Na0s
/

Llama-3.1-8B-Pruned-4-Layers_LoRA-PEFT-2.0

@@ -1,144 +1,88 @@
 ---
 library_name: transformers
 pipeline_tag: text-generation
 datasets:
 - meta-math/MetaMathQA
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
 ## Model Details
 ### Model Description
-<!-- Provide a longer summary of what this model is. -->
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-- **Developed by:** [More Information Needed]
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-## Uses
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-## How to Get Started with the Model
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 ## Training Details
-### Training Data
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-#### Preprocessing [optional]
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-#### Training Hyperparameters
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-#### Speeds, Sizes, Times [optional]
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 ## Evaluation
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 ## Environmental Impact
@@ -146,56 +90,5 @@ Use the code below to get started with the model.
 Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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 ---
+base_model: Na0s/Llama-3.1-8B-Pruned-4-Layers_LoRA-PEFT-1.0
 library_name: transformers
 pipeline_tag: text-generation
 datasets:
 - meta-math/MetaMathQA
 ---
+<a href="https://ibb.co/4jPnqYk"><img src="https://i.ibb.co/qRk3SW6/DALL-E-2024-08-08-05-52-48-Craft-an-epic-and-historic-image-for-a-model-card-blending-elements-of-an.webp" alt="DALL-E-2024-08-08-05-52-48-Craft-an-epic-and-historic-image-for-a-model-card-blending-elements-of-an" border="0"></a>
+# Model Card for Na0s/Llama-3.1-8B-Pruned-4-Layers_LoRA-PEFT-2.0
 ## Model Details
 ### Model Description
+- **Finetuned from model:[Na0s/Llama-3.1-8b-Pruned-4-Layers-1.0]**
+<!-- Provide a quick summary of what the model is/does. -->
 ## Training Details
+    model = FastLanguageModel.get_peft_model(
+    model,
+    r = 4,
+    target_modules = ["q_proj", "k_proj", "v_proj", "o_proj",
+                      "gate_proj", "up_proj", "down_proj",],
+    lora_alpha = 4,
+    lora_dropout = 0.05,
+    bias = "none",
+    use_gradient_checkpointing = "unsloth",
+    random_state = 3407,
+    use_rslora = False,
+    loftq_config = None,
+    )
+    from trl import SFTTrainer
+    from transformers import TrainingArguments
+    from unsloth import is_bfloat16_supported
+    trainer = SFTTrainer(
+    model = model,
+    tokenizer = tokenizer,
+    train_dataset = dataset,
+    dataset_text_field = "completion",
+    max_seq_length = max_seq_length,
+    dataset_num_proc = 2,
+    packing = False,
+    args = TrainingArguments(
+        per_device_train_batch_size = 10,
+        gradient_accumulation_steps = 4,
+        warmup_steps = 5,
+        max_steps=5000,
+        learning_rate = 2e-4,
+        fp16 = not is_bfloat16_supported(),
+        bf16 = is_bfloat16_supported(),
+        logging_steps = 1,
+        optim = "adamw_8bit",
+        weight_decay = 0.01,
+        lr_scheduler_type = "cosine",
+        seed = 3407,
+        output_dir = "outputs_4",
+        push_to_hub=True,
+        hub_always_push=True,
+    ),
+    )
+### Training Data
+[meta-math/MetaMathQA]
 ## Evaluation
+MMLU Pro 0-shot: 0.2872
+#### Evaluation Data
 <!-- This should link to a Dataset Card if possible. -->
+[TIGER-AI-Lab/MMLU-Pro]
 ## Environmental Impact
 Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).