Instructions to use EK-01/SyntheticLanguageAssociationArea_SLAA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use EK-01/SyntheticLanguageAssociationArea_SLAA with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="EK-01/SyntheticLanguageAssociationArea_SLAA")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("EK-01/SyntheticLanguageAssociationArea_SLAA") model = AutoModelForCausalLM.from_pretrained("EK-01/SyntheticLanguageAssociationArea_SLAA") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use EK-01/SyntheticLanguageAssociationArea_SLAA with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "EK-01/SyntheticLanguageAssociationArea_SLAA" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "EK-01/SyntheticLanguageAssociationArea_SLAA", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/EK-01/SyntheticLanguageAssociationArea_SLAA
- SGLang
How to use EK-01/SyntheticLanguageAssociationArea_SLAA with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "EK-01/SyntheticLanguageAssociationArea_SLAA" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "EK-01/SyntheticLanguageAssociationArea_SLAA", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "EK-01/SyntheticLanguageAssociationArea_SLAA" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "EK-01/SyntheticLanguageAssociationArea_SLAA", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use EK-01/SyntheticLanguageAssociationArea_SLAA with Docker Model Runner:
docker model run hf.co/EK-01/SyntheticLanguageAssociationArea_SLAA
Upload 7 files
Browse filesA period of 30 years of checkups will be required to ensure his reliability..

I have sealed him in this capsule, which will test his internal systems until his reliability has been confirmed. Please do not disturb the capsule until that time.
- LICENSE +11 -0
- README.md +41 -3
- config.json +34 -0
- model.safetensors +3 -0
- special_tokens_map.json +42 -0
- tokenizer.json +0 -0
- tokenizer_config.json +167 -0
LICENSE
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CREATIVE COMMONS CORPORATION: CC0 1.0 UNIVERSAL (PUBLIC DOMAIN DEDICATION)
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The person who associated a work with this deed has dedicated the work to the
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public domain by waiving all of his or her rights to the work worldwide under
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copyright law, including all related and neighboring rights, to the extent
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allowed by law.
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You can copy, modify, distribute and perform the work, even for commercial
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purposes, all without asking permission.
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THE WORK IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED.
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README.md
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---
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---
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---
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base_model:
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- HuggingFaceTB/SmolLM2-360M-Instruct
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library_name: transformers
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tags:
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- mergekit
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- merge
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---
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# specialized_robot_brain
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This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
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## Merge Details
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### Merge Method
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This model was merged using the [DARE TIES](https://arxiv.org/abs/2311.03099) merge method using . as a base.
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### Models Merged
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The following models were included in the merge:
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* [HuggingFaceTB/SmolLM2-360M-Instruct](https://huggingface.co/HuggingFaceTB/SmolLM2-360M-Instruct)
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### Configuration
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The following YAML configuration was used to produce this model:
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```yaml
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merge_method: dare_ties
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base_model: .
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models:
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- model: .
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parameters:
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weight: 0.65
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density: 1.0 # Keep 100% of your current elite grammar paths
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- model: HuggingFaceTB/SmolLM2-360M-Instruct
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parameters:
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weight: 0.35
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density: 0.15 # DROPS 85% of Instruct's facts, code, and safety bloat
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```
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config.json
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{
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"architectures": [
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"LlamaForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"bos_token_id": 0,
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"dtype": "bfloat16",
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"eos_token_id": 0,
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"head_dim": 64,
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"hidden_act": "silu",
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"hidden_size": 960,
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"initializer_range": 0.02,
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"intermediate_size": 2560,
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"is_llama_config": true,
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"max_position_embeddings": 8192,
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"mlp_bias": false,
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"model_type": "llama",
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"num_attention_heads": 15,
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"num_hidden_layers": 32,
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"num_key_value_heads": 5,
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"pad_token_id": null,
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"pretraining_tp": 1,
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"rms_norm_eps": 1e-05,
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"rope_interleaved": false,
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"rope_parameters": {
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"rope_theta": 100000,
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"rope_type": "default"
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},
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"tie_word_embeddings": true,
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"transformers_version": "5.9.0",
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"use_cache": true,
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"vocab_size": 49152
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}
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version https://git-lfs.github.com/spec/v1
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oid sha256:6e6e506f80184edae096570a4dfca08244de96f2bd5fcc7daa8f99790556ef39
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size 723674912
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special_tokens_map.json
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{
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"additional_special_tokens": [
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"<|endoftext|>",
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"<|im_start|>",
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"<filename>",
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"<gh_stars>",
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"<issue_start>",
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"<issue_closed>",
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"<jupyter_start>",
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"<jupyter_text>",
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"<jupyter_code>",
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"<jupyter_output>",
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"<jupyter_script>",
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"bos_token": {
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"eos_token": {
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"content": "<|endoftext|>",
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"lstrip": false,
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"normalized": false,
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"content": "<|endoftext|>",
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"lstrip": false,
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"single_word": false
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}
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}
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tokenizer.json
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The diff for this file is too large to render.
See raw diff
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tokenizer_config.json
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{
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"add_prefix_space": false,
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"added_tokens_decoder": {
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"0": {
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"lstrip": false,
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},
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},
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"special": true
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},
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"3": {
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"content": "<repo_name>",
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"lstrip": false,
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| 31 |
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"normalized": false,
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| 32 |
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"rstrip": false,
|
| 33 |
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"single_word": false,
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| 34 |
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"special": true
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},
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"4": {
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"content": "<reponame>",
|
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"lstrip": false,
|
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"normalized": false,
|
| 40 |
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"rstrip": false,
|
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"single_word": false,
|
| 42 |
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"special": true
|
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},
|
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"5": {
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"content": "<file_sep>",
|
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"lstrip": false,
|
| 47 |
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"normalized": false,
|
| 48 |
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"rstrip": false,
|
| 49 |
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"single_word": false,
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"special": true
|
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},
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"6": {
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"content": "<filename>",
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"lstrip": false,
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"normalized": false,
|
| 56 |
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"rstrip": false,
|
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"single_word": false,
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"special": true
|
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},
|
| 60 |
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"7": {
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| 61 |
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"content": "<gh_stars>",
|
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"lstrip": false,
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"normalized": false,
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| 64 |
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"rstrip": false,
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| 65 |
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"single_word": false,
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"special": true
|
| 67 |
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},
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| 68 |
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"8": {
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"content": "<issue_start>",
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"lstrip": false,
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"normalized": false,
|
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"rstrip": false,
|
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"single_word": false,
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| 74 |
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"special": true
|
| 75 |
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},
|
| 76 |
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"9": {
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| 77 |
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"content": "<issue_comment>",
|
| 78 |
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"10": {
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"content": "<issue_closed>",
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"lstrip": false,
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