Text Generation
Transformers
Safetensors
English
qwen3
mergekit
Merge
text-generation-inference
code
coder
withinusai
Instructions to use WithinUsAI/Darwin-Astral-Coder-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use WithinUsAI/Darwin-Astral-Coder-4B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="WithinUsAI/Darwin-Astral-Coder-4B")# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("WithinUsAI/Darwin-Astral-Coder-4B") model = AutoModelForMultimodalLM.from_pretrained("WithinUsAI/Darwin-Astral-Coder-4B") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use WithinUsAI/Darwin-Astral-Coder-4B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "WithinUsAI/Darwin-Astral-Coder-4B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "WithinUsAI/Darwin-Astral-Coder-4B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/WithinUsAI/Darwin-Astral-Coder-4B
- SGLang
How to use WithinUsAI/Darwin-Astral-Coder-4B 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 "WithinUsAI/Darwin-Astral-Coder-4B" \ --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": "WithinUsAI/Darwin-Astral-Coder-4B", "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 "WithinUsAI/Darwin-Astral-Coder-4B" \ --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": "WithinUsAI/Darwin-Astral-Coder-4B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use WithinUsAI/Darwin-Astral-Coder-4B with Docker Model Runner:
docker model run hf.co/WithinUsAI/Darwin-Astral-Coder-4B
Guy DuGan II commited on
Upload 4 files
Browse files- .gitattributes +1 -0
- README.md +54 -127
- special_tokens_map.json +31 -0
- tokenizer.json +3 -0
- tokenizer_config.json +239 -0
.gitattributes
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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tokenizer.json filter=lfs diff=lfs merge=lfs -text
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README.md
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weight: 0.5
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merge_method: slerp
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base_model: openfree/Darwin-Qwen3-4B
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parameters:
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t:
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- filter: embed_tokens
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value: 0.0
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value: 0.5
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value: 0.5
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value: 1.0
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- value: 0.5 # Catch-all for norm layers
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dtype: float16
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```
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## 🚀 How to Use (Transformers)
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You can run this model directly using the Hugging Face `transformers` library.
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```python
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import torch
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from transformers import AutoTokenizer, AutoModelForCausalLM
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model_id = "WithinUsAI/Darwin-Astral-4B-Coder" # Replace with your actual username/repo
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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torch_dtype=torch.float16,
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device_map="auto"
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)
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prompt = "Write a Python script to merge two sorted lists."
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messages = [
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{"role": "system", "content": "You are an advanced coding assistant."},
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{"role": "user", "content": prompt}
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]
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text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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inputs = tokenizer(text, return_tensors="pt").to(model.device)
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outputs = model.generate(
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**inputs,
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max_new_tokens=512,
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do_sample=True,
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temperature=0.7
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)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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```
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## 📜 License
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This model is released under the **Apache 2.0** license, following the licensing of the base Qwen models. Please refer to the original model cards for specific restrictions.
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---
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*Created with Amalgamation AI by Within Us AI.*
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```
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---
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### **Next Step: GGUF Conversion**
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Once you have uploaded this to Hugging Face (or even if you keep it local), the next logical step is to make it runnable on your iPhone, older laptops, or via Ollama.
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**Ready for the GGUF script?** I can provide a `convert_to_gguf.py` script that handles the quantization (making it smaller/faster) automatically.
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```
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---
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base_model: []
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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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# WithinUs_CPU_Hybrid
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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 [SLERP](https://en.wikipedia.org/wiki/Slerp) merge method.
