124 lines
4.2 KiB
Markdown
124 lines
4.2 KiB
Markdown
---
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base_model: unsloth/qwen2.5-coder-1.5b-instruct-bnb-4bit
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language:
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- en
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license: apache-2.0
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tags:
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- text-generation-inference
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- transformers
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- unsloth
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- qwen2
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- trl
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- sft
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- fast-apply
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- instant-apply
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---
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# FastApply-1.5B-v1.0
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[Github: kortix-ai/fast-apply](https://github.com/kortix-ai/fast-apply)
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[Dataset: Kortix/FastApply-dataset-v1.0](https://huggingface.co/datasets/Kortix/FastApply-dataset-v1.0)
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[Try it now on 👉 Google Colab](https://colab.research.google.com/drive/1BNCab4oK-xBqwFQD4kCcjKc7BPKivkm1?usp=sharing)
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## Model Details
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### Basic Information
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- **Developed by:** Kortix
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- **License:** apache-2.0
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- **Finetuned from model:** [unsloth/Qwen2.5-Coder-1.5B-Instruct-bnb-4bit](https://huggingface.co/unsloth/Qwen2.5-Coder-1.5B-Instruct-bnb-4bit)
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### Model Description
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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/).
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It is part of the Fast Apply pipeline for data generation and fine-tuning Qwen2.5 Coder models.
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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.
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## Intended Use
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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:
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- Instant code application tasks
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- Full file edits
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- Integration with AI-powered code editors like Aider and PearAI
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- Local tools to reduce the cost of frontier model output
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## Inference template
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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:
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```
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<|im_start|>system
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You are a coding assistant that helps merge code updates, ensuring every modification is fully integrated.<|im_end|>
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<|im_start|>user
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Merge all changes from the <update> snippet into the <code> below.
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- Preserve the code's structure, order, comments, and indentation exactly.
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- Output only the updated code, enclosed within <updated-code> and </updated-code> tags.
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- Do not include any additional text, explanations, placeholders, ellipses, or code fences.
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<code>{original_code}</code>
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<update>{update_snippet}</update>
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Provide the complete updated code.<|im_end|>
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<|im_start|>assistant
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```
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The model's output is structured as:
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```
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<updated-code>[Full-complete updated file]</updated-code>
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```
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## Additional Information
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For more details on the Fast Apply pipeline, data generation process, and deployment instructions, please refer to the [GitHub repository](https://github.com/kortix-ai/fast-apply).
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## How to Use
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To use the model, you can load it using the Hugging Face Transformers library:
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model = AutoModelForCausalLM.from_pretrained("Kortix/FastApply-1.5B-v1.0", device_map="auto")
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tokenizer = AutoTokenizer.from_pretrained("Kortix/FastApply-1.5B-v1.0")
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# Prepare your input following the prompt structure mentioned above
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input_text = """<|im_start|>system
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You are a coding assistant that helps merge code updates, ensuring every modification is fully integrated.<|im_end|>
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<|im_start|>user
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Merge all changes from the <update> snippet into the <code> below.
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- Preserve the code's structure, order, comments, and indentation exactly.
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- Output only the updated code, enclosed within <updated-code> and </updated-code> tags.
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- Do not include any additional text, explanations, placeholders, ellipses, or code fences.
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<code>{original_code}</code>
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<update>{update_snippet}</update>
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Provide the complete updated code.<|im_end|>
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<|im_start|>assistant
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"""
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input_text = input_text.format(
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original_code=original_code,
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update_snippet=update_snippet,
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).strip()
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# Generate the response
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input_ids = tokenizer.encode(input_text, return_tensors="pt")
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output = model.generate(input_ids, max_length=8192,)
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response = tokenizer.decode(output[0][len(input_ids[0]):])
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print(response)
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# Extract the updated code from the response
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updated_code = response.split("<updated-code>")[1].split("</updated-code>")[0]
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```
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## Evaluation:
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