Instructions to use theprint/phi-3-mini-4k-python with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use theprint/phi-3-mini-4k-python with Transformers:

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

pipe = pipeline("text-generation", model="theprint/phi-3-mini-4k-python")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("theprint/phi-3-mini-4k-python")
model = AutoModelForCausalLM.from_pretrained("theprint/phi-3-mini-4k-python")
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]:]))

llama-cpp-python

How to use theprint/phi-3-mini-4k-python with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="theprint/phi-3-mini-4k-python",
	filename="phi-3-mini-4k-python-unsloth.F16.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use theprint/phi-3-mini-4k-python with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf theprint/phi-3-mini-4k-python:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf theprint/phi-3-mini-4k-python:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf theprint/phi-3-mini-4k-python:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf theprint/phi-3-mini-4k-python:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf theprint/phi-3-mini-4k-python:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf theprint/phi-3-mini-4k-python:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf theprint/phi-3-mini-4k-python:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf theprint/phi-3-mini-4k-python:Q4_K_M

Use Docker

docker model run hf.co/theprint/phi-3-mini-4k-python:Q4_K_M

LM Studio
Jan

vLLM

How to use theprint/phi-3-mini-4k-python with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "theprint/phi-3-mini-4k-python"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "theprint/phi-3-mini-4k-python",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/theprint/phi-3-mini-4k-python:Q4_K_M

SGLang

How to use theprint/phi-3-mini-4k-python 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 "theprint/phi-3-mini-4k-python" \
    --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": "theprint/phi-3-mini-4k-python",
		"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 "theprint/phi-3-mini-4k-python" \
        --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": "theprint/phi-3-mini-4k-python",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use theprint/phi-3-mini-4k-python with Ollama:
```
ollama run hf.co/theprint/phi-3-mini-4k-python:Q4_K_M
```

Unsloth Studio new

How to use theprint/phi-3-mini-4k-python with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for theprint/phi-3-mini-4k-python to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for theprint/phi-3-mini-4k-python to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for theprint/phi-3-mini-4k-python to start chatting

Docker Model Runner
How to use theprint/phi-3-mini-4k-python with Docker Model Runner:
```
docker model run hf.co/theprint/phi-3-mini-4k-python:Q4_K_M
```

Lemonade

How to use theprint/phi-3-mini-4k-python with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull theprint/phi-3-mini-4k-python:Q4_K_M

Run and chat with the model

lemonade run user.phi-3-mini-4k-python-Q4_K_M

List all available models

lemonade list

phi-3-mini-4k-python

File size: 3,842 Bytes

---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- trl
- sft
base_model: unsloth/Phi-3-mini-4k-instruct-bnb-4bit
datasets:
- iamtarun/python_code_instructions_18k_alpaca
- ajibawa-2023/Python-Code-23k-ShareGPT
pipeline_tag: text-generation
model-index:
- name: phi-3-mini-4k-python
  results:
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: IFEval (0-Shot)
      type: HuggingFaceH4/ifeval
      args:
        num_few_shot: 0
    metrics:
    - type: inst_level_strict_acc and prompt_level_strict_acc
      value: 24.09
      name: strict accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=theprint/phi-3-mini-4k-python
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: BBH (3-Shot)
      type: BBH
      args:
        num_few_shot: 3
    metrics:
    - type: acc_norm
      value: 28.45
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=theprint/phi-3-mini-4k-python
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MATH Lvl 5 (4-Shot)
      type: hendrycks/competition_math
      args:
        num_few_shot: 4
    metrics:
    - type: exact_match
      value: 8.46
      name: exact match
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=theprint/phi-3-mini-4k-python
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: GPQA (0-shot)
      type: Idavidrein/gpqa
      args:
        num_few_shot: 0
    metrics:
    - type: acc_norm
      value: 5.48
      name: acc_norm
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=theprint/phi-3-mini-4k-python
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MuSR (0-shot)
      type: TAUR-Lab/MuSR
      args:
        num_few_shot: 0
    metrics:
    - type: acc_norm
      value: 9.22
      name: acc_norm
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=theprint/phi-3-mini-4k-python
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MMLU-PRO (5-shot)
      type: TIGER-Lab/MMLU-Pro
      config: main
      split: test
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 28.63
      name: accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=theprint/phi-3-mini-4k-python
      name: Open LLM Leaderboard
---

# Uploaded  model

- **Developed by:** theprint
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Phi-3-mini-4k-instruct-bnb-4bit

This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.

[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_theprint__phi-3-mini-4k-python)

|      Metric       |Value|
|-------------------|----:|
|Avg.               |17.39|
|IFEval (0-Shot)    |24.09|
|BBH (3-Shot)       |28.45|
|MATH Lvl 5 (4-Shot)| 8.46|
|GPQA (0-shot)      | 5.48|
|MuSR (0-shot)      | 9.22|
|MMLU-PRO (5-shot)  |28.63|