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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	---
	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)

	<div align="center">

	[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/shawaz03/LLM/blob/main/vibe_coder_quickstart.ipynb)
	[![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
	[![Base Model](https://img.shields.io/badge/Base%20Model-Qwen2.5--Coder--7B--Instruct-purple.svg)](https://huggingface.co/Qwen/Qwen2.5-Coder-7B-Instruct)
	[![Model Size](https://img.shields.io/badge/Parameters-7.61B-green.svg)]()
	[![Precision](https://img.shields.io/badge/Weights-FP16%20Safetensors-orange.svg)]()

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

	</div>

	---

	## ⚡ Quickstart on Google Colab (1-Click Run)

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

	👉 [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/shawaz03/LLM/blob/main/vibe_coder_quickstart.ipynb)

	---

	## 📌 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)

	```python
	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)

	```bash
	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.