Lewi Surge Preview 3B (Chat, Early)
An early, partially fine-tuned chat checkpoint of Lewi Surge, an open mixture-of-experts language model for programming trained from scratch by one person in Ukraine.
⚠️ Read this first. This checkpoint went through less than a quarter of the planned chat fine-tuning (SFT). The run had to stop when the compute budget ran out. It already answers in the chat format and stops on its own, but it is not a finished assistant. We publish it as it is, so you can see the real state of the project.
💛 Why it's unfinished, and how you can help. Lewi is built without funding, on free and donated compute. The full fine-tuning (identity, instruction following, code and multilingual conversations) is prepared and ready to run; what's missing is GPU time. About $3 buys one hour on an NVIDIA GH200, and just $50 would unlock ~120 hours on a GH200 (the provider gives us access after a $50 deposit, and our free credits cover the rest), enough to finish SFT and fix the problems below. If you'd like to see Lewi finished, you can support it on Ko-fi.
Known problems
All of these come from the fine-tuning being cut short, not from the architecture. The data to fix them is already prepared (382k conversations, including identity and instruction-following sets).
| Problem | What you'll see | Fix (needs compute) |
|---|---|---|
| Weak identity | May claim to be "a software engineer" or another persona instead of Lewi | Identity conversations in the full SFT |
| Ignores format and language instructions | "Only code" or "answer in Ukrainian" is often not followed | Instruction-following data in the full SFT |
| Loops in long lists | Long enumerations can repeat items | More SFT steps + long-context stage |
| Short context | Trained on up to 16k-token conversations; 128k stage is in progress | Long-context stage on TPU (queued) |
| Factual errors | States wrong facts confidently, especially outside code | Larger model and more data (Lewi Surge 24B2A) |
Model
| Base | Lewi-Surge-Preview-3B-Base (HumanEval 25.0%, MBPP 29.6% as a base model) |
| Parameters | ~3.45B total, ~0.5B active per token |
| Layers | 16 = 4 × (3 Gated DeltaNet + 1 full attention) |
| Experts | 128 per layer, top-6 routed + 1 shared |
| Chat format | ChatML (<|im_start|> / <|im_end|>), no system prompt required, no thinking mode |
Usage
Requires transformers >= 5.0.
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
repo = "PoliteChainsaw/Lewi-Surge-Preview-3B-Chat-Early"
tok = AutoTokenizer.from_pretrained(repo)
model = AutoModelForCausalLM.from_pretrained(repo, dtype=torch.bfloat16, device_map="auto")
messages = [{"role": "user", "content": "Write a Python function that checks if a string is a palindrome."}]
ids = tok.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
out = model.generate(ids, max_new_tokens=256, do_sample=False, repetition_penalty=1.1)
print(tok.decode(out[0][ids.shape[1]:], skip_special_tokens=True))
A light repetition_penalty (1.05–1.1) helps with the looping issue. Runs on a single consumer GPU (~7 GB in bf16).
Limitations
Early checkpoint of a small model: expect wrong facts, uneven instruction following and weak non-Python code. Not safety-tuned. Do not use it for anything where mistakes matter.