Upload fine-tuned model for task 7

adapter_config.json

@@ -10,16 +10,20 @@
   "layers_pattern": null,
   "layers_to_transform": null,
   "loftq_config": {},
-  "lora_alpha": 16,
-  "lora_dropout": 0.1,
+  "lora_alpha": 32,
+  "lora_dropout": 0.05,
   "megatron_config": null,
   "megatron_core": "megatron.core",
   "modules_to_save": null,
   "peft_type": "LORA",
-  "r": 4,
+  "r": 16,
   "rank_pattern": {},
   "revision": null,
   "target_modules": [
+    "o_proj",
+    "lm_head",
+    "down_proj",
+    "gate_up_proj",
     "qkv_proj"
   ],
   "task_type": "CAUSAL_LM",

adapter_model.safetensors

@@ -1,3 +1,3 @@
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-size 6300024
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+size 298823640

config.json

@@ -5,12 +5,16 @@
     "training_args": {
         "per_device_train_batch_size": 1,
         "gradient_accumulation_steps": 4,
-        "num_train_epochs": 10,
-        "lora_rank": 4,
-        "lora_alpha": 16,
-        "lora_dropout": 0.1,
+        "num_train_epochs": 5,
+        "lora_rank": 16,
+        "lora_alpha": 32,
+        "lora_dropout": 0.05,
         "target_modules": [
-            "qkv_proj"
+            "o_proj",
+            "qkv_proj",
+            "gate_up_proj",
+            "down_proj",
+            "lm_head"
         ]
     },
     "task_info": {
@@ -18,7 +22,7 @@
         "description": "FLock and OneKey are collaborating to launch the first AI-driven smart contract security challenge, combining FLock\u2019s decentralized model training with OneKey\u2019s expertise in blockchain security. By training AI on real-world vulnerabilities and security Q&A data, we aim to build a benchmark dataset and develop models capable of detecting and mitigating risks at scale. Top contributors will be rewarded with OneKey Hardware Wallet - FLock Limited Edition",
         "task_type": "training",
         "data": {
-            "training_set_url": "https://fed-ledger-prod-dataset.s3.amazonaws.com/7/training_set.jsonl?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIASSFQ745NLT5K57N2%2F20250408%2Fus-east-2%2Fs3%2Faws4_request&X-Amz-Date=20250408T052018Z&X-Amz-Expires=3600&X-Amz-SignedHeaders=host&X-Amz-Signature=f34d5b9e480db0b7f2fc2550418322267facf17f9261193bb44c4b4783e136d7",
+            "training_set_url": "https://fed-ledger-prod-dataset.s3.amazonaws.com/7/training_set.jsonl?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIASSFQ745NLT5K57N2%2F20250408%2Fus-east-2%2Fs3%2Faws4_request&X-Amz-Date=20250408T110758Z&X-Amz-Expires=3600&X-Amz-SignedHeaders=host&X-Amz-Signature=152eb7cbf735b0fb3b67057412d43ac70a0b1ce1bb4fc1f459ad10ad6dc4228d",
             "max_params": 15000000000,
             "context_length": 8192
         },

training_args.bin

@@ -1,3 +1,3 @@
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 size 5496