Instructions to use shawaz03/vibe-coder-7b-max with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use shawaz03/vibe-coder-7b-max with Transformers:

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

pipe = pipeline("text-generation", model="shawaz03/vibe-coder-7b-max")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("shawaz03/vibe-coder-7b-max")
model = AutoModelForCausalLM.from_pretrained("shawaz03/vibe-coder-7b-max", device_map="auto")
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]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use shawaz03/vibe-coder-7b-max with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "shawaz03/vibe-coder-7b-max"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "shawaz03/vibe-coder-7b-max",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/shawaz03/vibe-coder-7b-max

SGLang

How to use shawaz03/vibe-coder-7b-max 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 "shawaz03/vibe-coder-7b-max" \
    --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": "shawaz03/vibe-coder-7b-max",
		"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 "shawaz03/vibe-coder-7b-max" \
        --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": "shawaz03/vibe-coder-7b-max",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use shawaz03/vibe-coder-7b-max with Docker Model Runner:
```
docker model run hf.co/shawaz03/vibe-coder-7b-max
```

vibe-coder-7b-max / README.md

shawaz03

Update Model Card with 1-click Colab badge and 4-bit snippets

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metadata

language:
  - en
license: apache-2.0
tags:
  - code
  - code-generation
  - full-stack
  - react
  - nextjs
  - typescript
  - tailwindcss
  - prisma
  - zustand
  - qwen2.5-coder
  - vibe-coding
base_model: Qwen/Qwen2.5-Coder-7B-Instruct
pipeline_tag: text-generation
library_name: transformers

🚀 VIBE CODER v2.0 MAX (7B)

The Autonomous Full-Stack AI Software Engineer & Modern UI/UX Designer.
Zero Placeholders. Modern Anti-AI Aesthetics. Production-Grade TypeScript & Next.js Architecture.

⚡ Quickstart on Google Colab (1-Click Run)

Run Vibe Coder on a Free Google Colab T4 GPU with zero memory warnings:

👉

📌 Overview

Vibe Coder v2.0 MAX is a specialized, fine-tuned code generation model based on Qwen2.5-Coder-7B-Instruct. It is engineered specifically to eliminate common LLM coding pitfalls—such as lazy placeholder comments (// TODO: implement logic), broken imports, and outdated visual tropes.

🌟 Core Capabilities:

🛡️ Zero Placeholders Guaranteed: Generates complete, functional components, state hooks, and API routes with zero missing logic.
🎨 Modern Anti-AI Aesthetic Directives: Built-in design system rules that enforce dark neutral palettes (bg-neutral-900, border-neutral-800), custom typography, responsive grid layouts, and Lucide React icons.
⚡ Full-Stack Ecosystem Mastery: Native expertise in Next.js 15 App Router, React 19, TypeScript, Tailwind CSS, Zustand, Prisma ORM, Zod validation, and WebSockets.
🛠️ Self-Healing & Debugging: Diagnoses runtime hydration errors and type mismatches with exact root-cause explanations and drop-in code patches.

📊 Dataset & Training Architecture

Vibe Coder was trained on a 64,000-record Master Dataset structured in strict ChatML format across 7 specialized pipelines:

Pipeline	Dataset Focus	Size
1. Open-Source Repositories	Production Next.js server actions, Prisma schemas, Zustand stores	25,000 records
2. Handcrafted Vibe Templates	Complete Bento showcases, pricing matrices, checkout wizards, audio players	12,000 records
3. Multi-Turn Refinement	Multi-turn developer dialogues simulating feature additions and refactoring	10,000 records
4. Self-Healing & Debugging	Runtime errors, TypeScript compilation bugs, hydration fixes	5,000 records
5. Full-Stack Architectures	WebSocket chat rooms, Stripe webhook signature verifiers, Redis caching	12,000 records

⚡ Hyperparameters:

Base Model: Qwen/Qwen2.5-Coder-7B-Instruct
Method: 4-bit NF4 QLoRA $\rightarrow$ Full 16-bit FP16 Safetensors Merger
LoRA Config: Rank $r = 64$, $\alpha = 128$, rsLoRA = True (161.4M trainable parameters)
Attention Kernel: PyTorch SDPA (Scaled Dot-Product Flash Attention)
Final Validation Loss: 0.035 – 0.045
Token Accuracy: 98.5%

💻 Quick Start & Usage

1. Using Transformers in 4-bit (Google Colab / Low-VRAM GPUs)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig

model_id = "shawaz03/vibe-coder-7b-max"

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_use_double_quant=True,
    bnb_4bit_compute_dtype=torch.float16,
)

tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    quantization_config=bnb_config,
    device_map="auto",
    trust_remote_code=True
)

system_prompt = """You are Vibe Coder, a world-class principal full-stack software engineer and UI/UX designer.
Write complete, modern, production-grade code in TypeScript, React, Next.js, and Node.js with ZERO placeholders."""

messages = [
    {"role": "system", "content": system_prompt},
    {"role": "user", "content": "Build an interactive pricing matrix in React with Tailwind CSS, supporting monthly/annual toggle and feature checkmarks."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    **inputs,
    max_new_tokens=2048,
    temperature=0.2,
    top_p=0.95,
    repetition_penalty=1.05,
    do_sample=True,
)

print(tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))

2. High-Speed Production Serving (vLLM)

vllm serve shawaz03/vibe-coder-7b-max --port 8000 --dtype float16

🛡️ License

This project is open-source and licensed under the Apache 2.0 License.