Instructions to use field2437/phi-2-platypus-Commercial-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use field2437/phi-2-platypus-Commercial-lora with Transformers:

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

pipe = pipeline("text-generation", model="field2437/phi-2-platypus-Commercial-lora", trust_remote_code=True)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("field2437/phi-2-platypus-Commercial-lora", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("field2437/phi-2-platypus-Commercial-lora", trust_remote_code=True)

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use field2437/phi-2-platypus-Commercial-lora with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "field2437/phi-2-platypus-Commercial-lora"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "field2437/phi-2-platypus-Commercial-lora",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/field2437/phi-2-platypus-Commercial-lora

SGLang

How to use field2437/phi-2-platypus-Commercial-lora 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 "field2437/phi-2-platypus-Commercial-lora" \
    --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": "field2437/phi-2-platypus-Commercial-lora",
		"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 "field2437/phi-2-platypus-Commercial-lora" \
        --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": "field2437/phi-2-platypus-Commercial-lora",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use field2437/phi-2-platypus-Commercial-lora with Docker Model Runner:
```
docker model run hf.co/field2437/phi-2-platypus-Commercial-lora
```

phi-2-platypus-Commercial-lora

Model Details

Model Developers

field2437

Base Model

microsoft/phi-2(https://huggingface.co/microsoft/phi-2)

Training Dataset

kyujinpy/Open-platypus-Commercial(https://huggingface.co/datasets/kyujinpy/Open-platypus-Commercial)

Model comparisons1

AI-Harness evaluation; link

Model	Copa	HellaSwag	BoolQ	MMLU
	0-shot	0-shot	0-shot	0-shot
phi-2-platypus-Commercial-lora	0.8900	0.5573	0.8260	0.5513

Sample Code

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

torch.set_default_device("cuda")

model = AutoModelForCausalLM.from_pretrained("field2437/phi-2-platypus-Commercial-lora", torch_dtype="auto", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("field2437/phi-2-platypus-Commercial-lora", trust_remote_code=True)

inputs = tokenizer('''Below is an instruction that describes a task. Write a response that appropriately completes the request.

### Instruction:
Let $f(x)$ be the polynomial \\[f(x)=3x^4+5x^2-9x-2.\\] If $g(x)$ is equal to the polynomial $f(x-1)$, what is the sum of the coefficients of $g$?

### Response:
''', 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: 107

Safetensors

Model size

3B params

Tensor type

F16

field2437
/

phi-2-platypus-Commercial-lora

phi-2-platypus-Commercial-lora

Model Details

Model comparisons1

Sample Code

Dataset used to train field2437/phi-2-platypus-Commercial-lora