johnsnowlabs
/

CodeGemma-2B-dora

@@ -1,199 +1,82 @@
 ---
 library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
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-## Model Details
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-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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 ---
+license: cc-by-nc-4.0
+base_model: johnsnowlabs/CodeGemma-2B-Slerp
+tags:
+- generated_from_trainer
+- instruct
+- finetune
+- chatml
+- gpt4
+- synthetic data
+- distillation
+model-index:
+- name: CodeGemma-2B-Slerp-dora
+  results: []
+datasets:
+- argilla/distilabel-intel-orca-dpo-pairs
+language:
+- en
 library_name: transformers
+pipeline_tag: text-generation
 ---
+# CodeGemma-2B-Slerp-dora
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/660cfe98280a82e38fe4ef49/JrTnaEV4AapbLwx0Cb-Lc.png)
 <!-- Provide a quick summary of what the model is/does. -->
+CodeGemma-2B-Slerp-dora is a DPO fine-tuned of [johnsnowlabs/CodeGemma-2B-Slerp](https://huggingface.co/johnsnowlabs/CodeGemma-2B-Slerp) on [argilla/distilabel-intel-orca-dpo-pairs](https://huggingface.co/datasets/argilla/distilabel-intel-orca-dpo-pairs) preference dataset using DoRA. The model has been trained for 1080 steps. All hyperparams are given below.
+## 🏆 Evaluation results
+### Coming Soom
+## Usage
+```python
+!pip install -qU transformers accelerate
+from transformers import AutoTokenizer
+import transformers
+import torch
+model = "johnsnowlabs/CodeGemma-2B-dora"
+messages = [{"role": "user", "content": "Explain what is Machine learning."}]
+tokenizer = AutoTokenizer.from_pretrained(model)
+prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+pipeline = transformers.pipeline(
+    "text-generation",
+    model=model,
+    torch_dtype=torch.float16,
+    device_map="auto",
+)
+outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
+print(outputs[0]["generated_text"])
+```
+### Training hyperparameters
+The following hyperparameters were used during training:
+- learning_rate: 2e-04
+- train_batch_size: 1
+- gradient_accumulation_steps: 8
+- optimizer: PagedAdamW with 32-bit precision
+- lr_scheduler_type: Cosine
+- lr_scheduler_warmup_steps: 100
+- training_steps: 1080
+### LoRA Config
+- lora_r: 16
+- lora_alpha: 32
+- lora_dropout: 0.05
+- peft_use_dora: true
+### Framework versions
+- Transformers 4.39.0.dev0
+- Peft 0.9.1.dev0
+- Datasets 2.18.0
+- torch 2.2.0
+- accelerate 0.27.2