Hugging Face's logo

pahaadi
/
Qwen2.5-Coder-14B-houdini_vex_functions

PyTorch
GGUF
qwen2
conversational
Model card Files Community
1
Qwen2.5-Coder-14B-houdini_vex_functions
File size: 3,349 Bytes 
b119e23
 
 
8c929f3
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
 
---
license: apache-2.0
---
Below is a sample README file for the repository. You can adjust the sections as needed:

---

# Qwen2.5-Coder-14B Houdini Vex Functions

This repository hosts a fine-tuned version of the **Qwen2.5-Coder-14B** model, optimized specifically for generating Houdini VEX functions. The model has been fine-tuned using Houdini VEX Functions data and is designed to assist developers and technical artists working in Houdini.

## Model Details

- **Base Model:** Qwen2.5-Coder-14B
- **Fine-Tuning:** Finetuned using Houdini VEX Functions data
- **Architecture:** qwen2
- **Model Size:** 14.8B parameters
- **Quantization:** 8-bit (Q8_0)
- **License:** [Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0)

## Features

- **Houdini VEX Expertise:** Specially adapted to generate Houdini VEX code.
- **Procedural Workflow:** Ideal for creating procedural geometry, effects, and other Houdini-specific functions.
- **Efficient Performance:** Utilizes 8-bit quantization for faster inference while maintaining quality.

## Installation

To use this model, ensure you have the required dependencies installed. You can install the necessary Python packages using pip:

```bash
pip install transformers torch
```

Then, load the model in your Python script as follows:

```python
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "pahaadi/Qwen2.5-Coder-14B-houdini_vex_functions"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True)

# Example usage: Generate Houdini VEX code
prompt = "Write a Houdini VEX function that creates procedural geometry."
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=256)
generated_code = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(generated_code)
```

## Usage

This model is tailored for tasks such as:
- Generating Houdini VEX functions.
- Assisting with procedural generation tasks in Houdini.
- Accelerating coding workflows in Houdini-based projects.

Feel free to integrate the model into your Houdini pipeline to enhance your creative coding process.

## Fine-Tuning and Contributions

If you are interested in further fine-tuning this model or adapting it for other Houdini-related tasks, contributions and suggestions are welcome. Please follow the guidelines provided in the [Hugging Face documentation](https://huggingface.co/docs) for model fine-tuning and deployment.

## License

This model is released under the [Apache-2.0 License](https://www.apache.org/licenses/LICENSE-2.0).

## Citation

If you use this model in your research or projects, please consider citing it as follows:

```bibtex
@misc{pahaadi2025qwen2.5,
  author = {pahaadi},
  title = {Qwen2.5-Coder-14B Houdini Vex Functions},
  year = {2025},
  publisher = {Hugging Face},
  url = {https://huggingface.co/pahaadi/Qwen2.5-Coder-14B-houdini_vex_functions}
}
```

## Acknowledgments

Special thanks to the contributors and the Hugging Face community for their continuous support and for providing an open platform for sharing and developing innovative machine learning models.

---

This README provides an overview of the model, usage instructions, and additional details that help users understand and integrate the model into their projects.