Model save

README.md

@@ -0,0 +1,58 @@
+---
+base_model: google/gemma-3-4b-pt
+library_name: transformers
+model_name: gemma-3-4b-anomaly-detector
+tags:
+- generated_from_trainer
+- trl
+- sft
+licence: license
+---
+
+# Model Card for gemma-3-4b-anomaly-detector
+
+This model is a fine-tuned version of [google/gemma-3-4b-pt](https://huggingface.co/google/gemma-3-4b-pt).
+It has been trained using [TRL](https://github.com/huggingface/trl).
+
+## Quick start
+
+```python
+from transformers import pipeline
+
+question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
+generator = pipeline("text-generation", model="arefrazavi/gemma-3-4b-anomaly-detector", device="cuda")
+output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
+print(output["generated_text"])
+```
+
+## Training procedure
+
+ 
+
+
+This model was trained with SFT.
+
+### Framework versions
+
+- TRL: 0.15.2
+- Transformers: 4.50.0.dev0
+- Pytorch: 2.6.0
+- Datasets: 3.4.1
+- Tokenizers: 0.21.1
+
+## Citations
+
+
+
+Cite TRL as:
+    
+```bibtex
+@misc{vonwerra2022trl,
+	title        = {{TRL: Transformer Reinforcement Learning}},
+	author       = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
+	year         = 2020,
+	journal      = {GitHub repository},
+	publisher    = {GitHub},
+	howpublished = {\url{https://github.com/huggingface/trl}}
+}
+```

all_results.json

@@ -0,0 +1,8 @@
+{
+    "total_flos": 3.043890401016545e+17,
+    "train_loss": 0.2311508838485622,
+    "train_runtime": 4878.1888,
+    "train_samples": 7954,
+    "train_samples_per_second": 5.225,
+    "train_steps_per_second": 0.326
+}

train_results.json

@@ -0,0 +1,8 @@
+{
+    "total_flos": 3.043890401016545e+17,
+    "train_loss": 0.2311508838485622,
+    "train_runtime": 4878.1888,
+    "train_samples": 7954,
+    "train_samples_per_second": 5.225,
+    "train_steps_per_second": 0.326
+}

trainer_state.json

@@ -0,0 +1,147 @@
+{
+  "best_metric": null,
+  "best_model_checkpoint": null,
+  "epoch": 9.940438871473354,
+  "eval_steps": 500,
+  "global_step": 1590,
+  "is_hyper_param_search": false,
+  "is_local_process_zero": true,
+  "is_world_process_zero": true,
+  "log_history": [
+    {
+      "epoch": 0.6269592476489029,
+      "grad_norm": 0.4195554256439209,
+      "learning_rate": 0.0002,
+      "loss": 0.6385,
+      "step": 100
+    },
+    {
+      "epoch": 1.250783699059561,
+      "grad_norm": 0.3866879343986511,
+      "learning_rate": 0.0002,
+      "loss": 0.4144,
+      "step": 200
+    },
+    {
+      "epoch": 1.877742946708464,
+      "grad_norm": 0.34548789262771606,
+      "learning_rate": 0.0002,
+      "loss": 0.3602,
+      "step": 300
+    },
+    {
+      "epoch": 2.501567398119122,
+      "grad_norm": 0.40215349197387695,
+      "learning_rate": 0.0002,
+      "loss": 0.3016,
+      "step": 400
+    },
+    {
+      "epoch": 3.1253918495297803,
+      "grad_norm": 0.4264250099658966,
+      "learning_rate": 0.0002,
+      "loss": 0.2818,
+      "step": 500
+    },
+    {
+      "epoch": 3.7523510971786833,
+      "grad_norm": 0.38338279724121094,
+      "learning_rate": 0.0002,
+      "loss": 0.2397,
+      "step": 600
+    },
+    {
+      "epoch": 4.376175548589342,
+      "grad_norm": 0.4163173735141754,
+      "learning_rate": 0.0002,
+      "loss": 0.2157,
+      "step": 700
+    },
+    {
+      "epoch": 5.0,
+      "grad_norm": 0.644266664981842,
+      "learning_rate": 0.0002,
+      "loss": 0.2047,
+      "step": 800
+    },
+    {
+      "epoch": 5.6269592476489025,
+      "grad_norm": 0.4428400695323944,
+      "learning_rate": 0.0002,
+      "loss": 0.1671,
+      "step": 900
+    },
+    {
+      "epoch": 6.250783699059561,
+      "grad_norm": 0.3752143383026123,
+      "learning_rate": 0.0002,
+      "loss": 0.1598,
+      "step": 1000
+    },
+    {
+      "epoch": 6.877742946708464,
+      "grad_norm": 0.40711358189582825,
+      "learning_rate": 0.0002,
+      "loss": 0.1446,
+      "step": 1100
+    },
+    {
+      "epoch": 7.501567398119122,
+      "grad_norm": 0.45269152522087097,
+      "learning_rate": 0.0002,
+      "loss": 0.1257,
+      "step": 1200
+    },
+    {
+      "epoch": 8.12539184952978,
+      "grad_norm": 0.5310996174812317,
+      "learning_rate": 0.0002,
+      "loss": 0.1216,
+      "step": 1300
+    },
+    {
+      "epoch": 8.752351097178684,
+      "grad_norm": 0.3715245723724365,
+      "learning_rate": 0.0002,
+      "loss": 0.109,
+      "step": 1400
+    },
+    {
+      "epoch": 9.376175548589341,
+      "grad_norm": 0.4838401973247528,
+      "learning_rate": 0.0002,
+      "loss": 0.1009,
+      "step": 1500
+    },
+    {
+      "epoch": 9.940438871473354,
+      "step": 1590,
+      "total_flos": 3.043890401016545e+17,
+      "train_loss": 0.2311508838485622,
+      "train_runtime": 4878.1888,
+      "train_samples_per_second": 5.225,
+      "train_steps_per_second": 0.326
+    }
+  ],
+  "logging_steps": 100,
+  "max_steps": 1590,
+  "num_input_tokens_seen": 0,
+  "num_train_epochs": 10,
+  "save_steps": 500,
+  "stateful_callbacks": {
+    "TrainerControl": {
+      "args": {
+        "should_epoch_stop": false,
+        "should_evaluate": false,
+        "should_log": false,
+        "should_save": true,
+        "should_training_stop": true
+      },
+      "attributes": {}
+    }
+  },
+  "total_flos": 3.043890401016545e+17,
+  "train_batch_size": 8,
+  "trial_name": null,
+  "trial_params": null
+}