Instructions to use vince62s/phi-2-psy with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use vince62s/phi-2-psy with Transformers:

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

pipe = pipeline("text-generation", model="vince62s/phi-2-psy", trust_remote_code=True)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("vince62s/phi-2-psy", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("vince62s/phi-2-psy", trust_remote_code=True)

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use vince62s/phi-2-psy with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "vince62s/phi-2-psy"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "vince62s/phi-2-psy",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/vince62s/phi-2-psy

SGLang

How to use vince62s/phi-2-psy 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 "vince62s/phi-2-psy" \
    --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": "vince62s/phi-2-psy",
		"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 "vince62s/phi-2-psy" \
        --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": "vince62s/phi-2-psy",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use vince62s/phi-2-psy with Docker Model Runner:
```
docker model run hf.co/vince62s/phi-2-psy
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

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)

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	62.80
AI2 Reasoning Challenge (25-Shot)	60.84
HellaSwag (10-Shot)	75.52
MMLU (5-Shot)	57.57
TruthfulQA (0-shot)	48.22
Winogrande (5-shot)	75.45
GSM8k (5-shot)	59.21

Downloads last month: 84

Safetensors

Model size

3B params

Tensor type

BF16

Model tree for vince62s/phi-2-psy

dphn/dolphin-2_6-phi-2

rhysjones/phi-2-orange

Merge model

this model

Quantizations

2 models

Evaluation results

normalized accuracy on AI2 Reasoning Challenge (25-Shot)
test set Open LLM Leaderboard

60.840
normalized accuracy on HellaSwag (10-Shot)
validation set Open LLM Leaderboard

75.520
accuracy on MMLU (5-Shot)
test set Open LLM Leaderboard

57.570
mc2 on TruthfulQA (0-shot)
validation set Open LLM Leaderboard

48.220
accuracy on Winogrande (5-shot)
validation set Open LLM Leaderboard

75.450
accuracy on GSM8k (5-shot)
test set Open LLM Leaderboard

59.210