Lin-1 Epsilon Preview

Lin-1 Epsilon Preview is a 0.5B-parameter Rust code model from LindotAI, fine-tuned from Qwen2.5-Coder-0.5B-Instruct. It is small enough to run on a laptop CPU and is meant for quick completions, simple functions and generate-and-test loops. Its bigger sibling is Lin-1 Locus (4B).

Benchmarks

All numbers measured by us with the same harness for every model (Rust ports of the benchmarks; generated code is compiled and run against the tests).

Holdout is our private set of Rust tasks with tests that was never used for training.

Quick start

from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "LindotAI/Lin-1-Epsilon"
tok = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype="auto", device_map="auto")

messages = [{"role": "user", "content": "Write a Rust function that checks whether a string is a palindrome."}]
inputs = tok.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
out = model.generate(inputs, max_new_tokens=512, temperature=0.2, do_sample=True)
print(tok.decode(out[0][inputs.shape[1]:], skip_special_tokens=True))

GGUF (llama.cpp / Ollama): Lin-1-Epsilon-Q8_0.gguf (~0.5 GB) is included.

llama-cli -m Lin-1-Epsilon-Q8_0.gguf -p "Write a Rust function that reverses a Vec<i32> in place."

Training

Supervised fine-tuning on curated, compile-checked Rust, followed by reinforcement learning (GRPO) with a test-pass reward on Rust tasks, and a weight average of the SFT and RL checkpoints. All training data was decontaminated against the evaluation sets (12-gram overlap filter).

Limitations

• Preview release: a 0.5B model makes more mistakes than larger ones on multi-step logic.
• Focused on Rust. Always compile and test generated code.

License

Apache 2.0, same as the base model.