Initial commit
Browse files- .gitattributes +2 -0
- README.md +131 -1
- adapter_config.json +23 -0
- adapter_model.bin +3 -0
- generation_config.json +13 -0
- special_tokens_map.json +7 -0
- tokenizer.model +3 -0
- tokenizer_config.json +37 -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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adapter_model.bin filter=lfs diff=lfs merge=lfs -text
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tokenizer.model filter=lfs diff=lfs merge=lfs -text
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README.md
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@@ -1,3 +1,133 @@
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---
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-
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---
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---
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datasets:
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- IlyaGusev/ru_turbo_alpaca
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- IlyaGusev/ru_sharegpt_cleaned
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- IlyaGusev/oasst1_ru_main_branch
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- lksy/ru_instruct_gpt4
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- IlyaGusev/gpt_roleplay_realm
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language:
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- ru
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pipeline_tag: conversational
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license: cc-by-4.0
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---
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# Saiga2 70B, Russian LLaMA2-based chatbot
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Based on [LLaMA-2 70B fp16](https://huggingface.co/TheBloke/Llama-2-70B-fp16)
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This is an adapter-only version.
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Training code: [link](https://github.com/IlyaGusev/rulm/tree/master/self_instruct)
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**WARNING**: Avoid using V100 (in Colab, for example). Outputs are much worse in this case.
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```python
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from peft import PeftModel, PeftConfig
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from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
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MODEL_NAME = "IlyaGusev/saiga2_70b_lora"
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DEFAULT_MESSAGE_TEMPLATE = "<s>{role}\n{content}</s>\n"
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DEFAULT_SYSTEM_PROMPT = "Ты — Сайга, русскоязычный автоматический ассистент. Ты разговариваешь с людьми и помогаешь им."
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class Conversation:
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def __init__(
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self,
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message_template=DEFAULT_MESSAGE_TEMPLATE,
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system_prompt=DEFAULT_SYSTEM_PROMPT,
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start_token_id=1,
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bot_token_id=9225
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):
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self.message_template = message_template
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self.start_token_id = start_token_id
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self.bot_token_id = bot_token_id
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self.messages = [{
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"role": "system",
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"content": system_prompt
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}]
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def get_start_token_id(self):
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return self.start_token_id
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def get_bot_token_id(self):
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return self.bot_token_id
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def add_user_message(self, message):
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self.messages.append({
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"role": "user",
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"content": message
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})
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def add_bot_message(self, message):
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self.messages.append({
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"role": "bot",
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"content": message
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})
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def get_prompt(self, tokenizer):
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final_text = ""
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for message in self.messages:
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message_text = self.message_template.format(**message)
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final_text += message_text
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final_text += tokenizer.decode([self.start_token_id, self.bot_token_id])
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return final_text.strip()
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def generate(model, tokenizer, prompt, generation_config):
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data = tokenizer(prompt, return_tensors="pt")
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data = {k: v.to(model.device) for k, v in data.items()}
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output_ids = model.generate(
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**data,
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generation_config=generation_config
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)[0]
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output_ids = output_ids[len(data["input_ids"][0]):]
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output = tokenizer.decode(output_ids, skip_special_tokens=True)
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return output.strip()
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config = PeftConfig.from_pretrained(MODEL_NAME)
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model = AutoModelForCausalLM.from_pretrained(
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config.base_model_name_or_path,
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load_in_8bit=True,
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torch_dtype=torch.float16,
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device_map="auto"
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)
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model = PeftModel.from_pretrained(
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model,
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MODEL_NAME,
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torch_dtype=torch.float16
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)
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model.eval()
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tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, use_fast=False)
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generation_config = GenerationConfig.from_pretrained(MODEL_NAME)
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print(generation_config)
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inputs = ["Почему трава зеленая?", "Сочини длинный рассказ, обязательно упоминая следующие объекты. Дано: Таня, мяч"]
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for inp in inputs:
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conversation = Conversation()
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conversation.add_user_message(inp)
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prompt = conversation.get_prompt(tokenizer)
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output = generate(model, tokenizer, prompt, generation_config)
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print(inp)
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print(output)
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print()
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print("==============================")
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print()
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```
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Examples:
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```
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User: Почему трава зеленая?
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Saiga:
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```
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```
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User: Сочини длинный рассказ, обязательно упоминая следующие объекты. Дано: Таня, мяч
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Saiga:
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```
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v1:
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- dataset code revision 0dbd022613874fcda915f588f4a3292e137017d2
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- wandb [link](https://wandb.ai/ilyagusev/rulm_self_instruct/runs/4wp1y5jx)
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- 5 datasets: ru_turbo_alpaca, ru_sharegpt_cleaned, oasst1_ru_main_branch, gpt_roleplay_realm, ru_instruct_gpt4
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- Datasets merging script: [create_chat_set.py](https://github.com/IlyaGusev/rulm/blob/e4238fd9a196405b566a2d5838ab44b7a0f4dc31/self_instruct/src/data_processing/create_short_chat_set.py)
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adapter_config.json
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{
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"auto_mapping": null,
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"base_model_name_or_path": "models/llama2-70b",
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"bias": "none",
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"fan_in_fan_out": false,
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"inference_mode": true,
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"init_lora_weights": true,
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"layers_pattern": null,
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"layers_to_transform": null,
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"lora_alpha": 16,
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"lora_dropout": 0.05,
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"modules_to_save": null,
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"peft_type": "LORA",
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"r": 16,
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"revision": null,
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"target_modules": [
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"q_proj",
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"v_proj",
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"k_proj",
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"o_proj"
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],
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"task_type": "CAUSAL_LM"
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}
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adapter_model.bin
ADDED
@@ -0,0 +1,3 @@
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version https://git-lfs.github.com/spec/v1
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oid sha256:4dead3782b2210b8e3166e3978a19523476302fa13f937abb4da186aaba018e4
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size 262375757
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generation_config.json
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{
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"pad_token_id": 0,
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"bos_token_id": 1,
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"eos_token_id": 2,
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"temperature": 0.2,
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"top_p": 0.9,
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"top_k": 30,
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"do_sample": true,
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"max_new_tokens": 1536,
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"num_beams": 1,
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"repetition_penalty": 1.15,
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"no_repeat_ngram_size": 15
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}
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special_tokens_map.json
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{
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"bos_token": "<s>",
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"eos_token": "</s>",
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"pad_token": "<unk>",
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"sep_token": "<s>",
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"unk_token": "<unk>"
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}
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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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"bos_token": {
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"__type": "AddedToken",
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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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"clean_up_tokenization_spaces": false,
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"eos_token": {
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"__type": "AddedToken",
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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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"legacy": null,
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"model_max_length": 4096,
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"pad_token": null,
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"padding_side": "left",
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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": {
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"__type": "AddedToken",
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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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"use_default_system_prompt": true
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}
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