Instructions to use miguelcarv/phi-1_5-slimorca with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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)# pip install -U transformers accelerate # 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
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# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
# Doc / guide: https://huggingface.co/docs/hub/model-cards
{}
---
# Model Card for Phi 1.5 SlimOrca
<!-- Provide a quick summary of what the model is/does. -->
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
```python
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
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