Instructions to use SeonghuJeon/task-7-microsoft-Phi-3.5-mini-instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use SeonghuJeon/task-7-microsoft-Phi-3.5-mini-instruct with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("microsoft/Phi-3.5-mini-instruct") model = PeftModel.from_pretrained(base_model, "SeonghuJeon/task-7-microsoft-Phi-3.5-mini-instruct") - Notebooks
- Google Colab
- Kaggle
Upload fine-tuned model for task 7
Browse files
adapter_model.safetensors
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 6300024
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:6df79c4b2c658169810f806c5dbbc2af14ba14abed471fb055a021b07ed1d236
|
| 3 |
size 6300024
|
config.json
CHANGED
|
@@ -15,7 +15,7 @@
|
|
| 15 |
"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",
|
| 16 |
"task_type": "training",
|
| 17 |
"data": {
|
| 18 |
-
"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%2F20250410%2Fus-east-2%2Fs3%2Faws4_request&X-Amz-Date=
|
| 19 |
"max_params": 15000000000,
|
| 20 |
"context_length": 8192
|
| 21 |
},
|
|
|
|
| 15 |
"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",
|
| 16 |
"task_type": "training",
|
| 17 |
"data": {
|
| 18 |
+
"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%2F20250410%2Fus-east-2%2Fs3%2Faws4_request&X-Amz-Date=20250410T032912Z&X-Amz-Expires=3600&X-Amz-SignedHeaders=host&X-Amz-Signature=5cd5954367f866de53ecf261607dbf3d9c171c3a3c35ebb84da139dc97dc8fd9",
|
| 19 |
"max_params": 15000000000,
|
| 20 |
"context_length": 8192
|
| 21 |
},
|
runs/Apr10_03-29-12_571d52fadd5c/events.out.tfevents.1744255761.571d52fadd5c
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:3ce0a7870973d8a691c0718ed9fa3aa1c5c8e988e5fb7afe08611fb3a7245453
|
| 3 |
+
size 9904
|
training_args.bin
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 5496
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:46c73d27a7a955491d9483069abaec40815d313188e8bdc96798fc11bf3eb6de
|
| 3 |
size 5496
|