibling, since this dataset is genuine 2-turn instruction/response data).

Training

• Base model: mlx-community/Qwen3-8B-4bit
• Dataset: MnemicAI/Ling-Coder-SFT-English-Clean, Swift split -- 68,301 rows of genuine human/assistant coding Q&A, split 92/8 into 62,837 train / 5,464 validation examples.
• Method: LoRA fine-tuning (rank 8, scale 20, 16 layers), 9,600 iterations across 3 resumed sessions, batch size 1, sequence length 3072, trained as a real chat model via a fixed system prompt + messages format.
• Val loss: 0.652 at the end of training.

Evaluation

A held-out base-vs-tuned comparison used 100 prompts sampled from the dataset's own validation split (valid.jsonl, never touched during training), each fed the same system prompt and user question, with greedy decoding capped at 512 output tokens. Outputs were classified by extracting any ```swift code block and running it through the real Swift compiler (swiftc -typecheck) -- a genuine correctness oracle, not a heuristic or LLM judge.

	compiles cleanly	compile error	no code block reached
Base Qwen3-8B	0	0	100
CodeInSwift 1 Small	38	19	43

The base model's 0/100 is not a claim that it can't write Swift -- Qwen3-8B is a reasoning model, and unprompted it spends the entire 512-token budget inside a <think> block reasoning through the approach, never reaching a code fence at all (verified by inspecting the raw transcripts). CodeInSwift has learned to skip that and deliver a working, correctly-formatted answer inside a practical response budget, which is what actually matters for an interactive coding assistant. Even so, the tuned model's own remaining 43% no-code-block rate and 19% compile-error rate are real, unresolved gaps -- reported here rather than glossed over.

Usage

from mlx_lm import load, generate

model, tokenizer = load("VertexAGI/codeinswift-1-small")

messages = [
    {"role": "system", "content": "You are CodeInSwift, a coding specialist for the Swift programming language. Given a coding problem, explain your problem-solving approach clearly, then provide complete, correct Swift code."},
    {"role": "user", "content": "Write a function that reverses a linked list in place."},
]
prompt = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)
response = generate(model, tokenizer, prompt=prompt, max_tokens=512)
print(response)

This is a chat model -- use the system prompt above (or your own) plus a real question, not a raw code prefix.

Formats available

MLX only for this release (4-bit, via mlx-lm, for Apple Silicon). No GGUF yet -- same reason as CodeInLuau: the dequantize-to-fp16-then-quantize conversion path needs disk headroom this machine didn't have available at release time; may follow later.

Limitations

An 8B-parameter model fine-tuned via LoRA on ~63K examples. The evaluation above shows real, unresolved gaps -- a meaningful fraction of responses either don't produce a compilable code block within a practical response length or contain genuine compile errors. Treat output as a strong starting point to review and compile yourself, not code to ship unread.

License

Apache 2.0, inherited from the Qwen3 base model.