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@@ -33,3 +33,7 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+assets/figures/aiXapply-dataset_pipeline.jpg filter=lfs diff=lfs merge=lfs -text
+assets/figures/aiXapply-latency-accuracy-frontier.png filter=lfs diff=lfs merge=lfs -text
+assets/figures/aiXapply-vscode-workflow.png filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,3 +1,426 @@
----
-license: apache-2.0
----
+# aiXapply
+
+<p align="center">
+  <a href="#overview">Overview</a> |
+  <a href="#resources">Resources</a> |
+  <a href="#quick-start">Quick Start</a> |
+  <a href="#continue-integration">Continue Integration</a> |
+  <a href="#dataset">Dataset</a> |
+  <a href="#training">Training</a> |
+  <a href="#evaluation">Evaluation</a> |
+  <a href="#results">Results</a> |
+  <a href="#citation">Citation</a>
+</p>
+
+<p align="center">
+  <a href="LICENSE"><img src="https://img.shields.io/badge/License-Apache--2.0-blue.svg" alt="Apache-2.0 license"></a>
+  <img src="https://img.shields.io/badge/GitHub-aiXcoder--Apply-black.svg" alt="GitHub repository">
+  <img src="https://img.shields.io/badge/HuggingFace-Test%20Data-yellow.svg" alt="Hugging Face test dataset">
+  <img src="https://img.shields.io/badge/Task-Full--File%20Apply-green.svg" alt="Full-file Apply task">
+  <img src="https://img.shields.io/badge/Model-4B-orange.svg" alt="4B model">
+  <img src="https://img.shields.io/badge/Endpoint-OpenAI--Compatible-lightgrey.svg" alt="OpenAI-compatible endpoint">
+</p>
+
+**aiXapply** is a specialized 4B model and open-source toolkit for **Full-File Apply**: given an original file and a localized update snippet, it generates the complete updated file while preserving everything outside the requested edit.
+
+This repository is the official artifact repository for:
+
+> **AiXapply: Fast and Reliable Full-File Code Integration with Specialized Small Models for IDE Workflows**
+
+## Overview
+
+Modern coding assistants often produce a local edit snippet first. The hard downstream step is applying that snippet to the original file without changing unrelated code. Unified diffs are compact but brittle, and search-and-replace is easy to generate but depends on exact string matching. aiXapply treats this downstream step as a standalone code-integration task.
+
+![aiXapply workflow in VS Code](assets/figures/aiXapply-vscode-workflow.png)
+
+*Figure 1: aiXapply in an IDE workflow. An upstream coding assistant proposes an update snippet, aiXapply expands it into a complete updated file, and the IDE presents the resulting diff for review.*
+
+The repository includes:
+
+| Component | Path |
+| --- | --- |
+| OpenAI-compatible inference scripts | `experiments/aiXapply/` |
+| Experiment entrypoints for full-file Apply, unified diff, and search-and-replace | `experiments/` |
+| Shared evaluation and six-class error taxonomy | `experiments/evaluation/` |
+| Multi-language data construction pipeline | `data_generation/` |
+| SFT and RL training scripts | `training/sft/`, `training/rl/` |
+| Continue IDE integration adapter | `continue_config/` |
+
+### Highlights
+
+- **High accuracy**: aiXapply-SFT reaches **94.4%** average equivalence accuracy on the 1,637-sample main benchmark, close to Qwen3.5-397B-A17B (94.8%) and above DeepSeek-V3.2 (91.6%).
+- **Fast full-file generation**: with n-gram speculative decoding, aiXapply reaches **1.06s** average latency and **2692 tokens/s** on a single A100 40GB GPU.
+- **Deployment-ready apply backend**: the model can be served behind an OpenAI-compatible endpoint and used as a dedicated `apply` model in Continue.
+- **Reproducible pipeline**: data generation, training, inference, scoring, and error classification scripts are included.
