Fastapply 1.5B V1.0

🚀 FastApply-1.5B-v1.0

FastApply-1.5B-v1.0 is a 1.5B model designed for instant code application, capable of full - file edits to power SoftGen AI.

Github: kortix-ai/fast-apply
Dataset: Kortix/FastApply-dataset-v1.0
Try it now on 👉 Google Colab

🚀 Quick Start

To use the model, you can load it using the Hugging Face Transformers library:

Basic Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("Kortix/FastApply-1.5B-v1.0", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained("Kortix/FastApply-1.5B-v1.0")

# Prepare your input following the prompt structure mentioned above
input_text = """<|im_start|>system
You are a coding assistant that helps merge code updates, ensuring every modification is fully integrated.<|im_end|>
<|im_start|>user
Merge all changes from the <update> snippet into the <code> below.
- Preserve the code's structure, order, comments, and indentation exactly.
- Output only the updated code, enclosed within <updated-code> and </updated-code> tags.
- Do not include any additional text, explanations, placeholders, ellipses, or code fences.

<code>{original_code}</code>

<update>{update_snippet}</update>

Provide the complete updated code.<|im_end|>
<|im_start|>assistant
"""

input_text = input_text.format(
    original_code=original_code,
    update_snippet=update_snippet,
).strip() 

# Generate the response
input_ids = tokenizer.encode(input_text, return_tensors="pt")
output = model.generate(input_ids, max_length=8192,)

response = tokenizer.decode(output[0][len(input_ids[0]):])
print(response)

# Extract the updated code from the response
updated_code = response.split("<updated-code>")[1].split("</updated-code>")[0]

✨ Features

Model Details

Basic Information

Model Description

FastApply-1.5B-v1.0 is a 1.5B model designed for instant code application, producing full file edits to power SoftGen AI. It is part of the Fast Apply pipeline for data generation and fine - tuning Qwen2.5 Coder models.

The model achieves high throughput when deployed on fast providers like Fireworks while maintaining high edit accuracy, with a speed of approximately 340 tokens/second.

Intended Use

FastApply-1.5B-v1.0 is intended for use in AI-powered code editors and tools that require fast, accurate code modifications. It is particularly well - suited for:

• Instant code application tasks
• Full file edits
• Integration with AI-powered code editors like Aider and PearAI
• Local tools to reduce the cost of frontier model output

Inference template

FastApply-1.5B-v1.0 is based on the Qwen2.5 Coder architecture and is fine - tuned for code editing tasks. It uses a specific prompt structure for inference:

<|im_start|>system
You are a coding assistant that helps merge code updates, ensuring every modification is fully integrated.<|im_end|>
<|im_start|>user
Merge all changes from the <update> snippet into the <code> below.
- Preserve the code's structure, order, comments, and indentation exactly.
- Output only the updated code, enclosed within <updated-code> and </updated-code> tags.
- Do not include any additional text, explanations, placeholders, ellipses, or code fences.

<code>{original_code}</code>

<update>{update_snippet}</update>

Provide the complete updated code.<|im_end|>
<|im_start|>assistant

The model's output is structured as:

<updated-code>[Full-complete updated file]</updated-code>

Additional Information

For more details on the Fast Apply pipeline, data generation process, and deployment instructions, please refer to the GitHub repository.

📚 Documentation

Evaluation

📄 License

This model is released under the apache - 2.0 license.