Aigis
commited on
Commit
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Parent(s):
a4a159a
Added model files
Browse files- Inferencecode.py +35 -0
- README.md +85 -0
- UUFO.png +0 -0
- config.json +24 -0
- model.safetensors +3 -0
- special_tokens_map.json +23 -0
- tokenizer.json +0 -0
- tokenizer.model +3 -0
- tokenizer_config.json +42 -0
Inferencecode.py
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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MODEL_NAME = "UUFO-Aigis/Pico-OpenLAiNN-10M" #Replace 10M with 25M, 50M or 75M if you prefer those models.
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tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
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model = AutoModelForCausalLM.from_pretrained(MODEL_NAME)
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def generate_text(prompt, model, tokenizer, max_length=512, temperature=1, top_k=50, top_p=0.95):
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inputs = tokenizer.encode(prompt, return_tensors="pt")
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outputs = model.generate(
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inputs,
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max_length=max_length,
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temperature=temperature,
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top_k=top_k,
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top_p=top_p,
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do_sample=True
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)
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generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
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return generated_text
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def main():
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# Define your prompt
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prompt = "According to all known laws of aviation, there is no way a bee should be able to fly."
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generated_text = generate_text(prompt, model, tokenizer)
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print(generated_text)
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if __name__ == "__main__":
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main()
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README.md
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# Planck-OpenLAiNN 🤗
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Hey there fellow researchers, developers, and AI enthusiasts! Today I'm releasing a new family of Models, Planck LAiNN, These are probably some of the smallest LLMs that are on HF. They aren't super useful but it was a fun expierment!~
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## Models Overview
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- **Panck-OpenLAiNN-10M**: A Truely Tiny model with just 10 Million parameters, this is probably boarderline useless, but it *IS* functional.
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- **Panck-OpenLAiNN-25M**: The second smallest model, 25 million parameters, it's not that much better.
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- **Panck-OpenLAiNN-50M**: Surprisingly smart, it's 50 Million parameters and could potentially maybe, Possibly even be useful ;)
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- **Panck-OpenLAiNN-75M**: The current *""heavy""* weight of the Plank-OpenLAiNN Models.
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## Pretraining Details
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Plank-OpenLAiNN was trained on 32B tokens of the Fineweb dataset, it's the same one that was used for the Pico-LAiNN family of models. The model was pretrained with a context length of 1024 tokens.
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## Other information:
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- **Compatibility**: Built to be compatible with existing projects that use LLAMA 2's tokenizer and architecture.
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- **Ease of Use**: No need to reinvent the wheel. These models are ready to be plugged into your applications.
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- **Open Source**: Fully open source, so you can tweak, tune, and twist them to your heart's content.
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## Getting Started
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To start using these models, you can simply load them via the Hugging Face `transformers` library:
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```python
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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MODEL_NAME = "UUFO-Aigis/Panck-OpenLAiNN-10M"
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tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
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model = AutoModelForCausalLM.from_pretrained(MODEL_NAME)
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def generate_text(prompt, model, tokenizer, max_length=512, temperature=1, top_k=50, top_p=0.95):
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inputs = tokenizer.encode(prompt, return_tensors="pt")
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outputs = model.generate(
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inputs,
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max_length=max_length,
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temperature=temperature,
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top_k=top_k,
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top_p=top_p,
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do_sample=True
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)
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generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
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return generated_text
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def main():
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# Define your prompt
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prompt = "According to all known laws of aviation, there is no way a bee should be able to fly."
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generated_text = generate_text(prompt, model, tokenizer)
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print(generated_text)
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if __name__ == "__main__":
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main()
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```
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# Benchy
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| Tasks | Value | |Stderr|
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|--------------|------:|---|-----:|
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|arc_challenge | 0.1766|± |0.0111|
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|arc_easy | 0.3144|± |0.0095|
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|boolq | 0.5847|± |0.0086|
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|hellaswag | 0.2622|± |0.0044|
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|lambada_openai| 0.0047|± |0.0009| # Yes, really
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|piqa | 0.5718|± |0.0115|
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|winogrande | 0.4957|± |0.0141|
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## Future Plans
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- **More Models**: I'm currenetly training the bigger siblings of Pico-OpenLAiNN, including a 1B parameter version and beyond. 2-4 Billion parameter versions are planned. These will be Released as OpenLAiNN.
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- **New architecture**: This is still up in the air and I'm still developing it, things are going well and I'll post updates.
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- **Paper**: A detailed paper or training data will be posted at some point.
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## Credit Where Credit's Due
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If you find these models useful and decide to use these models, a link to this repository would be highly appreciated. I am a one man show running this and I'm doing this for free, Thanks 🤗
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## Contact
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If you have questions, Please reach out to me at [email protected]
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<p align="center">
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<img src="UUFO.png" alt="U.U.F.O Research Logo" width="250"/>
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</p>
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UUFO.png
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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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"bos_token_id": 1,
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"eos_token_id": 2,
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"hidden_act": "silu",
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"hidden_size": 192,
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"initializer_range": 0.02,
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"intermediate_size": 1024,
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"max_position_embeddings": 1024,
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"model_type": "llama",
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"num_attention_heads": 2,
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"num_hidden_layers": 1,
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"num_key_value_heads": 1,
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"pretraining_tp": 1,
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"rms_norm_eps": 1e-05,
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"rope_scaling": null,
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"tie_word_embeddings": false,
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"torch_dtype": "float32",
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"transformers_version": "4.31.0.dev0",
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"use_cache": true,
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"vocab_size": 32000
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}
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:7198d242c2903a94acc187bbaaf864637f4af8890b13b220fdf4e4af99590628
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size 25979272
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special_tokens_map.json
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{
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"bos_token": {
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"content": "<s>",
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"lstrip": false,
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"normalized": true,
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"rstrip": false,
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"single_word": false
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},
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"eos_token": {
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"content": "</s>",
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"lstrip": false,
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"normalized": true,
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"rstrip": false,
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"single_word": false
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},
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"unk_token": {
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"content": "<unk>",
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"lstrip": false,
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"normalized": true,
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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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See raw diff
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tokenizer.model
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version https://git-lfs.github.com/spec/v1
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oid sha256:9e556afd44213b6bd1be2b850ebbbd98f5481437a8021afaf58ee7fb1818d347
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size 499723
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tokenizer_config.json
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{
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"add_bos_token": true,
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"add_eos_token": false,
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"add_prefix_space": true,
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"added_tokens_decoder": {
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"0": {
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"content": "<unk>",
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"lstrip": false,
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"normalized": true,
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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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"1": {
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"content": "<s>",
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"lstrip": false,
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"normalized": true,
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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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"2": {
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"content": "</s>",
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"lstrip": false,
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"normalized": true,
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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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},
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"bos_token": "<s>",
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"clean_up_tokenization_spaces": false,
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"eos_token": "</s>",
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"legacy": true,
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"model_max_length": 2048,
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"pad_token": null,
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"sp_model_kwargs": {},
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"spaces_between_special_tokens": false,
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"tokenizer_class": "LlamaTokenizer",
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"unk_token": "<unk>",
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"use_default_system_prompt": false
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}
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