Instructions to use pankajmathur/orca_mini_phi-4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use pankajmathur/orca_mini_phi-4 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="pankajmathur/orca_mini_phi-4")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("pankajmathur/orca_mini_phi-4")
model = AutoModelForCausalLM.from_pretrained("pankajmathur/orca_mini_phi-4")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use pankajmathur/orca_mini_phi-4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "pankajmathur/orca_mini_phi-4"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "pankajmathur/orca_mini_phi-4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/pankajmathur/orca_mini_phi-4

SGLang

How to use pankajmathur/orca_mini_phi-4 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "pankajmathur/orca_mini_phi-4" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "pankajmathur/orca_mini_phi-4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "pankajmathur/orca_mini_phi-4" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "pankajmathur/orca_mini_phi-4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use pankajmathur/orca_mini_phi-4 with Docker Model Runner:
```
docker model run hf.co/pankajmathur/orca_mini_phi-4
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Model Name: orca_mini_phi-4

orca_mini_phi-4 is trained with various SFT Datasets on microsoft/phi-4 using Llama's architecture.

"Obsessed with Open Source GenAI's potential? So am I ! Let's Contribute together 🚀 https://www.linkedin.com/in/pankajam"

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 :)

Example Usage on Your Personal Computer

Download GGUF version here and Follow Ollama instructions: https://huggingface.co/pankajmathur/orca_mini_phi-4-GGUF

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

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

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

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])

Downloads last month: 866

Safetensors

Model size

15B params

Tensor type

BF16

Model tree for pankajmathur/orca_mini_phi-4

Base model

microsoft/phi-4

Finetuned

(96)

this model

Merges

1 model

Quantizations

5 models

pankajmathur
/

orca_mini_phi-4

Model Name: orca_mini_phi-4

NOTICE

Example Usage

Example Usage on Your Personal Computer

Model tree for pankajmathur/orca_mini_phi-4

Datasets used to train pankajmathur/orca_mini_phi-4

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