+
+## Resources
+
+This release is split into one GitHub repository and three Hugging Face artifacts:
+
+| Artifact        | Release target                                                                      | Description                                                                                                                                                                                   |
+| --------------- | ----------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| Code repository | [GitHub](https://github.com/aixcoder-plugin/aiXapply-4B)                            | Open-source project repository containing inference scripts, data construction code, training recipes, evaluation tools, Continue integration, and documentation.                             |
+| Test dataset    | [Hugging Face Dataset](https://huggingface.co/datasets/aiXcoder/aiXapply_test_data) | Public evaluation set for Full-File Apply, covering 20 programming languages and file formats. Use this artifact to reproduce benchmark scores without rebuilding the training data pipeline. |
+| RL model        | [Hugging Face Model](https://huggingface.co/aiXcoder/aiXapply-4B-RL)                | 4B Apply model post-trained with reinforcement learning / GRPO. It is optimized for task-level correctness, locality, and robustness under alternative edit representations.                  |
+| SFT model       | [Hugging Face Model](https://huggingface.co/aiXcoder/aiXapply-4B-SFT)              | 4B Apply model trained with supervised fine-tuning. It provides strong in-distribution accuracy and better long-context structural preservation in our experiments.                           |
+
+
+## Task Definition
+
+Full-File Apply takes:
+
+```text
+<language>{language}</language>
+<source_file>{original full file}</source_file>
+<update_snippet>{localized update snippet}</update_snippet>
+```
+
+and returns:
+
+```text
+<update_file>{complete updated file}</update_file>
+```
+
+The task has three core requirements:
+
+- **Complete output**: the model must return the full updated file, not a patch or partial fragment.
+- **No side effects**: content outside the requested edit region should remain identical to the source file.
+- **Placeholder expansion**: markers such as `// ... existing code ...` mean "copy the corresponding original content exactly"; placeholders must not appear in the final output.
+
+If anchors in the update snippet are ambiguous or cannot be located safely, the model should fail conservatively rather than hallucinate an unrelated edit.
+
+## Quick Start
+
+### Install
+
+```bash
+git clone --depth 1 --recurse-submodules https://github.com/aixcoder-plugin/aiXapply-4B.git
+cd aiXapply-4B
+
+python -m venv .venv
+source .venv/bin/activate
+python -m pip install -r requirements.txt
+```
+
+For model serving, install a `vllm` build compatible with your CUDA and PyTorch environment.
+
+### Serve a Model with vLLM
+
+```bash
+export WEIGHT_DIR=/path/to/aiXapply-4B-RL  # or /path/to/aiXapply-4B-SFT
+export SERVE_MODEL_NAME=aiXapply-4B-RL
+
+CUDA_VISIBLE_DEVICES=0 vllm serve "$WEIGHT_DIR" \
+  --host 0.0.0.0 \
+  --port 12003 \
+  --served-model-name "$SERVE_MODEL_NAME" \
+  --tensor-parallel-size 1 \
+  --enable-chunked-prefill \
+  --kv-cache-dtype auto \
+  --max-num-batched-tokens 4096 \
+  --max-model-len 32768 \
+  --gpu-memory-utilization 0.95 \
+  --speculative-config '{"method":"ngram","num_speculative_tokens":128,"prompt_lookup_max":7}'
+```
+
+Use `--max-model-len 262144` only if your serving setup has enough memory for the full long-context configuration.
+
+### Call the Endpoint
+
+```python
+from openai import OpenAI
+
+client = OpenAI(base_url="http://127.0.0.1:12003/v1", api_key="local")
+
+system_prompt = """You are a deterministic Code Patching Engine. Your task is to synthesize a "Updated File" by applying a partial "Update Snippet" to the provided "Source File".
+
+### Algorithm
+1. **Context Matching**: Analyze the `Update Snippet` to identify the context anchors (the lines of code surrounding the changes). Locate the exact corresponding block in the `Source File`. The match must be unique.
+2. **Code Merging**: Replace the matched block in the `Source File` with the logic from the `Update Snippet`.
+3. **Expansion**: The `Update Snippet` contains omission markers (e.g., `// ... existing code ...`). You MUST replace these markers with the original, unchanged lines from the `Source File`.
+4. **Output Generation**: Output the FULL content of the resulting file.
+
+### Constraints
+- **NO Laziness**: Never output comments like `// ... rest of code ...` in the final output. You must write out every single line of the final code.
+- **Strict Fidelity**: Preserve the original indentation style (spaces/tabs) and comments of the Source File for all unchanged parts.
+- **Safety**: If the context in the snippet is ambiguous or cannot be found, output nothing inside the tags.
+
+### Output Format
+<update_file>[Your final code here]</update_file>"""
+
+user_prompt = """<language>{language}</language>
+
+<source_file>{source_file}</source_file>
+
+<update_snippet>{update_snippet}</update_snippet>
+
+Please generate the full updated code strictly following the instructions."""
+
+
+LANGUAGE = "python"
+SOURCE_FILE = """def add(a, b):
+    return a + b
+
+def main():
+    print(add(1, 2))
+"""
+UPDATE_SNIPPET = """#  ... existing code ...
