bethrezen commited on
Commit
36aac00
·
1 Parent(s): f5c6854

initial, eval_loss: 0.5200754404067993

Browse files
.gitattributes CHANGED
@@ -33,3 +33,4 @@ saved_model/**/* 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
README.md CHANGED
@@ -1,3 +1,109 @@
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- ---
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- license: mit
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- ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ---
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+ license: mit
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+ datasets:
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+ - Egor-AI/Russian_thinking_dataset
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+ language:
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+ - ru
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+ - en
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+ base_model:
9
+ - t-tech/T-lite-it-1.0
10
+ pipeline_tag: question-answering
11
+ library_name: peft
12
+ tags:
13
+ - chat
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+ - o1
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+ - cot
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+ - thinking
17
+ - reflection
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+ ---
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+
20
+ # Russian o1 / T-lite-it-1.0 LoRA
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+
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+ Based on https://huggingface.co/evilfreelancer/o1_t-lite-it-1.0_lora
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+
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+ LoRA-адаптер для модели [T-lite-it-1.0](https://huggingface.co/t-tech/T-lite-it-1.0) обученный на
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+ датасете [Egor-AI/Russian_thinking_dataset](https://huggingface.co/datasets/Egor-AI/Russian_thinking_dataset) (машинный
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+ перевод на русский язык
27
+ датасета [BintangFortuna/OpenO1-SFT-EN-SY](https://huggingface.co/datasets/BintangFortuna/OpenO1-SFT-EN-SY)).
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+
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+ Обученная модель способна имитировать логические размышлению на русском языке по аналогии с тем, как
30
+ это делает `o1` от `OpenAI`.
31
+
32
+ Необходимо использовать следующего вида системный промт:
33
+
34
+ ```
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+ Вы — ИИ-помощник. Отформатируйте свои ответы следующим образом: <Thought> Ваши мысли (понимание, рассуждения) </Thought> <output> Ваш ответ </output>
36
+ ```
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+
38
+ Обучение производилось при помощи утилиты [impruver](https://github.com/EvilFreelancer/impruver) используя конфигурацию
39
+ [T-lite-it/7B_lora_thinking](https://github.com/EvilFreelancer/impruver/blob/main/recipes/configs/T-lite-it/7B_lora_thinking.yaml) с донастройкой:
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+ - load_in_4bit: false
41
+ - без max_tokens_count
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+ - optim: adamw_8bit
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+ - gradient_accumulation_steps: 1
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+
45
+ На всё про всё ушло примерно 17.6 часов на 1xH100 80GB, при этом понадобилось 67Гб видеопамяти.
46
+
47
+ Результатирующий eval_loss: 0.5200754404067993
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+
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+ W&B run: https://wandb.ai/b37h3z3n/trains/runs/6vwvuu46?nw=nwuserb37h3z3n
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+
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+
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+ ```yaml
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+ output_dir: ./models/T-lite-it_7B_lora_thinking
54
+ train_path: ./train.T-lite-it_7B_lora_thinking.jsonl
55
+ val_path: ./val.T-lite-it_7B_lora_thinking.jsonl
56
+
57
+ datasets:
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+ - name: Egor-AI/Russian_thinking_dataset
59
