DhruvParth
/

Mistral-7B-Instruct-v0.2-DPO-v0.1

@@ -1,199 +1,125 @@
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 ---
 library_name: transformers
+license: mit
+datasets:
+- DhruvParth/Mistral-7B-Instruct-v2.0-PairRM-DPO-Dataset
+language:
+- en
 ---
+# Model Card for DhruvParth/Mistral-7B-Instruct-v2.0-PairRM-DPO
+This model is a fine-tuned version of the Mistral-7B model, utilizing Direct Preference Optimization (DPO) to better align the model's responses with human preferences, specifically in a causal language modeling context.
 ## Model Details
 ### Model Description
+- Developed by: Dhruv Parthasarathy
+- Model type: Fine-tuned language model
+- Language(s) (NLP): English
+- License: MIT
+- Finetuned from model: Mistral-7B-Instruct-v2.0
+### Model Sources
+- **Repository:** https://huggingface.co/DhruvParth
+- **Paper:** Direct Preference Optimization (https://arxiv.org/abs/2305.18290)
+- **Demo:** (Will soon be made available)
 ## Uses
+This model is tailored for scenarios requiring alignment with human preferences in automated responses, suitable for applications in personalized chatbots, customer support, and other interactive services.
 ## Training Details
+### Notebook
+The fine-tuning process and the experiments were documented in a Jupyter Notebook, available [here](https://github.com/parthasarathydNU/gen-ai-coursework/blob/main/advanced-llms/direct-preference-optimization/dpomistralfinetuning.ipynb).
+### Training Configuration
+#### LoRA Configuration
+```python
+LoraConfig(
+    r=8,
+    lora_alpha=8,
+    lora_dropout=0.05,
+    bias="none",
+    task_type="CAUSAL_LM",
+    target_modules=['k_proj', 'v_proj', 'q_proj', 'dense']
+)
+```
+#### BitsAndBytes Configuration
+```python
+BitsAndBytesConfig(
+    load_in_4bit=True,
+    llm_int8_threshold=6.0,
+    llm_int8_has_fp16_weight=False,
+    bnb_4bit_compute_dtype=torch.bfloat16,
+    bnb_4bit_use_double_quant=True,
+    bnb_4bit_quant_type="nf4",
+)
+```
+#### Training Device Setup
+```python
+device_map = {"": 0}
+```
+#### Training Arguments
+```python
+DPOConfig(
+    per_device_train_batch_size=2,
+    gradient_accumulation_steps=4,
+    gradient_checkpointing=True,
+    learning_rate=5e-5,
+    lr_scheduler_type="cosine",
+    max_steps=50,
+    save_strategy="no",
+    logging_steps=1,
+    output_dir=new_model,
+    optim="paged_adamw_32bit",
+    warmup_steps=5,
+)
+```
+### DPO Trainer Setup
+```python
+DPOTrainer(
+    model,
+    args=training_args,
+    train_dataset=updated_train_dataset,
+    tokenizer=tokenizer,
+    peft_config=peft_config,
+    beta=0.1,
+    max_prompt_length=512,
+    max_length=1024,
+)
+```
 ## Evaluation
+Details on the model's performance, evaluation protocols, and results will be provided as they become available.
+## Citation
+If you use this model or dataset, please cite it as follows:
 **BibTeX:**
+```bibtex
+@misc{dhruvparth_mistral7b_dpo_2024,
+  author = {Dhruv Parthasarathy},
+  title = {Fine-tuning LLMs with Direct Preference Optimization},
+  year = {2024},
+  publisher = {GitHub},
+  journal = {GitHub repository},
+  url = {https://huggingface.co/DhruvParth/Mistral-7B-Instruct-v2.0-PairRM-DPO}
+}
+```
 **APA:**
+Dhruv Parthasarathy. (2024). Fine-tuning LLMs with Direct Preference Optimization. GitHub repository, https://huggingface.co/DhruvParth/Mistral-7B-Instruct-v2.0-PairRM-DPO
+For any queries or discussions regarding the project, please open an issue in the GitHub repository, post your comment in the community section, reach out to me via LinkedIn (https://www.linkedin.com/in/parthadhruv/) or contact me directly at [email protected].