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

Libraries

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

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="afrideva/phi-2-psy-GGUF",
	filename="phi-2-psy.fp16.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use afrideva/phi-2-psy-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-psy-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf afrideva/phi-2-psy-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-psy-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf afrideva/phi-2-psy-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-psy-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf afrideva/phi-2-psy-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-psy-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf afrideva/phi-2-psy-GGUF:Q4_K_M

Use Docker

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

LM Studio
Jan

vLLM

How to use afrideva/phi-2-psy-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-psy-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-psy-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

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

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

Unsloth Studio new

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

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

Lemonade

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

Pull the model

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

Run and chat with the model

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

List all available models

lemonade list

vince62s/phi-2-psy-GGUF

Quantized GGUF model files for phi-2-psy from vince62s

Name	Quant method	Size
phi-2-psy.fp16.gguf	fp16	5.56 GB
phi-2-psy.q2_k.gguf	q2_k	1.11 GB
phi-2-psy.q3_k_m.gguf	q3_k_m	1.43 GB
phi-2-psy.q4_k_m.gguf	q4_k_m	1.74 GB
phi-2-psy.q5_k_m.gguf	q5_k_m	2.00 GB
phi-2-psy.q6_k.gguf	q6_k	2.29 GB
phi-2-psy.q8_0.gguf	q8_0	2.96 GB

Original Model Card:

Phi-2-psy

Phi-2-psy is a merge of the following models:

🏆 Evaluation

The evaluation was performed using LLM AutoEval on Nous suite.

Model	AGIEval	GPT4All	TruthfulQA	Bigbench	Average
phi-2-psy	34.4	71.4	48.2	38.1	48.02
phixtral-2x2_8	34.1	70.4	48.8	37.8	47.78
dolphin-2_6-phi-2	33.1	69.9	47.4	37.2	46.89
phi-2-orange	33.4	71.3	49.9	37.3	47.97
phi-2	28.0	70.8	44.4	35.2	44.61

🧩 Configuration

slices:
  - sources:
      - model: rhysjones/phi-2-orange
        layer_range: [0, 32]
      - model: cognitivecomputations/dolphin-2_6-phi-2
        layer_range: [0, 32]
merge_method: slerp
base_model: rhysjones/phi-2-orange
parameters:
  t:
    - filter: self_attn
      value: [0, 0.5, 0.3, 0.7, 1]
    - filter: mlp
      value: [1, 0.5, 0.7, 0.3, 0]
    - value: 0.5
dtype: bfloat16

💻 Usage

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
torch.set_default_device("cuda")
model = AutoModelForCausalLM.from_pretrained("vince62s/phi-2-psy", torch_dtype="auto", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("vince62s/phi-2-psy", trust_remote_code=True)
inputs = tokenizer('''def print_prime(n):
   """
   Print all primes between 1 and n
   """''', return_tensors="pt", return_attention_mask=False)
outputs = model.generate(**inputs, max_length=200)
text = tokenizer.batch_decode(outputs)[0]
print(text)

Downloads last month: 61

GGUF

Model size

3B params

Architecture

phi2

Hardware compatibility

2-bit

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View +1 variant

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

Base model

vince62s/phi-2-psy

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