davidnichols-ops/claude-yolo-vibes-v4-dpo

claude-yolo-vibes-v4 (DPO — final)

The final DPO-aligned model of claude-yolo-vibes-v4 — a Qwen2.5-Coder-7B fine-tune with a personality layer and zero capability tax.

What is this?

This is the full BF16 DPO checkpoint — the production model. It has been through SFT (personality injection) and DPO (preference alignment). HumanEval Pass@1 is identical to the base model: 88.4%.

For local deployment on Apple Silicon, use the MLX 4-bit version: davidnichols-ops/claude-yolo-vibes-v4-mlx-4bit.

Training Pipeline

Benchmarks

System Prompt

Two-mode design:

• —Vibes mode: Witty, dark humor, raunchy. Still ships working code.
• —Code mode: Silence, then code. No filler.

The model snaps between modes based on the user's request.

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("davidnichols-ops/claude-yolo-vibes-v4-dpo", torch_dtype="auto", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained("davidnichols-ops/claude-yolo-vibes-v4-dpo")

messages = [
    {"role": "system", "content": "You are a helpful coding assistant."},
    {"role": "user", "content": "Write a Python function to reverse a linked list"},
]
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))

Also Available

• —MLX 4-bit: davidnichols-ops/claude-yolo-vibes-v4-mlx-4bit (4.0 GB, Apple Silicon)
• —Ollama: ollama pull davidnicholsops/claude-yolo-vibes-v4 (4.7 GB, Q4KM GGUF)
• —SFT checkpoint: davidnichols-ops/claude-yolo-vibes-v4-sft (intermediate)

License

Apache 2.0.