消费级 GPU 微调Llama 3 ：百万规模数据 (附数据&源码)

https://github.com/microsoft/DeepSpeed

from datasets import load_dataset              


dataset = load_dataset("teknium/OpenHermes-2.5")

def _return_header(message)-> str:                  role = message["from"]                  header = ""                  if role == "system":                      header = "system"                  elif role == "gpt":                      header = "assistant"                  elif role == "human":                      header = "user"                  return header              


def encode_header(message):                  text = ''                  text = text + "<|start_header_id|>"                  header = _return_header(message)                  text = text + header                  text = text + "<|end_header_id|>"                  text = text + "\n\n"                  return text              


def encode_message(message)->str:                  text = encode_header(message)                  text = text + message["value"].strip()                  text = text + "<|eot_id|>"                  return text              


def encode_dialog_prompt(dialog):                  text = ''                  text = text + "<|begin_of_text|>"                  for message in dialog:                      text = text + encode_message(message)                  return textds = dataset.map(lambda x: {"content":encode_dialog_prompt(x['conversations'])}, num_proc=10)

ds = ds.remove_columns(['custom_instruction', 'topic', 'model_name', 'model', 'skip_prompt_formatting', 'category', 'conversations', 'views', 'language', 'id', 'title', 'idx', 'hash', 'avatarUrl', 'system_prompt', 'source'])              train_test_split = ds["train"].train_test_split(test_size=0.3)

train_test_split.push_to_hub("sumandas/openhermes-2.5-llama3")

<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are an AI assistant. Provide a detailed answer so user don't need to search outside to understand the answer.<|eot_id|><|start_header_id|>user<|end_header_id|> Instructions: Given a sentence, generate what should be the most likely next statement. The next statement should be reasonable and logically correct. Input: The screen is full of white bubbles and words, while a pair of hands plays the piano. The bubbles and words disappear and it Output:<|eot_id|><|start_header_id|>assistant<|end_header_id|> Output: becomes apparent that the hands are creating a visual representation of the music being played, captivating the audience with this unique sensory experience.<|eot_id|>

git+https://github.com/huggingface/transformers              git+https://github.com/huggingface/accelerate              git+https://github.com/huggingface/peft              git+https://github.com/huggingface/trl              huggingface-hub              bitsandbytes              evaluate              datasets              einops              wandb              tiktoken              xformers              sentencepiece              deepspeed              torch==2.2.2

accelerate launch --config_file "deepspeed_config.yaml"  train.py \              --seed 100 \              --model_name_or_path "meta-llama/Meta-Llama-3-8B-Instruct" \              --dataset_name "sumandas/openhermes-2.5-llama3" \              --chat_template_format "none" \              --add_special_tokens False \              --append_concat_token False \              --splits "train,test" \              --max_seq_len 2048 \              --num_train_epochs 1 \              --logging_steps 5 \              --log_level "info" \              --logging_strategy "steps" \              --evaluation_strategy "epoch" \              --save_strategy "steps" \              --push_to_hub \              --hub_private_repo True \              --report_to "wandb" \              --hub_strategy "every_save" \              --bf16 True \              --packing True \              --learning_rate 1e-4 \              --lr_scheduler_type "cosine" \              --weight_decay 1e-4 \              --warmup_ratio 0.0 \              --max_grad_norm 1.0 \              --output_dir "llama3-openhermes-2.5" \              --per_device_train_batch_size 4\              --per_device_eval_batch_size 4\              --gradient_accumulation_steps 2 \              --gradient_checkpointing True \              --use_reentrant True \              --dataset_text_field "content" \              --use_flash_attn True \              --use_peft_lora True \              --lora_r 8 \              --lora_alpha 16 \              --lora_dropout 0.1 \              --lora_target_modules "all-linear" \              --use_4bit_quantization True \              --use_nested_quant True \              --bnb_4bit_compute_dtype "bfloat16" \              --bnb_4bit_quant_storage_dtype "bfloat16"

{                "train/learning_rate": 0.00004551803455482833,                "eval/steps_per_second": 0.893,                "_wandb.runtime": 51487,                "_runtime": 51480.36651659012,                "_timestamp": 1713698971.6200776,                "train/epoch": 1.0571428571428572,                "train/grad_norm": 0.14189070214353952,                "train/global_step": 8325,                "eval/samples_per_second": 7.141,                "_step": 1665,                "eval/loss": 0.963840126991272,                "train/loss": 0.9674,                "eval/runtime": 7532.9797              }

from peft import PeftModel              from transformers import AutoModelForCausalLM              


base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Meta-Llama-3-8B-Instruct",torch_dtype=torch.bfloat16, device_map="auto")              adapter_model = PeftModel.from_pretrained(base_model, "sumandas/llama3-openhermes-2.5")              adapter_model.merge_and_unload()

adapter_model.push_to_hub("sumandas/llama3-openhermes-2.5")

import torch              import transformers              from transformers import pipeline              


pipe = pipeline("text-generation", model="sumandas/llama3-openhermes-2.5",model_kwargs={"torch_dtype": torch.bfloat16},                  device="cuda")              


generate_kwargs = {                  "do_sample": True,                  "temperature": 0.7,                  "max_new_tokens": 35,              }pipe("<|begin_of_text|><|start_header_id|>user<|end_header_id|> Describe the food of Thailand in Chinese<|eot_id|><|start_header_id|>assistant<|end_header_id|>",max_length=2048)