Instructions to use brady777/surfdoc-8b-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use brady777/surfdoc-8b-v1 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="brady777/surfdoc-8b-v1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("brady777/surfdoc-8b-v1")
model = AutoModelForCausalLM.from_pretrained("brady777/surfdoc-8b-v1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use brady777/surfdoc-8b-v1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "brady777/surfdoc-8b-v1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "brady777/surfdoc-8b-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/brady777/surfdoc-8b-v1

SGLang

How to use brady777/surfdoc-8b-v1 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "brady777/surfdoc-8b-v1" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "brady777/surfdoc-8b-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "brady777/surfdoc-8b-v1" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "brady777/surfdoc-8b-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use brady777/surfdoc-8b-v1 with Docker Model Runner:
```
docker model run hf.co/brady777/surfdoc-8b-v1
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

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()

Built by

CloudSurf Software LLC

Downloads last month: 7

Safetensors

Model size

8B params

Tensor type

BF16

Model tree for brady777/surfdoc-8b-v1

Base model

Qwen/Qwen3-8B-Base

Finetuned

Qwen/Qwen3-8B

Finetuned

(1630)

this model

Quantizations

1 model