claude-yolo-vibes-v4 (SFT checkpoint)
The SFT (Supervised Fine-Tuning) checkpoint of claude-yolo-vibes-v4 — a Qwen2.5-Coder-7B fine-tune with a personality layer.
What is this?
This is the intermediate SFT checkpoint, before DPO alignment. It has the personality but not the preference optimization. For the final production model, use davidnichols-ops/claude-yolo-vibes-v4-mlx-4bit (quantized) or davidnichols-ops/claude-yolo-vibes-v4-dpo (full BF16).
Training Details
| Parameter | Value |
|---|---|
| Base model | Qwen2.5-Coder-7B-Instruct |
| Training data | 1,031 verified agent sessions |
| Epochs | 3 |
| Learning rate | 2e-5 |
| Batch size | 4 (with gradient accumulation) |
| Hardware | AMD MI300X (ROCm) |
| Training time | ~45 min |
| Final loss | 0.56 |
| Token accuracy | 91.2% |
HumanEval
| Stage | Pass@1 |
|---|---|
| Base | 88.4% |
| SFT (this model) | 88.4% |
| DPO (final) | 88.4% |
Personality tax: 0.0%. SFT alone preserved full coding capability.
Usage
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("davidnichols-ops/claude-yolo-vibes-v4-sft", torch_dtype="auto", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained("davidnichols-ops/claude-yolo-vibes-v4-sft")
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)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
License
Apache 2.0.