Model save

README.md

@@ -0,0 +1,58 @@
+---
+base_model: Qwen/Qwen2.5-0.5B-Instruct
+library_name: transformers
+model_name: Qwen2.5-1.5B-Open-R1-Distill-sft-debug
+tags:
+- generated_from_trainer
+- trl
+- sft
+licence: license
+---
+
+# Model Card for Qwen2.5-1.5B-Open-R1-Distill-sft-debug
+
+This model is a fine-tuned version of [Qwen/Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct).
+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="lukedai/Qwen2.5-1.5B-Open-R1-Distill-sft-debug", device="cuda")
+output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
+print(output["generated_text"])
+```
+
+## Training procedure
+
+[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/hanbrisk-freelancer/huggingface/runs/m5vr0uy8) 
+
+
+This model was trained with SFT.
+
+### Framework versions
+
+- TRL: 0.16.0.dev0
+- Transformers: 4.49.0
+- Pytorch: 2.5.1
+- Datasets: 3.3.2
+- Tokenizers: 0.21.0
+
+## 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}}
+}
+```

adapter_config.json

@@ -23,8 +23,8 @@
   "rank_pattern": {},
   "revision": null,
   "target_modules": [
-    "q_proj",
-    "v_proj"
+    "v_proj",
+    "q_proj"
   ],
   "task_type": "CAUSAL_LM",
   "use_dora": false,

all_results.json

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+{
+    "total_flos": 1171084304973824.0,
+    "train_loss": 0.0,
+    "train_runtime": 2.6815,
+    "train_samples": 100,
+    "train_samples_per_second": 32.444,
+    "train_steps_per_second": 3.729
+}

train_results.json

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+{
+    "total_flos": 1171084304973824.0,
+    "train_loss": 0.0,
+    "train_runtime": 2.6815,
+    "train_samples": 100,
+    "train_samples_per_second": 32.444,
+    "train_steps_per_second": 3.729
+}

trainer_state.json

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+{
+  "best_metric": null,
+  "best_model_checkpoint": null,
+  "epoch": 0.9195402298850575,
+  "eval_steps": 500,
+  "global_step": 10,
+  "is_hyper_param_search": false,
+  "is_local_process_zero": true,
+  "is_world_process_zero": true,
+  "log_history": [
+    {
+      "epoch": 0.45977011494252873,
+      "grad_norm": 0.2585427165031433,
+      "learning_rate": 3.140708399750594e-05,
+      "loss": 1.0544,
+      "step": 5
+    },
+    {
+      "epoch": 0.9195402298850575,
+      "grad_norm": 0.2384282946586609,
+      "learning_rate": 5e-06,
+      "loss": 0.9716,
+      "step": 10
+    },
+    {
+      "epoch": 0.9195402298850575,
+      "step": 10,
+      "total_flos": 1171084304973824.0,
+      "train_loss": 0.0,
+      "train_runtime": 2.6815,
+      "train_samples_per_second": 32.444,
+      "train_steps_per_second": 3.729
+    }
+  ],
+  "logging_steps": 5,
+  "max_steps": 10,
+  "num_input_tokens_seen": 0,
+  "num_train_epochs": 1,
+  "save_steps": 100,
+  "stateful_callbacks": {
+    "TrainerControl": {
+      "args": {
+        "should_epoch_stop": false,
+        "should_evaluate": false,
+        "should_log": false,
+        "should_save": true,
+        "should_training_stop": true
+      },
+      "attributes": {}
+    }
+  },
+  "total_flos": 1171084304973824.0,
+  "train_batch_size": 1,
+  "trial_name": null,
+  "trial_params": null
+}

training_args.bin

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