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Model: davidkim205/komt-mistral-7b-v1 Source: Original Platform
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README.md
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---
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language:
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- en
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- ko
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pipeline_tag: text-generation
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tags:
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- finetuned
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---
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# komt : korean multi task instruction tuning model
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Recently, due to the success of ChatGPT, numerous large language models have emerged in an attempt to catch up with ChatGPT's capabilities.
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However, when it comes to Korean language performance, it has been observed that many models still struggle to provide accurate answers or generate Korean text effectively.
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This study addresses these challenges by introducing a multi-task instruction technique that leverages supervised datasets from various tasks to create training data for Large Language Models (LLMs).
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## Model Details
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* **Model Developers** : davidkim(changyeon kim)
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* **Repository** : https://github.com/davidkim205/komt
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* **Model Architecture** : The komt-mistral-7b-v1 is is a fine-tuned version of the Mistral-7B-Instruct-v0.1.
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## Dataset
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korean multi-task instruction dataset
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## Hardware and Software
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- nvidia driver : 535.54.03
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- CUDA Version: 12.2
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## Training
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Refer https://github.com/davidkim205/komt
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## Prompt template: Mistral
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```
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<s>[INST] {prompt} [/INST]</s>
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```
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## Usage
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```
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from transformers import AutoTokenizer, AutoModelForCausalLM
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from transformers import TextStreamer, GenerationConfig
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model_name='davidkim205/komt-mistral-7b-v1'
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model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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streamer = TextStreamer(tokenizer)
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def gen(x):
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generation_config = GenerationConfig(
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temperature=0.8,
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top_p=0.8,
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top_k=100,
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max_new_tokens=1024,
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early_stopping=True,
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do_sample=True,
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)
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q = f"[INST]{x} [/INST]"
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gened = model.generate(
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**tokenizer(
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q,
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return_tensors='pt',
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return_token_type_ids=False
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).to('cuda'),
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generation_config=generation_config,
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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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streamer=streamer,
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)
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result_str = tokenizer.decode(gened[0])
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start_tag = f"\n\n### Response: "
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start_index = result_str.find(start_tag)
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if start_index != -1:
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result_str = result_str[start_index + len(start_tag):].strip()
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return result_str
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print(gen('제주도를 1박2일로 혼자 여행하려고 하는데 여행 코스를 만들어줘'))
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```
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output
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```
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<s> [INST]제주도를 1박2일로 혼자 여행하려고 하는데 여행 코스를 만들어줘 [/INST]1박 2일 여행 코스
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제주 여행 코스는 다음과 같습니다:
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첫째 날:
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* 아침: 제주시 신라로 이동하여 한라산 국립공원에 도착합니다. 오른쪽 발자국 광장에서 눈에 띄는 맥주숍을 찾아 한 병의 맥주를 마시고 계단으로 올라가 한라산 정상에 도착합니다.
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* 점심: 정상에서 계단을 내려와 중도에 위치한 골목에서 음식을 즐깁니다. 젓갈, 곰탕, 전복 등 맛있는 제주 특산물을 맛보며 힐링을 즐깁니다.
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* 오후: 중도에서 남하하여 남서쪽 제주로 이동합니다. 남서쪽 제주는 섬의 자연 경관과 백사장, 아름다운 바다 등 아름다운 풍광이 있는 곳입니다. 숙소에 숙박합니다.
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둘째 날:
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* 아침: 남서쪽 제주에서 북서쪽 제주로 이동합니다. 이 지역은 푸른 잔디 해변과 투명한 바다가 펼쳐져 있는 아름다운 풍광입니다. 숙소에서 아침을 먹고 바다로 향하여 해변에서 휴식을 취합니다.
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* 점심: 북서쪽 제주의 앞바다에서 시원한 바다를 보며 한 끼의 해산물을 맛보고 계절에 따라 해산물 요리를 즐깁니다.
