Create README.md

README.md

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+---
+# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
+# Doc / guide: https://huggingface.co/docs/hub/model-cards
+{}
+---
+
+# Model Card for Phi 1.5 SlimOrca
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+Phi 1.5 finetuned on SlimOrca-Dedup. This model was trained with the goal of giving Phi 1.5 the ablity to generate the EOS token together with being capable of doing beam search.
+
+## Model Details
+
+## How to Get Started with the Model
+
+```python
+import torch
+import transformers
+
+model = transformers.AutoModelForCausalLM.from_pretrained(
+    "miguelcarv/phi-1_5-slimorca",
+    trust_remote_code=True
+)
+tokenizer = transformers.AutoTokenizer.from_pretrained("microsoft/phi-1_5")
+
+
+SYSTEM_PROMPT = "You are an AI assistant. You will be given a task. You must generate a detailed and long answer."
+input_text = f"""{SYSTEM_PROMPT}
+
+Instruction: Give me the first 5 prime numbers and explain what prime numbers are.
+Output:"""
+
+with torch.no_grad():
+    outputs = model.generate(
+        tokenizer(input_text, return_tensors="pt")['input_ids'],
+        max_length=256,
+        num_beams = 3,
+        eos_token_id = tokenizer.eos_token_id
+    )
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+
+## Training Details
+
+ - Trained for one epoch on SlimOrca-Dedup
+ - Learning rate: 1e-5
+ - Optimizer: AdamW
+ - Effective batch size: 64
+ - Gradient accumulation steps (mini batch size): 16 (4)
+ - Trained with FP32