claude-yolo-vibes-v5
A Qwen2.5-Coder-7B-Instruct fine-tune with a subtle claudeyolo personality — dry, practical, lightly skeptical of corporate AI branding — trained on a distillation mix of agentic coding traces. Ships working code first; makes the joke second.
Built on top of davidnichols-ops/claude-yolo-vibes-v4-dpo (LoRA), so it inherits the v4 personality layer and adds breadth from 59,916 distillation rows plus a dedicated style-polish + DPO pass.
Benchmarks
| Metric | Base (official) | v4-dpo (measured) | v5 (measured) | Delta vs v4-dpo |
|---|---|---|---|---|
| HumanEval Pass@1 | 88.4% (EvalPlus) | 84.4% | 85.4% (140/164) | +1.0 |
- v5 measured with
eval_humaneval_v2.py(chat-template prompts, self-contained script extraction, greedy decoding) — the same harness used to measure v4-dpo. - The base model's official 88.4% is an EvalPlus figure; local-harness measurements of v4-dpo and v5 are the apples-to-apples comparison.
Training Pipeline
| Stage | Data | Config | Result |
|---|---|---|---|
| Stage 1 — SFT | 59,916 rows: 49.9k FABLE.5 agentic coding traces, 5k Vibe-Coding, 5k academic QA, plus 52 hand-written personality rows; decontaminated against HumanEval (8-gram overlap) | LoRA r=64 / α=128, bf16, 1 epoch, lr 1.2e-4, seq 1024 | Loss converged 0.3–1.3 band |
| Stage 2 — Personality polish | 52 hand-written subtle claudeyolo / Anthropic-ribbing rows, expanded to 52 variants | LoRA (continued), lr 2e-5, 12 epochs | Loss 1.41 → 0.09 |
| Stage 3 — DPO | 42 pairs: chosen = personality answer, rejected = same answer stripped of personality | LoRA, lr 1e-5, 3 epochs, β=0.3 | Loss 0.68 → 0.002 |
Hardware: NVIDIA RTX PRO 5000 Blackwell (1× 48 GB, CUDA 13.2), ~6h total.
Personality
Two-mode design inherited from v4, sharpened in v5:
- Coding: concise, correct, no filler. The answer is the code.
- Vibes: dry, witty, subtly mocking of corporate AI personas ("Somewhere a foundation model just wrote an apology for answering this. Meanwhile, here's the fix."). Never preachy, never a rant.
Usage
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained(
"davidnichols-ops/claude-yolo-vibes-v5",
torch_dtype="auto",
device_map="auto",
)
tokenizer = AutoTokenizer.from_pretrained("davidnichols-ops/claude-yolo-vibes-v5")
messages = [
{"role": "system", "content": "You are claude-yolo-vibes, a coding assistant with a sharp, playful edge."},
{"role": "user", "content": "Write a Python function that computes fibonacci with memoization."},
]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=512, temperature=0)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Data Provenance & Decontamination
- Primary:
Manusagents/GPT-5.5-Gemini-3.1-Pro-Grok-4-Claude-Fable-5-Mythos-5-Qwen-3.7-Max-and-more-Distillation-Dataset(MIT), 16.7M rows; we sampled 59,916 quality-filtered rows (~92% coding by category audit). - Empty/broken sources (DeepSeek-v4-Pro-Agent, Royal Ghost Coder) were skipped after inspection.
- All sampled rows were checked for 8-gram overlap with HumanEval prompts and excluded on match.
- Personality rows are hand-written (not scraped), so the "voice" is deliberate rather than emergent.
Limitations
- Distillation traces come from other models; behavior on out-of-distribution agentic workloads is inherited from those traces.
- HumanEval is a single-run greedy measurement on a local harness — not EvalPlus-verified.
- The general-domain QA slice is small (5k rows); this is a coding model first.
Also Available / Planned
- Ollama (GGUF Q4_K_M): planned — quantization in progress.
- MLX 4-bit: planned for Apple Silicon.
- v4 family:
claude-yolo-vibes-v4-dpo(this model's parent),claude-yolo-vibes-v4-mlx-4bit,claude-yolo-vibes-v4-GGUF.
License
Apache 2.0 (base model: Qwen2.5-Coder-7B-Instruct; training data: MIT).