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* 오후: 남서쪽 제주에서 이동하여 서쪽 제주로 이동합니다. 서쪽 제주는 절벽과 암석 해변, 수수께끼의 돌 정원 등 독특한 경관이 있는 곳입니다. 이곳에서는 서쪽 제주의 대표적인 명소인 청량사를 방문하고 서쪽 제주의 아름다운 풍광을 감상합니다.
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* 저녁: 서쪽 제주에서 제주 시로 이동하여 한 끼의 제주 특산물을 맛보고 도착한 제주 도심에서 저녁을 즐깁니다.
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* 야간: 제주 시의 도심에서 야간 활동을 즐기며 1박 2일의 여행을 마무리합니다.
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이렇게 제주를 1박 2일로 혼자 여행하면 제주의 아름다운 풍광, 푸른 잔디 해변, 투명한 바다 등을 경험할 수 있습니다.
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```
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## Evaluation
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For objective model evaluation, we initially used EleutherAI's lm-evaluation-harness but obtained unsatisfactory results. Consequently, we conducted evaluations using ChatGPT, a widely used model, as described in [Self-Alignment with Instruction Backtranslation](https://arxiv.org/pdf/2308.06502.pdf) and [Three Ways of Using Large Language Models to Evaluate Chat](https://arxiv.org/pdf/2308.06259.pdf) .
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| model | score | average(0~5) | percentage |
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| --------------------------------------- |---------| ------------ | ---------- |
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| gpt-3.5-turbo(close) | 147 | 3.97 | 79.45% |
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| naver Cue(close) | 140 | 3.78 | 75.67% |
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| clova X(close) | 136 | 3.67 | 73.51% |
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| WizardLM-13B-V1.2(open) | 96 | 2.59 | 51.89% |
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| Llama-2-7b-chat-hf(open) | 67 | 1.81 | 36.21% |
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| Llama-2-13b-chat-hf(open) | 73 | 1.91 | 38.37% |
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| nlpai-lab/kullm-polyglot-12.8b-v2(open) | 70 | 1.89 | 37.83% |
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| kfkas/Llama-2-ko-7b-Chat(open) | 96 | 2.59 | 51.89% |
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| beomi/KoAlpaca-Polyglot-12.8B(open) | 100 | 2.70 | 54.05% |
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| **komt-llama2-7b-v1 (open)(ours)** | **117** | **3.16** | **63.24%** |
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| **komt-llama2-13b-v1 (open)(ours)** | **129** | **3.48** | **69.72%** |
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| **komt-llama-30b-v1 (open)(ours)** | **129** | **3.16** | **63.24%** |
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| **komt-mistral-7b-v1 (open)(ours)** | **131** | **3.54** | **70.81%** |
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"<s>": 1,
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}
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config.json
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config.json
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{
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"_name_or_path": "mistralai/Mistral-7B-Instruct-v0.1",
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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": 2,
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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": false,
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"vocab_size": 32000
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|
||||
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|
||||
"model.layers.5.mlp.gate_proj.weight": "pytorch_model-00001-of-00002.bin",
|
||||
"model.layers.5.mlp.up_proj.weight": "pytorch_model-00001-of-00002.bin",
|
||||
"model.layers.5.post_attention_layernorm.weight": "pytorch_model-00001-of-00002.bin",
|
||||
"model.layers.5.self_attn.k_proj.weight": "pytorch_model-00001-of-00002.bin",
|
||||
