Upload 2 files

config.json

@@ -0,0 +1,44 @@
+{
+  "model_type": "llama",
+  "hidden_size": 4096,
+  "num_attention_heads": 16,
+  "num_hidden_layers": 32,
+  "intermediate_size": 11008,
+  "hidden_act": "gelu",
+  "initializer_range": 0.02,
+  "layer_norm_eps": 1e-5,
+  "max_position_embeddings": 2048,
+  "vocab_size": 32000,
+  "model_name": "LlamaForSequenceClassification",
+  "pipeline_tag": "text-generation",
+  "peft_config": {
+    "r": 16,
+    "target_modules": [
+      "q_proj", "k_proj", "v_proj", "o_proj",
+      "gate_proj", "up_proj", "down_proj",
+      "embed_tokens", "lm_head"
+    ],
+    "lora_alpha": 16,
+    "lora_dropout": 0,
+    "bias": "none",
+    "use_gradient_checkpointing": "unsloth",
+    "random_state": 3407,
+    "use_rslora": false,
+    "loftq_config": null
+  },
+  "training_args": {
+    "per_device_train_batch_size": 2,
+    "gradient_accumulation_steps": 4,
+    "warmup_steps": 5,
+    "max_steps": 60,
+    "learning_rate": 2e-4,
+    "fp16": true,
+    "bf16": false,
+    "logging_steps": 1,
+    "optim": "adamw_8bit",
+    "weight_decay": 0.01,
+    "lr_scheduler_type": "linear",
+    "seed": 3407,
+    "output_dir": "outputs"
+  }
+}

handler.py

@@ -0,0 +1,27 @@
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+class ModelHandler:
+    def __init__(self):
+        self.model = None
+        self.tokenizer = None
+
+    def load_model(self):
+        # 加载模型和分词器
+        self.model = AutoModelForCausalLM.from_pretrained("your-model-path")
+        self.tokenizer = AutoTokenizer.from_pretrained("your-model-path")
+
+    def predict(self, inputs):
+        # 将输入转换为模型可以处理的格式
+        inputs = self.tokenizer(inputs, return_tensors="pt")
+        with torch.no_grad():
+            outputs = self.model(**inputs)
+        return outputs
+
+handler = ModelHandler()
+handler.load_model()
+
+def handler(event, context):
+    inputs = event["data"]
+    outputs = handler.predict(inputs)
+    return outputs