Instructions to use ayoubkirouane/Phi-2.7B_MERGED with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ayoubkirouane/Phi-2.7B_MERGED with Transformers:

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

pipe = pipeline("text-generation", model="ayoubkirouane/Phi-2.7B_MERGED", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("ayoubkirouane/Phi-2.7B_MERGED", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use ayoubkirouane/Phi-2.7B_MERGED with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ayoubkirouane/Phi-2.7B_MERGED"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ayoubkirouane/Phi-2.7B_MERGED",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ayoubkirouane/Phi-2.7B_MERGED

SGLang

How to use ayoubkirouane/Phi-2.7B_MERGED 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 "ayoubkirouane/Phi-2.7B_MERGED" \
    --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": "ayoubkirouane/Phi-2.7B_MERGED",
		"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 "ayoubkirouane/Phi-2.7B_MERGED" \
        --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": "ayoubkirouane/Phi-2.7B_MERGED",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ayoubkirouane/Phi-2.7B_MERGED with Docker Model Runner:
```
docker model run hf.co/ayoubkirouane/Phi-2.7B_MERGED
```

Merge Method

This model was merged using the SLERP merge method.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:


slices:
  - sources:
      - model: rhysjones/phi-2-orange
        layer_range: [0, 32]
      - model: Yhyu13/phi-2-sft-dpo-gpt4_en-ep1
        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("ayoubkirouane/phi-2_MERGED", torch_dtype="auto", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("ayoubkirouane/phi-2_MERGED", trust_remote_code=True)
inputs = tokenizer('What Machine Learning ? ', return_tensors="pt", return_attention_mask=False)
outputs = model.generate(**inputs, max_length=50)
text = tokenizer.batch_decode(outputs)[0]
print(text)

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Safetensors

Model size

3B params

Tensor type

BF16

Model tree for ayoubkirouane/Phi-2.7B_MERGED

Yhyu13/phi-2-sft-dpo-gpt4_en-ep1

rhysjones/phi-2-orange

Merge model

this model