lexi-coder-v4.4
lexi-coder-v4.4 by Reallexi LLC AI Model Builder — llm.reallexi.io
Copyright (c) 2026 Reallexi LLC. All rights reserved.
A standalone model of 3.85B parameters, derived from reallexi/lexi-coder-v4.3.
The adapter has been merged into the base weights, so no PEFT adapter is needed at runtime.
Size and requirements
| Parameters | 3,847,556,096 (3.85B) |
| Weights on disk | 7.15 GB |
| Trained context length | 2,048 tokens |
| Base model | reallexi/lexi-coder-v4.3 |
Approximate memory to hold the weights. Add context and runtime overhead on top.
| Precision | Weights |
|---|---|
| FP16 / BF16 | 7.17 GB |
| 8-bit (Q8_0) | 3.58 GB |
| 4-bit (Q4_K_M) | 1.97 GB |
Training
| Strategy | lora |
| Adapter | Auto LoRA |
| LoRA rank / alpha | 8 / 16 |
| Dataset | reallexi/lexi-coder-v3-datasest |
| Samples learned | 395,000 (through phase 53 of 174) |
| Training steps | 15,000 |
| Epochs | 3 |
Before / after samples
The same prompts, drawn from the training data, run through the base model before training and the finished model after. This shows what the run changed on representative prompts -- it is not a benchmark. Full outputs are in SAMPLES.md and samples.json alongside this file.
Prompt: import numpy as np N, D = map(int, input().split()) cnt = 0 for i in range(N): x, y = map(int,
- Before: input().split()) if x2 + y2 <= D**2: cnt += 1 print(cnt) # print(N, D) # print(N, D, cnt) # print(N, D, cnt, cnt) # print(N, D, cnt, cnt, cnt) # print(N, D, cnt, cnt, cnt, cnt)
- After: input().split()) if x2 + y2 <= D2: cnt += 1 print(cnt) # print(x, y) # print(np.sqrt(x2 + y2)) # print(np.sqrt(D2)) # print(np.sqrt(D2 - x2)) # print(np.sqrt(D2 - y2))
Prompt: n, d = map(int, input().split()) ans = 0 for _ in range(n): x, y = map(float, input().split()) z = (x**2
- Before: + y2)0.5 if z <= d else -1 ans += 1 print(ans) # ans = 0 for _ in range(n): x, y = map(int, input().split()) z = (x2 + y2)**0.5 if z <= d else -1 ans += 1 print(ans) # ans = 0 for
- After: + y2)0.5 if z <= d else -1 ans += 1 print(ans) # ans = 0 for _ in range(n): x, y = map(int, input().split()) z = (x2 + y2)**0.5 if z <= d else -1 ans += 1 print(ans) # ans = 0 for
Prompt: #n, m = map(int,input().split()) k = int(input()) #hl = list(map(int,input().split())) s = len(str(k)) for i in range(1,k+1): if int('7'*i) %
- Before: k == 0: print(i) break k = int(input()) n = 7 for i in range(1,k+1): if n % k == 0: print(i) break n = n*10+7 else: print(-1) #k % 7 == 0の時は-1を返す
- After: k == 0: print(i) break k = int(input()) if k % 2 == 0 or k % 5 == 0: print(-1) else: i = 1 while True: if int('7'*i) % k == 0: print(i) break i += 1 if i >
Training curve
Usage
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("lexi-coder-v4.4")
tokenizer = AutoTokenizer.from_pretrained("lexi-coder-v4.4")
License and attribution
The effective terms are inherited from the base model and the training data, which are not necessarily the same as this project's own license. Review both before redistributing.
-
Base model:
reallexi/lexi-coder-v4.3 -
Training data:
reallexi/lexi-coder-v3-datasest
Copyright (c) 2026 Reallexi LLC. All rights reserved.
Produced by Reallexi LLC AI Model Builder from training job #1996. Core: https://llm.reallexi.io
Who, where, and what platform trained this?
Produced by Reallexi LLC on Reallexi AI Model Builder, a local-first training platform (https://llm.reallexi.io). Hugging Face repository: reallexi/lexi-coder-v4.4. Copyright (c) 2026 Reallexi LLC. All rights reserved.