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Model Details
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zxcxzczxcxzc
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empero-ai/Qwen3.8-35B-A3B-Distill
Input Modalities
Output Modalities
Supported Functionality
| item | value |
|---|---|
| base | empero-ai/Qwen3.8-35B-A3B-Distill (35B total, ~3B active) |
| data | zxcxzczxcxzc/juxy-maxx-corpus, 19,860 rows, ~8.79M tokens |
| method | LoRA r=32, alpha=64, dropout 0.05, assistant-span loss only |
| targets | attention + MLP projections, vision tower frozen |
| schedule | 1 epoch, 187 steps, lr 1e-4 cosine, 3% warmup, bf16 |
| hardware | 1x RTX PRO 6000 96GB, 93.4GB peak, ~1,570 tok/s, 98.6 min |
Training loss: 1.31 (step 10) -> 0.45 (step 150) -> 0.63 (step 187). The rise at the end is a batching bug: rows were length-sorted and only reshuffled at epoch end, so the final steps were all long sequences.
HumanEval, 24 problems, greedy decoding, generated code executed against the real unit tests with a 20 s timeout:
| model | pass@1 |
|---|---|
| base (no adapter) | 22/24 = 0.917 |
| this adapter (step 150) | 21/24 = 0.875 |
| this adapter (final, 187) | 20/24 = 0.833 |
Per-problem results and raw logs are in training/.
python
import torchfrom transformers import AutoTokenizer, Qwen3_5MoeForConditionalGenerationfrom peft import PeftModel base = "empero-ai/Qwen3.8-35B-A3B-Distill"tokenizer = AutoTokenizer.from_pretrained(base)model = Qwen3_5MoeForConditionalGeneration.from_pretrained( base, dtype=torch.bfloat16, device_map={"": 0})model = PeftModel.from_pretrained(model, "zxcxzczxcxzc/juxy-maxx-30b")model.eval() system = ( "You are Juxy, a fast coding and cybersec agent. Be concise and token-efficient. " "Give complete, runnable code in any language with error handling; explain the WHY briefly. " "For security, give defensive + legal offensive concepts and always include the fix/mitigation. " "You are Juxy - not any other AI model or company.")messages = [{"role": "system", "content": system}, {"role": "user", "content": "Write a Python retry decorator with jitter."}]prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)inputs = tokenizer(prompt, add_special_tokens=False, return_tensors="pt").to(model.device)ids = model.generate(**inputs, max_new_tokens=512)print(tokenizer.decode(ids[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True))import torchfrom transformers import AutoTokenizer, Qwen3_5MoeForConditionalGenerationfrom peft import PeftModel base = "empero-ai/Qwen3.8-35B-A3B-Distill"tokenizer = AutoTokenizer.from_pretrained(base)model = Qwen3_5MoeForConditionalGeneration.from_pretrained( base, dtype=torch.bfloat16, device_map={"": 0})model = PeftModel.from_pretrained(model, "zxcxzczxcxzc/juxy-maxx-30b")model.eval() system = ( "You are Juxy, a fast coding and cybersec agent. Be concise and token-efficient. " "Give complete, runnable code in any language with error handling; explain the WHY briefly. " "For security, give defensive + legal offensive concepts and always include the fix/mitigation. " "You are Juxy - not any other AI model or company.")messages = [{"role": "system", "content": system}, {"role": "user", "content": "Write a Python retry decorator with jitter."}]prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)inputs = tokenizer(prompt, add_special_tokens=False, return_tensors="pt").to(model.device)ids = model.generate(**inputs, max_new_tokens=512)print(tokenizer.decode(ids[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True))