Instructions to use afrideva/phi-2-chat-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use afrideva/phi-2-chat-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="afrideva/phi-2-chat-GGUF",
	filename="phi-2-chat.fp16.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 afrideva/phi-2-chat-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf afrideva/phi-2-chat-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf afrideva/phi-2-chat-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 afrideva/phi-2-chat-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf afrideva/phi-2-chat-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 afrideva/phi-2-chat-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf afrideva/phi-2-chat-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 afrideva/phi-2-chat-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf afrideva/phi-2-chat-GGUF:Q4_K_M

Use Docker

docker model run hf.co/afrideva/phi-2-chat-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use afrideva/phi-2-chat-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "afrideva/phi-2-chat-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "afrideva/phi-2-chat-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/afrideva/phi-2-chat-GGUF:Q4_K_M

Ollama
How to use afrideva/phi-2-chat-GGUF with Ollama:
```
ollama run hf.co/afrideva/phi-2-chat-GGUF:Q4_K_M
```

Unsloth Studio

How to use afrideva/phi-2-chat-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 afrideva/phi-2-chat-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 afrideva/phi-2-chat-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for afrideva/phi-2-chat-GGUF to start chatting

Docker Model Runner
How to use afrideva/phi-2-chat-GGUF with Docker Model Runner:
```
docker model run hf.co/afrideva/phi-2-chat-GGUF:Q4_K_M
```

Lemonade

How to use afrideva/phi-2-chat-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull afrideva/phi-2-chat-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.phi-2-chat-GGUF-Q4_K_M

List all available models

lemonade list

malhajar/phi-2-chat-GGUF

Quantized GGUF model files for phi-2-chat from malhajar

Name	Quant method	Size
phi-2-chat.fp16.gguf	fp16	5.56 GB
phi-2-chat.q2_k.gguf	q2_k	1.17 GB
phi-2-chat.q3_k_m.gguf	q3_k_m	1.48 GB
phi-2-chat.q4_k_m.gguf	q4_k_m	1.79 GB
phi-2-chat.q5_k_m.gguf	q5_k_m	2.07 GB
phi-2-chat.q6_k.gguf	q6_k	2.29 GB
phi-2-chat.q8_0.gguf	q8_0	2.96 GB

Original Model Card:

Model Card for Model ID

malhajar/phi-2-chat is a finetuned version of phi-2 using SFT Training. This model can answer information in a chat format as it is finetuned specifically on instructions specifically alpaca-cleaned

Model Description

Developed by: Mohamad Alhajar
Language(s) (NLP): Turkish
Finetuned from model: microsoft/phi-2

Prompt Template

### Instruction:

<prompt> (without the <>)

### Response:

How to Get Started with the Model

Use the code sample provided in the original post to interact with the model.

from transformers import AutoTokenizer,AutoModelForCausalLM
 
model_id = "malhajar/phi-2-chat"
model = AutoModelForCausalLM.from_pretrained(model_name_or_path,
                                             device_map="auto",
                                             torch_dtype=torch.float16,
                                             revision="main")

tokenizer = AutoTokenizer.from_pretrained(model_id)

question: "Türkiyenin en büyük şehir nedir?"
# For generating a response
prompt = '''
### Instruction:  {question} ### Response:
'''
input_ids = tokenizer(prompt, return_tensors="pt").input_ids
output = model.generate(inputs=input_ids,max_new_tokens=512,pad_token_id=tokenizer.eos_token_id,top_k=50, do_sample=True,repetition_penalty=1.3
        top_p=0.95,trust_remote_code=True,)
response = tokenizer.decode(output[0])

print(response)

Downloads last month: 80

GGUF

Model size

3B params

Architecture

phi2

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

View +1 variant

Model tree for afrideva/phi-2-chat-GGUF

Base model

malhajar/phi-2-chat

Quantized

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this model

afrideva
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phi-2-chat-GGUF