llama31_help_rm_full

README.md

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+---
+license: llama3.1
+base_model: meta-llama/Llama-3.1-8B-Instruct
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+model-index:
+- name: llama3_rm
+  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. -->
+
+# llama3_rm
+
+This model is a fine-tuned version of [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.3581
+- Accuracy: 0.8443
+
+## 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: 2e-06
+- train_batch_size: 4
+- eval_batch_size: 4
+- seed: 42
+- distributed_type: multi-GPU
+- num_devices: 8
+- gradient_accumulation_steps: 8
+- total_train_batch_size: 256
+- total_eval_batch_size: 32
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: cosine
+- lr_scheduler_warmup_ratio: 0.03
+- num_epochs: 1
+
+### Training results
+
+| Training Loss | Epoch  | Step | Validation Loss | Accuracy |
+|:-------------:|:------:|:----:|:---------------:|:--------:|
+| 0.8063        | 0.0705 | 10   | 0.6141          | 0.7339   |
+| 0.5355        | 0.1410 | 20   | 0.5117          | 0.7604   |
+| 0.4811        | 0.2115 | 30   | 0.4766          | 0.7719   |
+| 0.4922        | 0.2819 | 40   | 0.4512          | 0.7792   |
+| 0.4919        | 0.3524 | 50   | 0.4428          | 0.8      |
+| 0.476         | 0.4229 | 60   | 0.4174          | 0.8083   |
+| 0.3965        | 0.4934 | 70   | 0.4047          | 0.8161   |
+| 0.3523        | 0.5639 | 80   | 0.3918          | 0.8224   |
+| 0.455         | 0.6344 | 90   | 0.3794          | 0.8344   |
+| 0.4096        | 0.7048 | 100  | 0.3682          | 0.8396   |
+| 0.3726        | 0.7753 | 110  | 0.3636          | 0.8417   |
+| 0.3802        | 0.8458 | 120  | 0.3600          | 0.8448   |
+| 0.3908        | 0.9163 | 130  | 0.3585          | 0.8427   |
+| 0.4002        | 0.9868 | 140  | 0.3581          | 0.8443   |
+
+
+### Framework versions
+
+- Transformers 4.43.4
+- Pytorch 2.1.2+cu121
+- Datasets 4.4.1
+- Tokenizers 0.19.1

config.json

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+{
+  "_name_or_path": "meta-llama/Llama-3.1-8B-Instruct",
+  "architectures": [
+    "LlamaForSequenceClassification"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 128000,
+  "eos_token_id": [
+    128001,
+    128008,
+    128009
+  ],
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "id2label": {
+    "0": "LABEL_0"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 14336,
+  "label2id": {
+    "LABEL_0": 0
+  },
+  "max_position_embeddings": 131072,
+  "mlp_bias": false,
+  "model_type": "llama",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 8,
+  "pad_token_id": 128256,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": {
+    "factor": 8.0,
+    "high_freq_factor": 4.0,
+    "low_freq_factor": 1.0,
+    "original_max_position_embeddings": 8192,
+    "rope_type": "llama3"
+  },
+  "rope_theta": 500000.0,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.43.4",
+  "use_cache": false,
+  "vocab_size": 128257
+}

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