iamtarun/python_code_instructions_18k_alpaca
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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]:]))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?"
}
]
)How to use theprint/phi-3-mini-4k-python with llama.cpp:
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
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
# 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
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
docker model run hf.co/theprint/phi-3-mini-4k-python:Q4_K_M
How to use theprint/phi-3-mini-4k-python with vLLM:
# 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?"
}
]
}'docker model run hf.co/theprint/phi-3-mini-4k-python:Q4_K_M
How to use theprint/phi-3-mini-4k-python with SGLang:
# 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?"
}
]
}'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?"
}
]
}'How to use theprint/phi-3-mini-4k-python with Ollama:
ollama run hf.co/theprint/phi-3-mini-4k-python:Q4_K_M
How to use theprint/phi-3-mini-4k-python with Unsloth Studio:
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
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
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for theprint/phi-3-mini-4k-python to start chatting
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
How to use theprint/phi-3-mini-4k-python with Lemonade:
# Download Lemonade from https://lemonade-server.ai/ lemonade pull theprint/phi-3-mini-4k-python:Q4_K_M
lemonade run user.phi-3-mini-4k-python-Q4_K_M
lemonade list
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf theprint/phi-3-mini-4k-python:# Run inference directly in the terminal:
llama-cli -hf theprint/phi-3-mini-4k-python:# 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:# Run inference directly in the terminal:
./llama-cli -hf theprint/phi-3-mini-4k-python: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:# Run inference directly in the terminal:
./build/bin/llama-cli -hf theprint/phi-3-mini-4k-python:docker model run hf.co/theprint/phi-3-mini-4k-python:This mistral model was trained 2x faster with Unsloth and Huggingface's TRL library.
Detailed results can be found here
| 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 |
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Install from brew
# Start a local OpenAI-compatible server with a web UI: llama-server -hf theprint/phi-3-mini-4k-python:# Run inference directly in the terminal: llama-cli -hf theprint/phi-3-mini-4k-python: