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README.md ADDED
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+ ---
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+ license: apache-2.0
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+ datasets:
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+ - Open-Orca/OpenOrca
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+ - OpenAssistant/oasst_top1_2023-08-25
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+ language:
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+ - bg
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+ - ca
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+ - cs
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+ - da
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+ - de
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+ - en
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+ - es
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+ - fr
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+ - hr
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+ - hu
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+ - it
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+ - nl
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+ - pl
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+ - pt
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+ - ro
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+ - ru
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+ - sl
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+ - sr
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+ - sv
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+ - uk
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+
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+ library_name: transformers
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+ ---
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+
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+
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+ ![image/png](https://cdn-uploads.huggingface.co/production/uploads/641b435ba5f876fe30c5ae0a/rJ1RxzuE-3gzgCppx-T8f.png)
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+
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+ ```
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+ reference-data-model:
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+
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+ datasets:
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+ - OpenAssistant/oasst_top1_2023-08-25:
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+ lang: "bg,ca,cs,da,de,en,es,fr,hr,hu,it,nl,pl,pt,ro,ru,sl,sr,sv,uk"
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+ link: https://huggingface.co/datasets/OpenAssistant/oasst_top1_2023-08-25
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+
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+ model:
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+ - Open-Orca/Mistral-7B-OpenOrca
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+ Link:
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+ https://huggingface.co/Open-Orca/Mistral-7B-OpenOrca
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+
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+ 100 examples of generating:
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+ - Link:
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+ https://huggingface.co/NickyNicky/Mistral-7B-OpenOrca-oasst_top1_2023-08-25-v2/blob/main/output.xlsx
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+
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+ Activated training with:
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+ - Link:
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+ https://huggingface.co/blog/tomaarsen/attention-sinks
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+ https://github.com/tomaarsen/attention_sinks
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+ https://arxiv.org/abs/2309.17453
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+
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+ Version:
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+ - Link:
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+ https://huggingface.co/NickyNicky/Mistral-7B-OpenOrca-oasst_top1_2023-08-25-v1
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+ https://huggingface.co/NickyNicky/Mistral-7B-OpenOrca-oasst_top1_2023-08-25-v3
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+
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+ ```
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+
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+
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+ ##
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+
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+
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+ ```py
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+ # attention-sinks
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+ pip install attention_sinks
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+
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+ # flash-attn
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+ !export CUDA_HOME=/usr/local/cuda-11.8
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+ !MAX_JOBS=4 pip install flash-attn --no-build-isolation -qqq
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+ !pip install git+"https://github.com/HazyResearch/flash-attention.git#subdirectory=csrc/rotary" -qqq
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+ ```
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+
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+
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+ ## Version
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+ ```py
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+ import torch, transformers,torchvision
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+ torch.__version__,transformers.__version__, torchvision.__version__
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+ #OUTPUTS: ('2.0.1+cu118', '4.34.0.dev0', '0.15.2+cu118')
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+ ```
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+
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+ ## How to use
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+ ```py
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+
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+ from transformers import (
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+ AutoModelForCausalLM,
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+ AutoTokenizer,
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+ BitsAndBytesConfig,
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+ HfArgumentParser,
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+ TrainingArguments,
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+ pipeline,
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+ logging,
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+ GenerationConfig,
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+ TextIteratorStreamer,
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+ )
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+
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+ from attention_sinks import AutoModelForCausalLM
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+
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+ import torch
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+
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+ # model_id = 'Open-Orca/Mistral-7B-OpenOrca'
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+ model_id='NickyNicky/Mistral-7B-OpenOrca-oasst_top1_2023-08-25-v2'
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+
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+ model = AutoModelForCausalLM.from_pretrained(model_id,
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+ device_map="auto",
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+ trust_remote_code=True,
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+ torch_dtype=torch.bfloat16,
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+ load_in_4bit=True,
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+ low_cpu_mem_usage= True,
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+
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+ attention_sink_size=4,
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+ attention_sink_window_size=1024, #512, # <- Low for the sake of faster generation
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+ )
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+
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+ max_length=2048
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+ print("max_length",max_length)
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+
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+
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+ tokenizer = AutoTokenizer.from_pretrained(model_id,
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+ # use_fast = False,
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+ max_length=max_length,)
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+
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+ tokenizer.pad_token = tokenizer.eos_token
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+ tokenizer.padding_side = 'right'
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+
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+ #EXAMPLE #1
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+ txt="""<|im_start|>user
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+ I'm looking for an efficient Python script to output prime numbers. Can you help me out? I'm interested in a script that can handle large numbers and output them quickly. Also, it would be great if the script could take a range of numbers as input and output all the prime numbers within that range. Can you generate a script that fits these requirements? Thanks!<|im_end|>
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+ <|im_start|>assistant
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+ """
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+
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+ #EXAMPLE #2
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+ txt="""<|im_start|>user
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+ Estoy desarrollando una REST API con Nodejs, y estoy tratando de aplicar algún sistema de seguridad, ya sea con tokens o algo similar, me puedes ayudar?<|im_end|>
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+ <|im_start|>assistant
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+ """
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+
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+ inputs = tokenizer.encode(txt, return_tensors="pt").to("cuda")
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+
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+ generation_config = GenerationConfig(
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+ max_new_tokens=max_new_tokens,
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+ temperature=0.7,
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+ top_p=0.9,
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+ top_k=len_tokens,
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+ repetition_penalty=1.11,
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+ do_sample=True,
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+ # pad_token_id=tokenizer.eos_token_id,
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+ # eos_token_id=tokenizer.eos_token_id,
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+ # use_cache=True,
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+ # stopping_criteria= StoppingCriteriaList([stopping_criteria]),
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+ )
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+ outputs = model.generate(generation_config=generation_config,
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+ input_ids=inputs,)
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+ tokenizer.decode(outputs[0], skip_special_tokens=False) #True
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+ ```
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+ "<|im_end|>": 32000,
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+ "<|im_start|>": 32001
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+ }
config.json ADDED
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+ {
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+ "_name_or_path": "Open-Orca/Mistral-7B-OpenOrca",
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+ "architectures": [
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+ "MistralForCausalLM"
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+ ],
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+ "bos_token_id": 1,
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+ "eos_token_id": 32000,
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+ "hidden_act": "silu",
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+ "hidden_size": 4096,
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+ "initializer_range": 0.02,
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+ "intermediate_size": 14336,
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+ "max_position_embeddings": 32768,
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+ "model_type": "mistral",
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+ "num_attention_heads": 32,
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+ "num_hidden_layers": 32,
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+ "num_key_value_heads": 8,
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+ "rms_norm_eps": 1e-05,
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+ "rope_theta": 10000.0,
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+ "sliding_window": 4096,
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+ "tie_word_embeddings": false,
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+ "torch_dtype": "bfloat16",
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+ "transformers_version": "4.34.0",
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+ "use_cache": true,
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+ "vocab_size": 32002
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+ }
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+ "bos_token_id": 1,
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+ "chat_template": "{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}",
55
+ "clean_up_tokenization_spaces": false,
56
+ "eos_token": "<|im_end|>",
57
+ "legacy": true,
58
+ "model_max_length": 1000000000000000019884624838656,
59
+ "pad_token": null,
60
+ "sp_model_kwargs": {},
61
+ "spaces_between_special_tokens": false,
62
+ "tokenizer_class": "LlamaTokenizer",
63
+ "trust_remote_code": false,
64
+ "unk_token": "<unk>",
65
+ "use_default_system_prompt": true,
66
+ "use_fast": true
67
+ }