Instructions to use dschauhan08/MiniCPM5-1B-Checkpoints with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dschauhan08/MiniCPM5-1B-Checkpoints with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("dschauhan08/MiniCPM5-1B-Checkpoints", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Desktop
Training in progress, step 2940, checkpoint
Browse files
last-checkpoint/adapter_model.safetensors
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 44871152
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:8e06eb6076d702c790a643108cff6e70d61f27c4a8c0054750ad24aee92e626a
|
| 3 |
size 44871152
|
last-checkpoint/optimizer.pt
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 23160933
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:7e62a38f86d28296ff1681105797c41a05458f9c85c4b56c154bc85e7488e2e2
|
| 3 |
size 23160933
|
last-checkpoint/scaler.pt
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 1383
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:9cf226528602f349c7c927b31d9819774aa5266cf6c2feebd36b0cbe6a6007a5
|
| 3 |
size 1383
|
last-checkpoint/scheduler.pt
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 1465
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:4c94daa81f21e6e941509b67ca17cfdc205162f25da470dddc0aedfdfaedfe74
|
| 3 |
size 1465
|
last-checkpoint/trainer_state.json
CHANGED
|
@@ -2,9 +2,9 @@
|
|
| 2 |
"best_global_step": null,
|
| 3 |
"best_metric": null,
|
| 4 |
"best_model_checkpoint": null,
|
| 5 |
-
"epoch": 1.
|
| 6 |
"eval_steps": 500,
|
| 7 |
-
"global_step":
|
| 8 |
"is_hyper_param_search": false,
|
| 9 |
"is_local_process_zero": true,
|
| 10 |
"is_world_process_zero": true,
|
|
@@ -2003,6 +2003,69 @@
|
|
| 2003 |
"learning_rate": 7.806207228653921e-05,
|
| 2004 |
"loss": 0.3635621309280396,
|
| 2005 |
"step": 2850
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 2006 |
}
|
| 2007 |
],
|
| 2008 |
"logging_steps": 10,
|
|
@@ -2022,7 +2085,7 @@
|
|
| 2022 |
"attributes": {}
|
| 2023 |
}
|
| 2024 |
},
|
| 2025 |
-
"total_flos":
|
| 2026 |
"train_batch_size": 4,
|
| 2027 |
"trial_name": null,
|
| 2028 |
"trial_params": null
|
|
|
|
| 2 |
"best_global_step": null,
|
| 3 |
"best_metric": null,
|
| 4 |
"best_model_checkpoint": null,
|
| 5 |
+
"epoch": 1.2039520835466366,
|
| 6 |
"eval_steps": 500,
|
| 7 |
+
"global_step": 2940,
|
| 8 |
"is_hyper_param_search": false,
|
| 9 |
"is_local_process_zero": true,
|
| 10 |
"is_world_process_zero": true,
|
|
|
|
| 2003 |
"learning_rate": 7.806207228653921e-05,
|
| 2004 |
"loss": 0.3635621309280396,
|
| 2005 |
"step": 2850
|
| 2006 |
+
},
|
| 2007 |
+
{
|
| 2008 |
+
"epoch": 1.1711886966315144,
|
| 2009 |
+
"grad_norm": 0.17187663912773132,
|
| 2010 |
+
"learning_rate": 7.741551233134628e-05,
|
| 2011 |
+
"loss": 0.4021602153778076,
|
| 2012 |
+
"step": 2860
|
| 2013 |
+
},
|
| 2014 |
+
{
|
| 2015 |
+
"epoch": 1.1752841199959045,
|
| 2016 |
+
"grad_norm": 0.23882046341896057,
|
| 2017 |
+
"learning_rate": 7.676994572462312e-05,
|
| 2018 |
+
"loss": 0.3855579137802124,
|
| 2019 |
+
"step": 2870
|
| 2020 |
+
},
|
| 2021 |
+
{
|
| 2022 |
+
"epoch": 1.179379543360295,
|
| 2023 |
+
"grad_norm": 0.16917739808559418,
|
| 2024 |
+
"learning_rate": 7.612540086075313e-05,
|
| 2025 |
+
"loss": 0.3872795104980469,
|
| 2026 |
+
"step": 2880
|
| 2027 |
+
},
|
| 2028 |
+
{
|
| 2029 |
+
"epoch": 1.183474966724685,
|
| 2030 |
+
"grad_norm": 0.1488448679447174,
|
| 2031 |
+
"learning_rate": 7.548190608917964e-05,
|
| 2032 |
+
"loss": 0.3769106149673462,
|
| 2033 |
+
"step": 2890
|
| 2034 |
+
},
|
| 2035 |
+
{
|
| 2036 |
+
"epoch": 1.1875703900890755,
|
| 2037 |
+
"grad_norm": 0.19162490963935852,
|
| 2038 |
+
"learning_rate": 7.48394897131591e-05,
|
| 2039 |
+
"loss": 0.3580836534500122,
|
| 2040 |
+
"step": 2900
|
| 2041 |
+
},
|
| 2042 |
+
{
|
| 2043 |
+
"epoch": 1.1916658134534657,
|
| 2044 |
+
"grad_norm": 0.19842198491096497,
|
| 2045 |
+
"learning_rate": 7.419817998851623e-05,
|
| 2046 |
+
"loss": 0.3587891340255737,
|
| 2047 |
+
"step": 2910
|
| 2048 |
+
},
|
| 2049 |
+
{
|
| 2050 |
+
"epoch": 1.195761236817856,
|
| 2051 |
+
"grad_norm": 0.1560589224100113,
|
| 2052 |
+
"learning_rate": 7.355800512240115e-05,
|
| 2053 |
+
"loss": 0.38894004821777345,
|
| 2054 |
+
"step": 2920
|
| 2055 |
+
},
|
| 2056 |
+
{
|
| 2057 |
+
"epoch": 1.1998566601822462,
|
| 2058 |
+
"grad_norm": 0.1998666226863861,
|
| 2059 |
+
"learning_rate": 7.29189932720487e-05,
|
| 2060 |
+
"loss": 0.40459065437316893,
|
| 2061 |
+
"step": 2930
|
| 2062 |
+
},
|
| 2063 |
+
{
|
| 2064 |
+
"epoch": 1.2039520835466366,
|
| 2065 |
+
"grad_norm": 0.20374324917793274,
|
| 2066 |
+
"learning_rate": 7.228117254354005e-05,
|
| 2067 |
+
"loss": 0.35155019760131834,
|
| 2068 |
+
"step": 2940
|
| 2069 |
}
|
| 2070 |
],
|
| 2071 |
"logging_steps": 10,
|
|
|
|
| 2085 |
"attributes": {}
|
| 2086 |
}
|
| 2087 |
},
|
| 2088 |
+
"total_flos": 5.1019023841848115e+17,
|
| 2089 |
"train_batch_size": 4,
|
| 2090 |
"trial_name": null,
|
| 2091 |
"trial_params": null
|