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### Models Merged
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The following models were included in the merge:
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* X:/Genesis_X/models/openfree-Darwin-Qwen3-4B
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* X:/Genesis_X/models/Lucidity-AI-Astral-4B-Coder
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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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base_model: X:/Genesis_X/models/Lucidity-AI-Astral-4B-Coder
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dtype: float16
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merge_method: slerp
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parameters:
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t:
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- filter: embed_tokens
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value: 0.0
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- filter: self_attn
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value: 0.5
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- filter: mlp
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value: 0.5
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- filter: lm_head
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value: 1.0
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- value: 0.5
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slices:
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- sources:
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- layer_range:
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- 0
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- 36
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model: X:/Genesis_X/models/Lucidity-AI-Astral-4B-Coder
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- layer_range:
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- 0
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- 36
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model: X:/Genesis_X/models/openfree-Darwin-Qwen3-4B
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```
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special_tokens_map.json
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{
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"additional_special_tokens": [
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"<|im_start|>",
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"<|im_end|>",
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"<|object_ref_start|>",
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"<|object_ref_end|>",
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"<|box_start|>",
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"<|box_end|>",
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"<|quad_start|>",
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"<|quad_end|>",
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"<|vision_start|>",
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"<|vision_end|>",
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"<|vision_pad|>",
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"<|image_pad|>",
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"<|video_pad|>"
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],
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"eos_token": {
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"content": "<|im_end|>",
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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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},
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"pad_token": {
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"content": "<|endoftext|>",
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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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}
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}
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tokenizer.json
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version https://git-lfs.github.com/spec/v1
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oid sha256:aeb13307a71acd8fe81861d94ad54ab689df773318809eed3cbe794b4492dae4
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size 11422654
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tokenizer_config.json
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| 1 |
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{
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| 2 |
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| 3 |
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| 4 |
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| 5 |
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| 6 |
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| 7 |
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| 8 |
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| 9 |
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| 10 |
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| 11 |
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| 12 |
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| 13 |
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| 14 |
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| 15 |
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| 16 |
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| 17 |
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| 18 |
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| 19 |
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| 20 |
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| 21 |
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| 22 |
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| 23 |
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| 24 |
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| 25 |
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| 26 |
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| 27 |
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| 28 |
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| 29 |
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| 30 |
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| 31 |
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| 32 |
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| 33 |
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| 34 |
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| 35 |
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| 36 |
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| 37 |
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| 38 |
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| 39 |
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| 40 |
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| 41 |
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| 42 |
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| 43 |
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| 44 |
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| 45 |
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| 46 |
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| 47 |
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| 48 |
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| 49 |
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| 50 |
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| 51 |
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| 52 |
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| 53 |
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| 54 |
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| 55 |
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| 58 |
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| 59 |
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| 60 |
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| 62 |
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| 63 |
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| 64 |
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| 65 |
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| 66 |
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| 67 |
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| 68 |
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| 69 |
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| 70 |
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| 71 |
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| 72 |
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| 73 |
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| 74 |
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| 75 |
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| 76 |
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| 77 |
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| 78 |
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| 82 |
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| 83 |
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| 84 |
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| 86 |
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| 88 |
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| 90 |
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| 91 |
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| 94 |
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| 95 |
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| 99 |
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| 100 |
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| 101 |
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| 102 |
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| 103 |
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| 104 |
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| 105 |
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| 106 |
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| 107 |
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| 108 |
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| 109 |
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| 110 |
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| 111 |
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| 112 |
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| 113 |
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| 114 |
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| 115 |
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| 116 |
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| 118 |
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| 119 |
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| 120 |
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| 121 |
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| 122 |
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| 123 |
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| 124 |
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| 125 |
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| 126 |
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| 127 |
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| 128 |
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| 129 |
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| 130 |
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| 131 |
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| 132 |
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| 133 |
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| 134 |
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| 135 |
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| 136 |
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| 137 |
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| 138 |
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| 139 |
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| 140 |
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| 141 |
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| 142 |
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| 143 |
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| 144 |
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| 145 |
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| 146 |
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| 147 |
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| 148 |
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| 149 |
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| 150 |
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| 151 |
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| 152 |
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| 153 |
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| 154 |
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| 155 |
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| 156 |
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| 157 |
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| 158 |
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| 159 |
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| 160 |
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| 161 |
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| 162 |
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| 163 |
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| 164 |
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| 165 |
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| 166 |
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| 167 |
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| 168 |
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| 169 |
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| 170 |
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| 171 |
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| 172 |
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| 173 |
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| 174 |
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| 175 |
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| 176 |
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| 177 |
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| 178 |
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| 179 |
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| 180 |
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| 181 |
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| 182 |
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| 183 |
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| 184 |
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| 185 |
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| 186 |
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| 187 |
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| 188 |
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| 189 |
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| 190 |
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| 191 |
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| 192 |
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| 193 |
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| 194 |
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| 195 |
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| 196 |
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| 197 |
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| 198 |
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| 199 |
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| 200 |
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| 201 |
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| 202 |
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| 203 |
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| 204 |
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| 205 |
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| 206 |
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| 207 |
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| 208 |
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| 209 |
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| 210 |
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| 211 |
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| 212 |
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| 213 |
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| 214 |
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| 215 |
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| 216 |
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| 217 |
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| 218 |
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| 219 |
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| 220 |
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| 221 |
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| 222 |
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| 223 |
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| 224 |
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| 225 |
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| 226 |
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| 227 |
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| 228 |
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],
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| 229 |
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| 230 |
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| 231 |
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| 232 |
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| 233 |
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| 234 |
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| 235 |
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| 236 |
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| 237 |
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| 238 |
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| 239 |
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