metadata
language:
- en
license: apache-2.0
library_name: transformers
tags:
- surfdoc
- qwen3
- text-generation
- fine-tuned
pipeline_tag: text-generation
base_model: Qwen/Qwen3-8B
model-index:
- name: surfdoc-8b-v1
results: []
SurfDoc AI (surfdoc:8b v1)
Fine-tuned Qwen3-8B for generating structurally valid SurfDoc (.surf) documents.
Model Details
- Base model: Qwen/Qwen3-8B
- Method: QLoRA (rank 32, 87M trainable params, 1.05% of model)
- Training data: 9,241 instruction-output pairs from 26 CloudSurf repositories
- Training hardware: NVIDIA GB10 (Blackwell), 128GB unified memory
- Training time: 5.7 hours (235 steps, Flash Attention 2 enabled)
- Framework: Unsloth 2026.3.4 + PyTorch 2.10 + CUDA 12.8
What it generates
Valid SurfDoc documents with:
- YAML frontmatter (title, type, version, status, tags)
- Typed block directives (::summary, ::callout, ::data, ::action-items)
- Markdown headings and structured content
- Correct formatting and syntax
Usage
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("brady777/surfdoc-8b-v1")
tokenizer = AutoTokenizer.from_pretrained("brady777/surfdoc-8b-v1")
messages = [
{"role": "system", "content": "You are SurfDoc AI. Generate valid SurfDoc documents."},
{"role": "user", "content": "Create a SurfDoc plan about improving website performance"},
]
inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True)
outputs = model.generate(inputs.input_ids, max_new_tokens=512, temperature=0.7)
print(tokenizer.decode(outputs[0][inputs.input_ids.shape[-1]:], skip_special_tokens=True))
Evaluation
Evaluated on 20 diverse SurfDoc generation prompts:
- Average score: 79%
- Has title: 70% | Has type: 60% | Has headings: 75%
- Uses blocks: 55% | Blocks closed: 100%
- No repetition: 95% | Good length: 100%
Note
Qwen3 is a reasoning model — outputs include <think> tags before the actual response. Strip these before display:
import re
response = re.sub(r'<think>.*?</think>', '', response, flags=re.DOTALL).strip()