+ converter: impruver.instruction_to_messages
60
+ add_global_bos: false
61
+ add_global_eos: false
62
+ mapping:
63
+ system: system
64
+ instruction: prompt
65
+ output: response
66
+
67
+ model:
68
+ class: transformers.AutoModelForCausalLM
69
+ name: t-tech/T-lite-it-1.0
70
+ load_in_4bit: false
71
+ load_in_8bit: false
72
+ dtype: bf16
73
+
74
+ lora:
75
+ r: 16
76
+ lora_alpha: 16
77
+ lora_dropout: 0
78
+ bias: none
79
+ target_modules: [ q_proj, k_proj, v_proj, o_proj, gate_proj, down_proj, up_proj ]
80
+ task_type: CAUSAL_LM
81
+
82
+ tokenizer:
83
+ class: transformers.AutoTokenizer
84
+ name: t-tech/T-lite-it-1.0
85
+ # max_tokens_count: 1500
86
+
87
+ trainer:
88
+ eval_strategy: steps
89
+ save_strategy: steps
90
+ eval_steps: 100
91
+ save_steps: 100
92
+ per_device_train_batch_size: 1
93
+ per_device_eval_batch_size: 1
94
+ gradient_accumulation_steps: 1
95
+ logging_steps: 10
96
+ learning_rate: 0.0004
97
+ num_train_epochs: 3
98
+ lr_scheduler_type: cosine
99
+ warmup_steps: 16
100
+ optim: adamw_torch_4bit
101
+ metric_for_best_model: eval_loss
102
+ load_best_model_at_end: true
103
+ save_total_limit: 2
104
+ seed: 42
105
+ remove_unused_columns: false
106
+ max_grad_norm: 1.0
107
+ weight_decay: 0.08
108
+ torch_compile: false
109
+ ```
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+ "bias": "none",
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+ "eva_config": null,
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+ "exclude_modules": null,
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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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+ "layer_replication": null,
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+ "layers_pattern": null,
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+ "layers_to_transform": null,
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+ "loftq_config": {},
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+ "lora_alpha": 16,
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+ "lora_bias": false,
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+ "lora_dropout": 0,
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+ "megatron_config": null,
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+ "megatron_core": "megatron.core",
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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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+ "rank_pattern": {},
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+ "revision": null,
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+ "target_modules": [
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+ "o_proj",
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+ "q_proj",
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+ "down_proj",
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+ "up_proj",
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+ "gate_proj",
31
+ "k_proj",
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+ "v_proj"
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+ ],
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+ "task_type": "CAUSAL_LM",
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+ "use_dora": false,
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+ "use_rslora": false
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+ }
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1
+ import fire
2
+ from typing import List, Dict
3
+ import torch
4
+ from peft import PeftModel
5
+ from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig, BitsAndBytesConfig
6
+
7
+ MODEL_BASE = "t-tech/T-lite-it-1.0"
8
+ MODEL_ADAPTER = "evilfreelancer/o1_t-lite-it-1.0_lora"
9
+
10
+ SYSTEM_PROMPT = "Вы — ИИ-помощник. Отформатируйте свои ответы следующим образом: <Thought> Ваши мысли (понимание, рассуждения) </Thought> <output> Ваш ответ </output>"
11
+
12
+
13
+ class ChatHistory:
14
+ def __init__(self, history_limit: int = None, system_prompt: str = None):
15
+ self.history_limit: int | None = history_limit
16
+ self.system_prompt: str | None = system_prompt
17
+ self.messages: List[Dict] = []
18
+ if self.system_prompt is not None:
19
+ self.messages.append({"role": "system", "content": self.system_prompt})