+def main():
+    print(add(7, 8))
+"""
+
+
+response = client.chat.completions.create(
+    model="aiXapply-4B-RL",
+    messages=[
+        {"role": "system", "content": system_prompt},
+        {"role": "user", "content": user_prompt.format(language=LANGUAGE, source_file=SOURCE_FILE, update_snippet=UPDATE_SNIPPET)},
+    ],
+    temperature=0,
+)
+
+print(response.choices[0].message.content)
+```
+
+## Continue Integration
+
+`continue_config/` contains an adapter for using aiXapply as Continue's dedicated Apply backend.
+
+The recommended local workflow is:
+
+```text
+Continue -> continue_apply_proxy.py -> OpenAI-compatible aiXapply endpoint
+```
+
+Start the proxy:
+
+```bash
+cd continue_config
+export APPLY_PROXY_UPSTREAM_CHAT_URL="http://127.0.0.1:12003/v1/chat/completions"
+export APPLY_PROXY_HOST="127.0.0.1"
+export APPLY_PROXY_PORT="14124"
+python3 continue_apply_proxy.py
+```
+
+Then merge the `apply` model block from `continue_config/continue.config.yaml.example` into your Continue config. The proxy strips `<update_file>...</update_file>` tags before returning the result to Continue and supports streaming responses.
+
+See [continue_config/README.md](continue_config/README.md) for configuration details and troubleshooting.
+
+## Dataset
+
+The public test dataset is released separately on Hugging Face. It contains the benchmark examples used to evaluate aiXapply and comparable models. Each example follows the Apply format:
+
+```text
+<source_file, update_snippet, update_file>
+```
+
+The broader training-data construction pipeline is included in this repository. It synthesizes Apply examples from real-world commits, including CommitPack-style records with `(old_file, new_file, commit_message)`.
+
+![aiXapply dataset construction pipeline](assets/figures/aiXapply-dataset_pipeline.jpg)
+
+*Figure 2: Dataset construction pipeline. Raw CommitPack records are sampled, consistency-verified, solvability-filtered, and split into train/test sets.*
+
+High-level pipeline:
+
+1. **Sampling and filtering**: keep localized same-file edits and balance languages/formats.
+2. **Change description generation**: make the intent of each commit explicit.
+3. **Snippet synthesis**: produce a localized `update_snippet` and full-file ground truth.
+4. **Consistency verification**: ensure every diff is explained by the snippet and no extra change is introduced.
+5. **Solvability filtering**: remove ambiguous or non-reproducible samples, then convert to training format.
+
+Dataset scale:
+
+| Split | Samples | Notes |
+| --- | ---: | --- |
+| Train | 19,347 | Multi-language Apply training examples |
+| Test | 1,637 | Public Hugging Face test dataset |
+
+The test set covers C, C++, Dockerfile, Go, HTML, INI, Java, JavaScript, JSON, Makefile, Markdown, Python, reStructuredText, Rust, Shell, SQL, Text, TypeScript, XML, and YAML.
+
+See [data_generation/README.md](data_generation/README.md) for scripts, configs, and reconstruction steps.
+
+## Training
+
+aiXapply is trained from a Qwen3-4B backbone with two complementary strategies:
+
+- **SFT**: direct supervised learning from `(source_file, update_snippet)` to `update_file`.
+- **RL / GRPO**: task-level optimization with rewards based on equivalence, patch correctness, and side-effect penalties.
+
+The released model artifacts are `aiXapply-4B-SFT` and `aiXapply-4B-RL`. Use the SFT model as the default choice for high full-file Apply accuracy and long-context fidelity; use the RL model when you want the RL-aligned variant used in the latency/accuracy frontier and cross-format experiments.
+
+### SFT
+
+```bash
+python -m pip install --extra-index-url https://download.pytorch.org/whl/cu128 -r training/sft/requirements.txt
+
+cd training/sft
+WANDB_PROJECT=aiXapply_sft \
+WANDB_RUN_NAME=qwen3-4b-sft \
+accelerate launch --config_file fsdp_config.yaml run_sft.py \
+  --train_dataset_path /path/to/train.parquet \
+  --test_dataset_path /path/to/test.parquet \
+  --model_name /path/to/Qwen3-4B \
+  --output_dir checkpoints/full_finetune
+```
+
+Update `training/sft/fsdp_config.yaml` for your machine, especially `num_processes` and context-parallel settings.
+
+### RL / GRPO
+
+The RL setup uses veRL. A typical training environment can be started with:
+
+```bash
+docker pull verlai/verl:vllm011.latest
+
+export WORKSPACE=/path/to/workspace
+docker create -it --runtime=nvidia --gpus all --net=host --ipc=host \
+  --cap-add=SYS_ADMIN \
+  -v "$WORKSPACE:$WORKSPACE" \
+  --entrypoint /bin/bash \
+  --name aixapply_verl \
+  verlai/verl:vllm011.latest \
+  -c "sleep infinity"
+
+docker start aixapply_verl
+docker exec -it aixapply_verl bash
+```
+
+Inside the container:
+
+```bash
+git submodule update --init --recursive
+cd training/rl/verl
+pip install -e .
