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README.md

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+---
+base_model: unsloth/qwen2.5-coder-1.5b-instruct-bnb-4bit
+language:
+- en
+license: apache-2.0
+tags:
+- text-generation-inference
+- transformers
+- unsloth
+- qwen2
+- trl
+- sft
+- fast-apply
+- instant-apply
+---
+
+# FastApply-1.5B-v1.0
+
+[Github: kortix-ai/fast-apply](https://github.com/kortix-ai/fast-apply)   
+[Dataset: Kortix/FastApply-dataset-v1.0](https://huggingface.co/datasets/Kortix/FastApply-dataset-v1.0)    
+[Try it now on 👉 Google Colab](https://colab.research.google.com/drive/1BNCab4oK-xBqwFQD4kCcjKc7BPKivkm1?usp=sharing)
+
+## Model Details
+
+### Basic Information
+
+- **Developed by:** Kortix
+- **License:** apache-2.0
+- **Finetuned from model:** [unsloth/Qwen2.5-Coder-1.5B-Instruct-bnb-4bit](https://huggingface.co/unsloth/Qwen2.5-Coder-1.5B-Instruct-bnb-4bit)
+
+### 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](https://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](https://github.com/Kortex/FastApply).
+
+## How to Use
+
+To use the model, you can load it using the Hugging Face Transformers library:
+
+```python
+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]
+```