Create README.md

README.md

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+---
+base_model: Haleshot/Mathmate-7B-DELLA-ORPO
+tags:
+- finetuned
+- orpo
+- everyday-conversations
+datasets:
+- HuggingFaceTB/everyday-conversations-llama3.1-2k
+license: apache-2.0
+language:
+- en
+library_name: transformers
+pipeline_tag: text-generation
+---
+
+# Mathmate-7B-DELLA-ORPO-D
+
+Mathmate-7B-DELLA-ORPO-D is a finetuned version of [Haleshot/Mathmate-7B-DELLA-ORPO](https://huggingface.co/Haleshot/Mathmate-7B-DELLA-ORPO) using the ORPO method, combined with a LoRA adapter trained on everyday conversations.
+
+## Model Details
+
+- **Base Model:** [Haleshot/Mathmate-7B-DELLA-ORPO](https://huggingface.co/Haleshot/Mathmate-7B-DELLA-ORPO)
+- **Training Dataset:** [HuggingFaceTB/everyday-conversations-llama3.1-2k](https://huggingface.co/datasets/HuggingFaceTB/everyday-conversations-llama3.1-2k)
+
+## Dataset
+
+The model incorporates training on the [HuggingFaceTB/everyday-conversations-llama3.1-2k](https://huggingface.co/datasets/HuggingFaceTB/everyday-conversations-llama3.1-2k) dataset, which focuses on everyday conversations and small talk.
+
+## Usage
+
+Here's an example of how to use the model:
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+model_name = "Haleshot/Mathmate-7B-DELLA-ORPO-ORPO-D"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16, device_map="auto")
+
+def generate_response(prompt, max_length=512):
+    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+    outputs = model.generate(**inputs, max_length=max_length, num_return_sequences=1, do_sample=True, temperature=0.7)
+    return tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+prompt = "Let's have a casual conversation about weekend plans."
+response = generate_response(prompt)
+print(response)
+```
+
+## Acknowledgements
+
+Thanks to the HuggingFaceTB team for providing the everyday conversations dataset used in this finetuning process.