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ruGPT-3.5 13B LoRA: Adapter-Only Version
Welcome to the adapter-only version of ruGPT-3.5 13B LoRA. This model is built upon the foundation of ruGPT-3.5-13B.
📌 Important: This model was trained using settings identical to GigaSaiga, but incorporates additional dataset.
🔗 Training code is here.
Note: If you prefer, you can opt to use the ruGPT-3.5 13B fp16 base model.
Code sample
import torch
from peft import PeftModel, PeftConfig
from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
MODEL_NAME = "evilfreelancer/ruGPT-3.5-13B-lora"
DEFAULT_MESSAGE_TEMPLATE = "<s>{role}\n{content}</s>\n"
DEFAULT_SYSTEM_PROMPT = "Ты — ruGPT-3.5, русскоязычный автоматический ассистент на 13 миллиардов параметров. Ты разговариваешь с людьми и помогаешь им."
class Conversation:
def __init__(
self,
message_template=DEFAULT_MESSAGE_TEMPLATE,
system_prompt=DEFAULT_SYSTEM_PROMPT,
start_token_id=2,
bot_token_id=46787
):
self.message_template = message_template
self.start_token_id = start_token_id
self.bot_token_id = bot_token_id
self.messages = [{
"role": "system",
"content": system_prompt
}]
def get_start_token_id(self):
return self.start_token_id
def get_bot_token_id(self):
return self.bot_token_id
def add_user_message(self, message):
self.messages.append({
"role": "user",
"content": message
})
def add_bot_message(self, message):
self.messages.append({
"role": "bot",
"content": message
})
def get_prompt(self, tokenizer):
final_text = ""
for message in self.messages:
message_text = self.message_template.format(**message)
final_text += message_text
final_text += tokenizer.decode([self.start_token_id, self.bot_token_id])
return final_text.strip()
def generate(model, tokenizer, prompt, generation_config):
data = tokenizer(prompt, return_tensors="pt")
data = {k: v.to(model.device) for k, v in data.items()}
output_ids = model.generate(
**data,
generation_config=generation_config
)[0]
output_ids = output_ids[len(data["input_ids"][0]):]
output = tokenizer.decode(output_ids, skip_special_tokens=True)
return output.strip()
config = PeftConfig.from_pretrained(MODEL_NAME)
model = AutoModelForCausalLM.from_pretrained(
config.base_model_name_or_path,
load_in_8bit=True,
torch_dtype=torch.float16,
device_map="auto"
)
model = PeftModel.from_pretrained(
model,
MODEL_NAME,
torch_dtype=torch.float16
)
model.eval()
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, use_fast=False)
generation_config = GenerationConfig.from_pretrained(MODEL_NAME)
print(generation_config)
conversation = Conversation()
while True:
user_message = input("User: ")
if user_message.strip() == "/reset":
conversation = Conversation()
print("History reset completed!")
continue
if user_message.strip() == "":
continue
conversation.add_user_message(user_message)
prompt = conversation.get_prompt(tokenizer)
output = generate(
model=model,
tokenizer=tokenizer,
prompt=prompt,
generation_config=generation_config
)
conversation.add_bot_message(output)
print("ruGPT-3.5:", output)
print()
print("==============================")
print()
Examples
User: Привет! Почему трава зелёная?
ruGPT-3.5: Трава зелёная благодаря хлорофиллу - пигменту растений, который поглощает световые волны определенной длины и преобразует их в энергию химических связей. Хлорофилл также обеспечивает растения питательными веществами из почвы через фотосинтез.
User: Придумай стишок про нейросети.
ruGPT-3.5: Нейросеть – это чудо науки,
Она способна решать любые задачи.
И если ты хочешь стать программистом,
То лучше всего изучать нейронные сети.
User: Высота самой высокой горы на Земле?
ruGPT-3.5: Самая высокая гора на Земле находится в Гималаях и называется Эверест (8848 метров).
User: Сколько максимум лет может прожить галопогосская черепаха?
ruGPT-3.5: Галопогосские черепахи могут жить до 100 лет и более. Они являются одними из самых долгоживущих животных на планете.
📚 Training Datasets
The datasets utilized for training this model are consistent with those used for Saiga-2.
Here's the comprehensive list:
- ru_turbo_alpaca
- ru_turbo_alpaca_evol_instruct
- ru_turbo_saiga
- ru_sharegpt_cleaned
- oasst1_ru_main_branch
- gpt_roleplay_realm
- ru_instruct_gpt4
🛠 Training Procedure
The following bitsandbytes
quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
⚙️ Framework Versions
Ensure you have the following framework versions for compatibility:
- PyTorch 2.1.0
- PEFT 0.5.0
- bitsandbytes 0.41.1
- transformers 4.34.0
Links
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