Create README.md

README.md

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+---
+license: mit
+datasets:
+- HuggingFaceH4/ultrachat_200k
+language:
+- en
+---
+
+## Model Summary
+
+phi2-ultrachat-qlora is a Transformer fine tuned using the ultrachat dataset. 
+
+Our model hasn't been fine-tuned through reinforcement learning from human feedback. The intention behind crafting this open-source model is to provide the research community with a non-restricted small model to explore vital safety challenges, such as reducing toxicity, understanding societal biases, enhancing controllability, and more.
+
+
+### Inference Code:
+
+```python
+import warnings
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+path= f"sandeepsundaram/phi2-ultrachat-qlora"
+tokenizer = AutoTokenizer.from_pretrained(path)
+tokenizer.eos_token_id = model.config.eos_token_id
+tokenizer.pad_token = tokenizer.eos_token
+tokenizer.add_special_tokens({'pad_token': '[PAD]'})
+
+warnings.filterwarnings('ignore')  # Ignore all warnings
+#inputs = tokenizer('Question: why human are cute then human? write in the form of poem. \n Output: ', return_tensors="pt", return_attention_mask=False).to('cuda')
+inputs = tokenizer('''write code for fibonaci series in python.''', return_tensors="pt", return_attention_mask=False).to('cuda')
+generation_params = {
+    'max_length': 512,
+    'do_sample': True,
+    'temperature': .5,
+    'top_p': 0.9,
+    'top_k': 50
+}
+
+outputs = model.generate(**inputs, **generation_params)
+decoded_outputs = tokenizer.batch_decode(outputs)
+
+for text in decoded_outputs:
+    text = text.replace('\\n', '\n')
+    print(text)
+    print("\n\n")
+```