+pip install -e .[sglang]
+cd ../../..
+
+cd training/rl
+MODEL_PATH=/path/to/Qwen3-4B \
+TRAIN_FILES=/path/to/train.parquet \
+TEST_FILES=/path/to/test.parquet \
+bash run_qwen3-4b_sgl_megatron_multi_grpo.sh
+```
+
+Training is resource-intensive; the paper experiments use multi-GPU A100-class hardware.
+
+## Evaluation
+
+Run inference:
+
+```bash
+python experiments/aiXapply/infer_openai.py \
+  --provider local \
+  --data-path /path/to/test.parquet
+```
+
+The `local` provider in `experiments/aiXapply/infer_openai.py` expects an OpenAI-compatible endpoint at `http://127.0.0.1:12003/v1`. If you serve the model on a different port or with a different served model name, update the local provider config in that script before running evaluation.
+
+Score predictions:
+
+```bash
+python experiments/evaluation/run_evaluation.py \
+  -i predictions/xxx.jsonl \
+  --classify_errors
+```
+
+Optional LLM-assisted error classification:
+
+```bash
+export OPENAI_BASE_URL="http://your_endpoint/v1"
+export OPENAI_MODEL="your_judge_model"
+
+python experiments/evaluation/run_evaluation.py \
+  -i predictions/xxx.jsonl \
+  --classify_errors \
+  --llm
+```
+
+The primary metric is **equivalence accuracy**:
+
+- Code files are compared with Pygments token equivalence.
+- Structured formats such as JSON, YAML, XML, and INI are parsed or classified as invalid when parsing fails.
+- Errors can be grouped into `OUTPUT_INVALID`, `PATCH_NOT_APPLIED`, `PATCH_INCOMPLETE`, `PATCH_INCORRECT`, `WRONG_POSITION`, and `OUT_OF_PATCH_SIDE_EFFECT`.
+
+See [experiments/README.md](experiments/README.md) and [experiments/evaluation/README.md](experiments/evaluation/README.md) for the full experiment layout.
+
+## Results
+
+![Accuracy-latency frontier for code apply](assets/figures/aiXapply-latency-accuracy-frontier.png)
+
+*Figure 3: Accuracy-latency comparison across unified diff, search-and-replace, and full-file Apply. aiXapply-RL keeps full-file Apply accuracy while reducing latency to an interactive range.*
+
+### Main Benchmark
+
+Average equivalence accuracy on the 1,637-example aiXapply test set:
+
+| Model | Avg Accuracy |
+| --- | ---: |
+| Qwen3-4B baseline | 0.626 |
+| Fast-Apply-7B | 0.620 |
+| DeepSeek-V3.2 | 0.916 |
+| GLM-5 | 0.921 |
+| aiXapply-RL | 0.938 |
+| aiXapply-SFT | 0.944 |
+| Qwen3.5-397B-A17B | 0.948 |
+
+### Editing Paradigms
+
+Under the same DeepSeek-V3.2 model, full-file Apply improves one-shot accuracy over common edit representations:
+
+| Representation | Accuracy | Avg Latency |
+| --- | ---: | ---: |
+| Unified diff | 0.560 | 14.22s |
+| Search-and-replace | 0.749 | 28.48s |
+| Full-file Apply | 0.916 | 108.96s |
+| aiXapply-RL full-file Apply | 0.938 | 1.44s |
+
+### Speculative Decoding
+
+| Method | Avg Latency | P95 Latency | Throughput |
+| --- | ---: | ---: | ---: |
+| No speculation | 28.83s | 90.23s | 102.04 tokens/s |
+| Suffix default | 5.75s | 20.74s | 509.54 tokens/s |
+| N-gram default | 2.17s | 6.94s | 1343.99 tokens/s |
+| N-gram best (`n=7`, `k=128`) | 1.06s | 3.38s | 2692.01 tokens/s |
+
+### Generalization
+
+| Setting | DeepSeek-V3.2 | aiXapply-RL | aiXapply-SFT |
+| --- | ---: | ---: | ---: |
+| Long context | 0.588 | 0.647 | 0.843 |
+| Untrained languages avg. | 0.932 | 0.938 | 0.941 |
+| Random placeholders avg. | 0.932 | 0.948 | 0.951 |
+| Chunk file avg. | 0.850 | 0.881 | 0.900 |
+
+### Industrial Deployment
+
+In the aiXcoder IDE plugin, aiXapply is deployed as a dedicated Apply service after the upstream model generates an update snippet. In production traces, the Apply stage drops from **50s** average latency to **1.89s**, with P95 latency reduced from **89s** to **3.78s**. The setup also offloads full-file generation from the upstream large model, improving serving capacity and reducing cost.
