项目初始化，增加了模型准备和微调两个代码文件

README.md

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+本项目采用 unsloth 进行 LLM 的微调。

src/before.py

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+from unsloth import FastLanguageModel
+import torch
+max_seq_length = 2048
+dtype = None
+load_in_4bit = True
+model, tokenizer = FastLanguageModel.from_pretrained(
+    model_name = "unsloth/llama-3-8b-bnb-4bit", 
+    max_seq_length = max_seq_length, 
+    dtype = dtype,     
+    load_in_4bit = load_in_4bit,
+    token = "https://hf-mirror.com"  
+)
+
+alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
+### Instruction:
+{}
+### Input:
+{}
+### Response:
+{}"""
+
+FastLanguageModel.for_inference(model) 
+inputs = tokenizer(
+[
+    alpaca_prompt.format(
+        "海绵宝宝的书法是不是叫做海绵体",
+        "", 
+        "", 
+    )
+], return_tensors = "pt").to("cuda")
+
+from transformers import TextStreamer
+text_streamer = TextStreamer(tokenizer)
+_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 128)
+
+EOS_TOKEN = tokenizer.eos_token # 必须添加 EOS_TOKEN
+def formatting_prompts_func(examples):
+    instructions = examples["instruction"]
+    inputs       = examples["input"]
+    outputs      = examples["output"]
+    texts = []
+    for instruction, input, output in zip(instructions, inputs, outputs):
+        # 必须添加EOS_TOKEN，否则生成将永无止境
+        text = alpaca_prompt.format(instruction, input, output) + EOS_TOKEN
+        texts.append(text)
+    return { "text" : texts, }
+pass
+

src/tunning.py

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+import os
+from unsloth import FastLanguageModel
+import torch
+from trl import SFTTrainer
+from transformers import TrainingArguments
+from datasets import load_dataset
+
+#加载模型
+max_seq_length = 2048
+dtype = None
+load_in_4bit = True
+model, tokenizer = FastLanguageModel.from_pretrained(
+    model_name = "unsloth/llama-3-8b-bnb-4bit", 
+    max_seq_length = max_seq_length, 
+    dtype = dtype,     
+    load_in_4bit = load_in_4bit,
+    token = "https://hf-mirror.com"
+)
+
+#准备训练数据
+alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
+### Instruction:
+{}
+### Input:
+{}
+### Response:
+{}"""
+
+EOS_TOKEN = tokenizer.eos_token # 必须添加 EOS_TOKEN
+def formatting_prompts_func(examples):
+    instructions = examples["instruction"]
+    inputs       = examples["input"]
+    outputs      = examples["output"]
+    texts = []
+    for instruction, input, output in zip(instructions, inputs, outputs):
+        # 必须添加EOS_TOKEN，否则无限生成
+        text = alpaca_prompt.format(instruction, input, output) + EOS_TOKEN
+        texts.append(text)
+    return { "text" : texts, }
+pass
+
+#hugging face数据集路径
+dataset = load_dataset("kigner/ruozhiba-llama3", split = "train")
+dataset = dataset.map(formatting_prompts_func, batched = True,)
+
+#设置训练参数
+model = FastLanguageModel.get_peft_model(
+    model,
+    r = 16,
+    target_modules = ["q_proj", "k_proj", "v_proj", "o_proj",
+                      "gate_proj", "up_proj", "down_proj",],
+    lora_alpha = 16,
+    lora_dropout = 0, 
+    bias = "none",    
+    use_gradient_checkpointing = True,
+    random_state = 3407,
+    max_seq_length = max_seq_length,
+    use_rslora = False,  
+    loftq_config = None, 
+)
+
+trainer = SFTTrainer(
+    model = model,
+    train_dataset = dataset,
+    dataset_text_field = "text",
+    max_seq_length = max_seq_length,
+    tokenizer = tokenizer,
+    args = TrainingArguments(
+        per_device_train_batch_size = 2,
+        gradient_accumulation_steps = 4,
+        warmup_steps = 10,
+        max_steps = 60,
+        fp16 = not torch.cuda.is_bf16_supported(),
+        bf16 = torch.cuda.is_bf16_supported(),
+        logging_steps = 1,
+        output_dir = "outputs",
+        optim = "adamw_8bit",
+        weight_decay = 0.01,
+        lr_scheduler_type = "linear",
+        seed = 3407,
+    ),
+)
+#开始训练
+trainer.train()
+
+#保存微调模型
+model.save_pretrained("lora_model") 
+
+#合并模型，保存为16位hf
+model.save_pretrained_merged("outputs", tokenizer, save_method = "merged_16bit",)
+
+#合并模型，并量化成4位gguf
+#model.save_pretrained_gguf("model", tokenizer, quantization_method = "q4_k_m")
+
+