Model save

README.md

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+---
+library_name: transformers
+license: apache-2.0
+base_model: Qwen/Qwen2.5-14B-Instruct
+tags:
+- trl
+- dpo
+- generated_from_trainer
+model-index:
+- name: lambda-qwen2.5-14b-dpo-test
+  results: []
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# lambda-qwen2.5-14b-dpo-test
+
+This model is a fine-tuned version of [Qwen/Qwen2.5-14B-Instruct](https://huggingface.co/Qwen/Qwen2.5-14B-Instruct) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.4924
+- Rewards/chosen: -2.4470
+- Rewards/rejected: -3.3410
+- Rewards/accuracies: 0.7400
+- Rewards/margins: 0.8939
+- Logps/rejected: -828.8748
+- Logps/chosen: -734.7765
+- Logits/rejected: -1.2644
+- Logits/chosen: -1.2477
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 5e-07
+- train_batch_size: 2
+- eval_batch_size: 2
+- seed: 42
+- distributed_type: multi-GPU
+- num_devices: 8
+- gradient_accumulation_steps: 8
+- total_train_batch_size: 128
+- total_eval_batch_size: 16
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: cosine
+- lr_scheduler_warmup_ratio: 0.1
+- num_epochs: 1
+
+### Training results
+
+| Training Loss | Epoch  | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
+|:-------------:|:------:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|
+| 0.5269        | 0.2094 | 100  | 0.5333          | -1.6756        | -2.3320          | 0.7000             | 0.6564          | -727.9815      | -657.6356    | -1.3952         | -1.3850       |
+| 0.5086        | 0.4187 | 200  | 0.5044          | -2.0906        | -2.9287          | 0.7040             | 0.8381          | -787.6511      | -699.1298    | -1.2939         | -1.2773       |
+| 0.4787        | 0.6281 | 300  | 0.4948          | -2.2927        | -3.1689          | 0.7320             | 0.8762          | -811.6696      | -719.3386    | -1.2846         | -1.2646       |
+| 0.4825        | 0.8375 | 400  | 0.4924          | -2.4470        | -3.3410          | 0.7400             | 0.8939          | -828.8748      | -734.7765    | -1.2644         | -1.2477       |
+
+
+### Framework versions
+
+- Transformers 4.44.2
+- Pytorch 2.4.0+cu121
+- Datasets 2.19.1
+- Tokenizers 0.19.1

added_tokens.json

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all_results.json

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+{
+    "epoch": 0.998691442030882,
+    "total_flos": 0.0,
+    "train_loss": 0.5192520058629898,
+    "train_runtime": 13862.6582,
+    "train_samples": 61134,
+    "train_samples_per_second": 4.41,
+    "train_steps_per_second": 0.034
+}

config.json

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+{
+  "_name_or_path": "Qwen/Qwen2.5-14B-Instruct",
+  "architectures": [
+    "Qwen2ForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
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+  "hidden_act": "silu",
+  "hidden_size": 5120,
+  "initializer_range": 0.02,
+  "intermediate_size": 13824,
+  "max_position_embeddings": 32768,
+  "max_window_layers": 70,
+  "model_type": "qwen2",
+  "num_attention_heads": 40,
+  "num_hidden_layers": 48,
+  "num_key_value_heads": 8,
+  "rms_norm_eps": 1e-06,
+  "rope_theta": 1000000.0,
+  "sliding_window": null,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.44.2",
+  "use_cache": false,
+  "use_sliding_window": false,
+  "vocab_size": 152064
+}

generation_config.json

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+  "top_k": 20,
+  "top_p": 0.8,
+  "transformers_version": "4.44.2"
+}

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runs/Sep20_06-57-19_action-graph-trainer/events.out.tfevents.1726816203.action-graph-trainer.2950951.0

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+size 42423

special_tokens_map.json

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+    "<|im_end|>",
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+}

tokenizer_config.json

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+  "errors": "replace",
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train_results.json

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