+
+## Repository Notes
+
+- The current release focuses on single-file Apply. Multi-file edits and interactive multi-step editing are future work.
+- aiXapply optimizes deterministic integration, not semantic validation. You should still run tests and review generated diffs before accepting edits.
+- Do not commit secrets, checkpoints, datasets, or generated prediction artifacts unless they are intentionally part of a release.
+
+## Contributing
+
+Contributions are welcome. Please read [CONTRIBUTING.md](CONTRIBUTING.md) before opening issues or pull requests.
+
+For useful bug reports, include the script or endpoint you ran, the command/configuration, the observed output or traceback, and enough model/provider context to reproduce the problem.
+
+## License
+
+This repository is licensed under the Apache License 2.0. See [LICENSE](LICENSE) for details.
+
+## Citation
+
+If you find aiXapply useful, please cite:
+
+```bibtex
+@misc{jiang2026aixapply,
+  title = {AiXapply: Fast and Reliable Full-File Code Integration with Specialized Small Models for IDE Workflows},
+  author = {Jiang, Siyuan and Cai, Xiang and Wang, Peixu and Han, Yu and Dong, Yihong and Ning, Wei and Guo, Xuyuan and Wen, Jincheng and Zhao, Wei and Li, Ge},
+  year = {2026},
+  url = {https://github.com/aixcoder-plugin/aiXapply-4B}
+}
+```

added_tokens.json

@@ -0,0 +1,28 @@
+{
+  "</think>": 151668,
+  "</tool_call>": 151658,
+  "</tool_response>": 151666,
+  "<think>": 151667,
+  "<tool_call>": 151657,
+  "<tool_response>": 151665,
+  "<|box_end|>": 151649,
+  "<|box_start|>": 151648,
+  "<|endoftext|>": 151643,
+  "<|file_sep|>": 151664,
+  "<|fim_middle|>": 151660,
+  "<|fim_pad|>": 151662,
+  "<|fim_prefix|>": 151659,
+  "<|fim_suffix|>": 151661,
+  "<|im_end|>": 151645,
+  "<|im_start|>": 151644,
+  "<|image_pad|>": 151655,
+  "<|object_ref_end|>": 151647,
+  "<|object_ref_start|>": 151646,
+  "<|quad_end|>": 151651,
+  "<|quad_start|>": 151650,
+  "<|repo_name|>": 151663,
+  "<|video_pad|>": 151656,
+  "<|vision_end|>": 151653,
+  "<|vision_pad|>": 151654,
+  "<|vision_start|>": 151652
+}

chat_template.jinja

@@ -0,0 +1,61 @@
+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0].role == 'system' %}
+        {{- messages[0].content + '\n\n' }}
+    {%- endif %}
+    {{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
+{%- else %}
+    {%- if messages[0].role == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- for message in messages %}
+    {%- if message.content is string %}
+        {%- set content = message.content %}
+    {%- else %}
+        {%- set content = '' %}
+    {%- endif %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
+        {{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {{- '<|im_start|>' + message.role + '\n' + content }}
+        {%- if message.tool_calls %}
+            {%- for tool_call in message.tool_calls %}
+                {%- if (loop.first and content) or (not loop.first) %}
+                    {{- '\n' }}
+                {%- endif %}
+                {%- if tool_call.function %}
+                    {%- set tool_call = tool_call.function %}
+                {%- endif %}
+                {{- '<tool_call>\n{"name": "' }}
+                {{- tool_call.name }}
+                {{- '", "arguments": ' }}
+                {%- if tool_call.arguments is string %}
+                    {{- tool_call.arguments }}
+                {%- else %}
+                    {{- tool_call.arguments | tojson }}
+                {%- endif %}
+                {{- '}\n</tool_call>' }}
+            {%- endfor %}
+        {%- endif %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- content }}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+{%- endif %}

config.json

@@ -0,0 +1,68 @@
+{
+  "architectures": [
+    "Qwen3ForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "dtype": "bfloat16",
+  "eos_token_id": 151645,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 2560,
+  "initializer_range": 0.02,
+  "intermediate_size": 9728,
+  "layer_types": [
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
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+}