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# 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