Instructions to use miguelcarv/phi-1_5-slimorca with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use miguelcarv/phi-1_5-slimorca with Transformers:

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

pipe = pipeline("text-generation", model="miguelcarv/phi-1_5-slimorca", trust_remote_code=True)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("miguelcarv/phi-1_5-slimorca", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("miguelcarv/phi-1_5-slimorca", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use miguelcarv/phi-1_5-slimorca with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "miguelcarv/phi-1_5-slimorca"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "miguelcarv/phi-1_5-slimorca",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/miguelcarv/phi-1_5-slimorca

SGLang

How to use miguelcarv/phi-1_5-slimorca 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 "miguelcarv/phi-1_5-slimorca" \
    --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": "miguelcarv/phi-1_5-slimorca",
		"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 "miguelcarv/phi-1_5-slimorca" \
        --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": "miguelcarv/phi-1_5-slimorca",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use miguelcarv/phi-1_5-slimorca with Docker Model Runner:
```
docker model run hf.co/miguelcarv/phi-1_5-slimorca
```

Model Card for Phi 1.5 SlimOrca

Phi 1.5 finetuned on SlimOrca-Dedup. This model was trained with the goal of giving Phi 1.5 the ablity to generate the EOS token together with being capable of doing beam search. It can also follow custom system prompts as shown in the example below.

Model Details

How to Get Started with the Model

import torch
import transformers

model = transformers.AutoModelForCausalLM.from_pretrained(
    "miguelcarv/phi-1_5-slimorca",
    trust_remote_code=True
)
tokenizer = transformers.AutoTokenizer.from_pretrained("microsoft/phi-1_5")


SYSTEM_PROMPT = "You are an AI assistant. You will be given a task. You must generate a detailed and long answer."
input_text = f"""{SYSTEM_PROMPT}

Instruction: Give me the first 5 prime numbers and explain what prime numbers are.
Output:"""

with torch.no_grad():
    outputs = model.generate(
        tokenizer(input_text, return_tensors="pt")['input_ids'],
        max_length=1024,
        num_beams = 3,
        eos_token_id = tokenizer.eos_token_id
    )
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Training Details

Trained for one epoch on SlimOrca-Dedup
Learning rate: 2e-5
Cosine learning rate decay
Optimizer: AdamW
Batch size: 256
Trained with FP32

Downloads last month: 37

Safetensors

Model size

1B params

Tensor type

F32