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
```

shawaz03 commited on 5 days ago

Commit

b7070bf

verified ·

1 Parent(s): e4117ea

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

Browse files

Files changed (1) hide show

README.md +20 -6

README.md CHANGED Viewed

@@ -23,6 +23,7 @@ library_name: transformers
 <div align="center">
 [![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)]()
@@ -35,6 +36,14 @@ library_name: transformers
 ---
 ## 📌 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.
@@ -71,24 +80,29 @@ Vibe Coder was trained on a **64,000-record Master Dataset** structured in stric
 ## 💻 Quick Start & Usage
-### 1. Using Transformers (Python)
 ```python
 import torch
-from transformers import AutoModelForCausalLM, AutoTokenizer
 model_id = "shawaz03/vibe-coder-7b-max"
-# Load Model & Tokenizer
 tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
 model = AutoModelForCausalLM.from_pretrained(
     model_id,
-    torch_dtype=torch.float16,
     device_map="auto",
     trust_remote_code=True
 )
-# System Prompt
 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."""
@@ -103,7 +117,7 @@ inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
 outputs = model.generate(
     **inputs,
     max_new_tokens=2048,
-    temperature=0.2,   # 0.2 is recommended for precise code logic
     top_p=0.95,
     repetition_penalty=1.05,
     do_sample=True,

 <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)]()
 ---
+## ⚡ 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.
 ## 💻 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."""
 outputs = model.generate(
     **inputs,
     max_new_tokens=2048,
+    temperature=0.2,
     top_p=0.95,
     repetition_penalty=1.05,
     do_sample=True,