README.md

---
license: mit
datasets:
- pankajmathur/orca_mini_v1_dataset
- pankajmathur/orca_mini_v8_sharegpt_format
language:
- en
base_model:
- microsoft/phi-4
library_name: transformers
---

# Model Name: orca_mini_phi-4

**orca_mini_phi-4 is trained with various SFT Datasets on [microsoft/phi-4](https://huggingface.co/microsoft/phi-4) using Llama's architecture.**

<img src="https://huggingface.co/pankajmathur/orca_mini_v5_8b/resolve/main/orca_minis_small.jpeg" width="auto" />

<strong>
"Obsessed with GenAI's potential? So am I ! Let's create together 🚀 <a href="https://www.linkedin.com/in/pankajam" target="_blank">https://www.linkedin.com/in/pankajam</a>"
</strong>

<br>

### NOTICE
By providing proper credit and attribution, you are granted permission to use this model as a foundational base for further Full fine tuning, DPO, PPO or ORPO tuning and any kind of Merges. 
I actively encourage users to customize and enhance the model according to their specific needs, as this version is designed to be a comprehensive general model. 
Dive in and innovate!


### Example Usage

**Use this model for Free on Google Colab with T4 GPU :)**

<a target="_blank" href="https://colab.research.google.com/#fileId=https://huggingface.co/pankajmathur/orca_mini_v9_2_14B-Instruct/blob/main/Orca_Mini_Chatbot_14B.ipynb">
  <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
</a>

### Example Usage on Your Personal Computer

Download GGUF version here and Follow Ollama instructions:
coming soon....

Below shows a code example on how to use this model in default half precision (bfloat16) format

```python
import torch
from transformers import pipeline

model_slug = "pankajmathur/orca_mini_phi-4"
pipeline = pipeline(
    "text-generation",
    model=model_slug,
    device_map="auto",
)
messages = [
    {"role": "system", "content": "You are Orca Mini, a helpful AI assistant."},
    {"role": "user", "content": "Hello Orca Mini, what can you do for me?"}
]
outputs = pipeline(messages, max_new_tokens=128, do_sample=True, temperature=0.01, top_k=100, top_p=0.95)
print(outputs[0]["generated_text"][-1])
```

Below shows a code example on how to use this model in 4-bit format via bitsandbytes library

```python
import torch
from transformers import BitsAndBytesConfig, pipeline

model_slug = "pankajmathur/orca_mini_phi-4"
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype="float16",
    bnb_4bit_use_double_quant=True,
)
pipeline = pipeline(
    "text-generation",
    model=model_slug,
    model_kwargs={"quantization_config": quantization_config},
    device_map="auto",
)
messages = [
    {"role": "system", "content": "You are Orca Mini, a helpful AI assistant."},
    {"role": "user", "content": "Hello Orca Mini, what can you do for me?"}
]
outputs = pipeline(messages, max_new_tokens=128, do_sample=True, temperature=0.01, top_k=100, top_p=0.95)
print(outputs[0]["generated_text"][-1])

```

Below shows a code example on how to use this model in 8-bit format via bitsandbytes library

```python
import torch
from transformers import BitsAndBytesConfig, pipeline

model_slug = "pankajmathur/orca_mini_phi-4"
quantization_config = BitsAndBytesConfig(
    load_in_8bit=True
)
pipeline = pipeline(
    "text-generation",
    model=model_slug,
    model_kwargs={"quantization_config": quantization_config},
    device_map="auto",
)
messages = [
    {"role": "system", "content": "You are Orca Mini, a helpful AI assistant."},
    {"role": "user", "content": "Hello Orca Mini, what can you do for me?"}
]
outputs = pipeline(messages, max_new_tokens=128, do_sample=True, temperature=0.01, top_k=100, top_p=0.95)
print(outputs[0]["generated_text"][-1])

```


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