"model.layers.5.self_attn.o_proj.weight": "pytorch_model-00001-of-00002.bin",
|
||||
"model.layers.5.self_attn.q_proj.weight": "pytorch_model-00001-of-00002.bin",
|
||||
"model.layers.5.self_attn.v_proj.weight": "pytorch_model-00001-of-00002.bin",
|
||||
"model.layers.6.input_layernorm.weight": "pytorch_model-00001-of-00002.bin",
|
||||
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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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|
||||
"model.layers.7.input_layernorm.weight": "pytorch_model-00001-of-00002.bin",
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||||
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||||
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|
||||
"model.layers.7.mlp.up_proj.weight": "pytorch_model-00001-of-00002.bin",
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"model.layers.8.input_layernorm.weight": "pytorch_model-00001-of-00002.bin",
|
||||
"model.layers.8.mlp.down_proj.weight": "pytorch_model-00001-of-00002.bin",
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"model.layers.8.self_attn.q_proj.weight": "pytorch_model-00001-of-00002.bin",
|
||||
"model.layers.8.self_attn.v_proj.weight": "pytorch_model-00001-of-00002.bin",
|
||||
"model.layers.9.input_layernorm.weight": "pytorch_model-00001-of-00002.bin",
|
||||
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|
||||
"model.layers.9.mlp.gate_proj.weight": "pytorch_model-00001-of-00002.bin",
|
||||
"model.layers.9.mlp.up_proj.weight": "pytorch_model-00001-of-00002.bin",
|
||||
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|
||||
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|
||||
"model.layers.9.self_attn.o_proj.weight": "pytorch_model-00001-of-00002.bin",
|
||||
"model.layers.9.self_attn.q_proj.weight": "pytorch_model-00001-of-00002.bin",
|
||||
"model.layers.9.self_attn.v_proj.weight": "pytorch_model-00001-of-00002.bin",
|
||||
"model.norm.weight": "pytorch_model-00002-of-00002.bin"
|
||||
}
|
||||
}
|
||||
11
special_tokens_map.json
Normal file
11
special_tokens_map.json
Normal file
@@ -0,0 +1,11 @@
|
||||
{
|
||||
"additional_special_tokens": [
|
||||
"<unk>",
|
||||
"<s>",
|
||||
"</s>"
|
||||
],
|
||||
"bos_token": "<s>",
|
||||
"eos_token": "</s>",
|
||||
"pad_token": "</s>",
|
||||
"unk_token": "<unk>"
|
||||
}
|
||||
BIN
tokenizer.model
(Stored with Git LFS)
Normal file
BIN
tokenizer.model
(Stored with Git LFS)
Normal file
Binary file not shown.
49
tokenizer_config.json
Normal file
49
tokenizer_config.json
Normal file
@@ -0,0 +1,49 @@
|
||||
{
|
||||
"add_bos_token": true,
|
||||
"add_eos_token": false,
|
||||
"added_tokens_decoder": {
|
||||
"0": {
|
||||
"content": "<unk>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"1": {
|
||||
"content": "<s>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"2": {
|
||||
"content": "</s>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
}
|
||||
},
|
||||
"additional_special_tokens": [
|
||||
"<unk>",
|
||||
"<s>",
|
||||
"</s>"
|
||||
],
|
||||
"bos_token": "<s>",
|
||||
"chat_template": "{{ bos_token }}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if message['role'] == 'user' %}{{ '[INST] ' + message['content'] + ' [/INST]' }}{% elif message['role'] == 'assistant' %}{{ message['content'] + eos_token + ' ' }}{% else %}{{ raise_exception('Only user and assistant roles are supported!') }}{% endif %}{% endfor %}",
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "</s>",
|
||||
"legacy": true,
|
||||
"model_max_length": 1024,
|
||||
"pad_token": "</s>",
|
||||
"padding_side": "right",
|
||||
"sp_model_kwargs": {},
|
||||
"spaces_between_special_tokens": false,
|
||||
"tokenizer_class": "LlamaTokenizer",
|
||||
"tokenizer_file": "/home/dev/.cache/huggingface/hub/models--mistralai--Mistral-7B-Instruct-v0.1/snapshots/7ad5799710574ba1c1d953eba3077af582f3a773/tokenizer.json",
|
||||
"unk_token": "<unk>",
|
||||
"use_default_system_prompt": true
|
||||
}
|
||||
Reference in New Issue
Block a user