20
+
21
+ def add_message(self, role: str, message: str):
22
+ self.messages.append({"role": role, "content": message})
23
+ self.trim_history()
24
+
25
+ def add_user_message(self, message: str):
26
+ self.add_message("user", message)
27
+
28
+ def add_assistant_message(self, message: str):
29
+ self.add_message("assistant", message)
30
+
31
+ def add_function_call(self, message: str):
32
+ self.add_message("function_call", message)
33
+
34
+ def add_function_response(self, message: str):
35
+ self.add_message("function_response", message)
36
+
37
+ def trim_history(self):
38
+ appendix = 0
39
+ if self.system_prompt is not None:
40
+ appendix = 1
41
+ if self.history_limit is not None and len(self.messages) > self.history_limit + appendix:
42
+ overflow = len(self.messages) - (self.history_limit + appendix)
43
+ self.messages = [self.messages[0]] + self.messages[overflow + appendix:]
44
+
45
+ def get_messages(self) -> list:
46
+ return self.messages
47
+
48
+
49
+ def generate(model, tokenizer, prompt, generation_config):
50
+ data = tokenizer(prompt, return_tensors="pt")
51
+ data = {k: v.to(model.device) for k, v in data.items()}
52
+ output_ids = model.generate(**data, generation_config=generation_config)[0]
53
+ output_ids = output_ids[len(data["input_ids"][0]):]
54
+ output = tokenizer.decode(output_ids, skip_special_tokens=True)
55
+ return output.strip()
56
+
57
+
58
+ def get_prompt(tokenizer, messages: List[Dict], add_generation_prompt: bool = False):
59
+ return tokenizer.apply_chat_template(
60
+ messages,
61
+ add_special_tokens=False,
62
+ tokenize=False,
63
+ add_generation_prompt=add_generation_prompt,
64
+ )
65
+
66
+
67
+ def chat(
68
+ history_limit: int = 1,
69
+ system_prompt: str | None = SYSTEM_PROMPT,
70
+ max_new_tokens: int = 2048,
71
+ repetition_penalty: float = 1.2,
72
+ do_sample: bool = True,
73
+ temperature: float = 0.5,
74
+ top_p: float = 0.6,
75
+ top_k: int = 40,
76
+ ):
77
+ #
78
+ # Tokenizer preparation
79
+ #
80
+
81
+ tokenizer = AutoTokenizer.from_pretrained(MODEL_BASE)
82
+
83
+ #
84
+ # Model preparation
85
+ #
86
+
87
+ # Quantization config
88
+ quantization_config = BitsAndBytesConfig(
89
+ load_in_4bit=True,
90
+ bnb_4bit_compute_dtype=torch.bfloat16,
91
+ bnb_4bit_quant_type="nf4",
92
+ bnb_4bit_use_double_quant=True
93
+ )
94
+
95
+ # Generator config
96
+ generation_config = GenerationConfig.from_pretrained(MODEL_ADAPTER)
97
+ generation_config.max_new_tokens = max_new_tokens
98
+ generation_config.repetition_penalty = repetition_penalty
99
+ generation_config.do_sample = do_sample
100
+ generation_config.temperature = temperature
101
+ generation_config.top_p = top_p
102
+ generation_config.top_k = top_k
103
+
104
+ # Read model from folder with trained checkpoints
105
+ model = AutoModelForCausalLM.from_pretrained(
106
+ MODEL_BASE,
107
+ generation_config=generation_config,
108
+ quantization_config=quantization_config,
109
+ torch_dtype=torch.bfloat16,
110
+ attn_implementation=None
111
+ )
112
+
113
+ # If we've trained a LoRA adapter
114
+ model = PeftModel.from_pretrained(
115
+ model=model,
116
+ model_id=MODEL_ADAPTER,
117
+ torch_dtype=torch.bfloat16,
118
+ )
119
+
120
+ #
121
+ # Chat loop
122
+ #
123
+
124
+ # Start chat loop
125
+ chat_history = ChatHistory(history_limit, system_prompt)
126
+ while True:
127
+ user_message = input("User: ")
128
+
129
+ # Reset chat command
130
+ if user_message.strip() == "/reset":
131
+ chat_history = ChatHistory(history_limit, system_prompt)
132
+ print("History reset completed!")
133
+ continue
134
+
135
+ # Skip empty messages from user
136
+ if user_message.strip() == "":
137
+ continue
138
+
139
+ # Add user message to chat history
140
+ chat_history.add_user_message(user_message)
141
+
142
+ # Get list of messages
143
+ prompt = get_prompt(tokenizer, chat_history.get_messages(), True)
144
+
145
+ # Generate response
146
+ output = generate(model, tokenizer, prompt, generation_config)
147
+
148
+ # Save response to chat history as assistant's message
149
+ chat_history.add_assistant_message(output)
150
+ print("Assistant:", output)
151
+
152
+
153
+ if __name__ == "__main__":
154
+ fire.Fire(chat)
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