Instructions to use TKDKid1000/phi-1_5-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TKDKid1000/phi-1_5-GGUF with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="TKDKid1000/phi-1_5-GGUF", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("TKDKid1000/phi-1_5-GGUF", trust_remote_code=True, dtype="auto") - llama-cpp-python
How to use TKDKid1000/phi-1_5-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="TKDKid1000/phi-1_5-GGUF", filename="phi-1_5-Q2_K.gguf", )
output = llm( "Once upon a time,", max_tokens=512, echo=True ) print(output)
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use TKDKid1000/phi-1_5-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf TKDKid1000/phi-1_5-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf TKDKid1000/phi-1_5-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf TKDKid1000/phi-1_5-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf TKDKid1000/phi-1_5-GGUF: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 TKDKid1000/phi-1_5-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf TKDKid1000/phi-1_5-GGUF: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 TKDKid1000/phi-1_5-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf TKDKid1000/phi-1_5-GGUF:Q4_K_M
Use Docker
docker model run hf.co/TKDKid1000/phi-1_5-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use TKDKid1000/phi-1_5-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "TKDKid1000/phi-1_5-GGUF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "TKDKid1000/phi-1_5-GGUF", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/TKDKid1000/phi-1_5-GGUF:Q4_K_M
- SGLang
How to use TKDKid1000/phi-1_5-GGUF 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 "TKDKid1000/phi-1_5-GGUF" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "TKDKid1000/phi-1_5-GGUF", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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 "TKDKid1000/phi-1_5-GGUF" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "TKDKid1000/phi-1_5-GGUF", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Ollama
How to use TKDKid1000/phi-1_5-GGUF with Ollama:
ollama run hf.co/TKDKid1000/phi-1_5-GGUF:Q4_K_M
- Unsloth Studio
How to use TKDKid1000/phi-1_5-GGUF 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 TKDKid1000/phi-1_5-GGUF 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 TKDKid1000/phi-1_5-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for TKDKid1000/phi-1_5-GGUF to start chatting
- Docker Model Runner
How to use TKDKid1000/phi-1_5-GGUF with Docker Model Runner:
docker model run hf.co/TKDKid1000/phi-1_5-GGUF:Q4_K_M
- Lemonade
How to use TKDKid1000/phi-1_5-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull TKDKid1000/phi-1_5-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.phi-1_5-GGUF-Q4_K_M
List all available models
lemonade list
| # Copyright (c) Microsoft Corporation. | |
| # Licensed under the MIT license. | |
| import math | |
| from typing import Optional | |
| from transformers import PretrainedConfig | |
| class PhiConfig(PretrainedConfig): | |
| """Phi configuration.""" | |
| model_type = "phi-msft" | |
| attribute_map = { | |
| "max_position_embeddings": "n_positions", | |
| "hidden_size": "n_embd", | |
| "num_attention_heads": "n_head", | |
| "num_hidden_layers": "n_layer", | |
| } | |
| def __init__( | |
| self, | |
| vocab_size: int = 50304, | |
| n_positions: int = 2048, | |
| n_embd: int = 1024, | |
| n_layer: int = 20, | |
| n_inner: Optional[int] = None, | |
| n_head: int = 16, | |
| n_head_kv: Optional[int] = None, | |
| rotary_dim: Optional[int] = 32, | |
| activation_function: Optional[str] = "gelu_new", | |
| flash_attn: bool = False, | |
| flash_rotary: bool = False, | |
| fused_dense: bool = False, | |
| attn_pdrop: float = 0.0, | |
| embd_pdrop: float = 0.0, | |
| resid_pdrop: float = 0.0, | |
| layer_norm_epsilon: float = 1e-5, | |
| initializer_range: float = 0.02, | |
| tie_word_embeddings: bool = False, | |
| pad_vocab_size_multiple: int = 64, | |
| **kwargs | |
| ) -> None: | |
| self.vocab_size = int(math.ceil(vocab_size / pad_vocab_size_multiple) * pad_vocab_size_multiple) | |
| self.n_positions = n_positions | |
| self.n_embd = n_embd | |
| self.n_layer = n_layer | |
| self.n_inner = n_inner | |
| self.n_head = n_head | |
| self.n_head_kv = n_head_kv | |
| self.rotary_dim = min(rotary_dim, n_embd // n_head) | |
| self.activation_function = activation_function | |
| self.flash_attn = flash_attn | |
| self.flash_rotary = flash_rotary | |
| self.fused_dense = fused_dense | |
| self.attn_pdrop = attn_pdrop | |
| self.embd_pdrop = embd_pdrop | |
| self.resid_pdrop = resid_pdrop | |
| self.layer_norm_epsilon = layer_norm_epsilon | |
| self.initializer_range = initializer_range | |
| super().__init__(tie_word_embeddings=tie_word_embeddings, **kwargs) | |