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README.md

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+---
+library_name: transformers
+license: other
+base_model: llava-hf/llava-v1.6-mistral-7b-hf
+tags:
+- llama-factory
+- full
+- generated_from_trainer
+model-index:
+- name: AA_preference_cosi_new_step10_0_30
+  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. -->
+
+# AA_preference_cosi_new_step10_0_30
+
+This model is a fine-tuned version of [llava-hf/llava-v1.6-mistral-7b-hf](https://huggingface.co/llava-hf/llava-v1.6-mistral-7b-hf) on the AA_preference_cosi_new_step10_0_30 dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.5883
+- Rewards/chosen: 1.9964
+- Rewards/rejected: -0.3785
+- Rewards/accuracies: 0.8403
+- Rewards/margins: 2.3750
+- Logps/rejected: -233.2932
+- Logps/chosen: -249.7375
+- Logits/rejected: -2.2233
+- Logits/chosen: -2.2292
+
+## 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: 1e-06
+- train_batch_size: 8
+- eval_batch_size: 8
+- seed: 42
+- distributed_type: multi-GPU
+- num_devices: 8
+- gradient_accumulation_steps: 4
+- total_train_batch_size: 256
+- total_eval_batch_size: 64
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: cosine
+- lr_scheduler_warmup_steps: 10
+- num_epochs: 3.0
+
+### Training results
+
+| Training Loss | Epoch  | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
+|:-------------:|:------:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|
+| 0.2594        | 1.2422 | 50   | 0.5902          | 1.6739         | -0.2014          | 0.7986             | 1.8753          | -231.5221      | -252.9634    | -2.2944         | -2.2956       |
+| 0.1658        | 2.4845 | 100  | 0.5874          | 2.0247         | -0.3441          | 0.8333             | 2.3688          | -232.9490      | -249.4547    | -2.2372         | -2.2420       |
+
+
+### Framework versions
+
+- Transformers 4.45.2
+- Pytorch 2.4.0+cu121
+- Datasets 2.21.0
+- Tokenizers 0.20.3

added_tokens.json

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+{
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+  "<pad>": 32001
+}

all_results.json

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+{
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+    "eval_logits/chosen": -2.2292356491088867,
+    "eval_logits/rejected": -2.2232847213745117,
+    "eval_logps/chosen": -249.73751831054688,
+    "eval_logps/rejected": -233.29318237304688,
+    "eval_loss": 0.5883417725563049,
+    "eval_rewards/accuracies": 0.8402777910232544,
+    "eval_rewards/chosen": 1.9964463710784912,
+    "eval_rewards/margins": 2.374969959259033,
+    "eval_rewards/rejected": -0.3785232603549957,
+    "eval_runtime": 74.456,
+    "eval_samples_per_second": 15.311,
+    "eval_steps_per_second": 0.242,
+    "total_flos": 1414680891359232.0,
+    "train_loss": 0.34148518641789755,
+    "train_runtime": 4236.5791,
+    "train_samples_per_second": 7.263,
+    "train_steps_per_second": 0.028
+}

checkpoint-100/added_tokens.json

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+{
+  "<image>": 32000,
+  "<pad>": 32001
+}

checkpoint-100/config.json

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+{
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+  "architectures": [
+    "LlavaNextForConditionalGeneration"
+  ],
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+  "projector_hidden_act": "gelu",
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+    "model_type": "mistral",
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+    "torch_dtype": "bfloat16",
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+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.45.2",
+  "use_cache": false,
+  "use_image_newline_parameter": true,
+  "vision_config": {
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+    "model_type": "clip_vision_model",
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+    "vocab_size": 32000
+  },
+  "vision_feature_layer": -2,
+  "vision_feature_select_strategy": "default",
+  "vocab_size": 32064
+}

checkpoint-100/generation_config.json

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+  "eos_token_id": 2,
+  "transformers_version": "4.45.2"
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@@ -0,0 +1,52 @@
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special_tokens_map.json

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