Instructions to use prithivMLmods/Viper-OneCoder-UIGEN with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use prithivMLmods/Viper-OneCoder-UIGEN with Transformers:

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

pipe = pipeline("text-generation", model="prithivMLmods/Viper-OneCoder-UIGEN")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("prithivMLmods/Viper-OneCoder-UIGEN")
model = AutoModelForCausalLM.from_pretrained("prithivMLmods/Viper-OneCoder-UIGEN")
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

vLLM

How to use prithivMLmods/Viper-OneCoder-UIGEN with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/prithivMLmods/Viper-OneCoder-UIGEN

SGLang

How to use prithivMLmods/Viper-OneCoder-UIGEN 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 "prithivMLmods/Viper-OneCoder-UIGEN" \
    --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": "prithivMLmods/Viper-OneCoder-UIGEN",
		"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 "prithivMLmods/Viper-OneCoder-UIGEN" \
        --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": "prithivMLmods/Viper-OneCoder-UIGEN",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use prithivMLmods/Viper-OneCoder-UIGEN with Docker Model Runner:
```
docker model run hf.co/prithivMLmods/Viper-OneCoder-UIGEN
```

prithivMLmods commited on Feb 27, 2025

Commit

bb9bce0

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1 Parent(s): e638797

Update README.md

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@@ -17,4 +17,69 @@ datasets:
 - smirki/UI_REASONING_v1.01
 - smirki/Parkytest
 ---
-![zdbfcbdf.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/75Ic8lgquvpGjSLJnm8V7.png)

 - smirki/UI_REASONING_v1.01
 - smirki/Parkytest
 ---
+![zdbfcbdf.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/75Ic8lgquvpGjSLJnm8V7.png)
+# **Viper-OneCoder-UIGEN**
+Viper-OneCoder-UIGEN is based on the Qwen 2.5 14B modality architecture, designed to be the **best** for web development and structured coding logic. It has been fine-tuned on a synthetic dataset leveraging the latest coding logits and CoT datasets, further optimizing its **step-by-step logic breakdown** and **front-end problem-solving** abilities. The model demonstrates significant improvements in **context understanding, structured UI development, and long-context comprehension**, making it ideal for **web-based coding tasks, HTML/CSS/Tailwind development, and detailed instruction following**.
+### **Key Improvements**
+1. **Best-in-Class Web Development Proficiency**: Advanced understanding of **HTML, CSS, Tailwind, JavaScript**, and front-end frameworks.
+2. **Fine-Tuned Step-by-Step Logic Breakdown**: Optimized for structured explanations, component-based UI coding, and logic-driven development.
+3. **Advanced Instruction Following**: Delivers precise responses, structured outputs (e.g., JSON, YAML), and extended text generation (**8K+ tokens**).
+4. **Long-Context Mastery**: Handles up to **128K tokens** with an output capability of **8K tokens** per response.
+5. **Multilingual Code Support**: Excels in **HTML, CSS, JavaScript, React, Tailwind CSS, Python**, and other major web-related languages, with documentation in **29+ languages**.
+### **Quickstart with Transformers**
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+model_name = "prithivMLmods/Viper-OneCoder-UIGEN"
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto",
+    trust_remote_code=True
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+prompt = "Create a responsive navigation bar using Tailwind CSS."
+messages = [
+    {"role": "system", "content": "You are an advanced AI assistant with expert-level UI coding and reasoning abilities."},
+    {"role": "user", "content": prompt}
+]
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True
+)
+model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
+generated_ids = model.generate(
+    **model_inputs,
+    max_new_tokens=512
+)
+generated_ids = [
+    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
+]
+response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+print(response)
+```
+### **Intended Use**
+- **Elite Web Development & UI Design**: Best-in-class model for writing, analyzing, and optimizing front-end code.
+- **Step-by-Step Coding Logic Breakdown**: Guides developers through structured programming approaches and best practices.
+- **Component-Based UI Development**: Generates reusable Tailwind and React components with clear explanations.
+- **Structured Data Processing**: Handles JSON, XML, and structured UI component automation.
+- **Multilingual Programming Support**: Proficient in HTML, CSS, Tailwind, JavaScript, React, Python, and Go.
+- **Extended Technical Content Generation**: Ideal for writing documentation, blog posts, and front-end tutorials.
+### **Limitations**
+1. **High Computational Demand**: Requires powerful GPUs/TPUs for smooth inference due to **14B parameters**.
+2. **Framework-Specific Variability**: Performance may vary across different front-end frameworks.
+3. **Possible Error Propagation**: Extended text outputs might introduce logical inconsistencies.
+4. **Limited Real-World Awareness**: The model does not have access to real-time internet updates.
+5. **Prompt Sensitivity**: Performance depends on how well the prompt is structured.