Text Generation
Transformers
Safetensors
phi
mergekit
Merge
custom_code
text-generation-inference
4-bit precision
bitsandbytes
Instructions to use Shaleen123/phi-2-merge with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Shaleen123/phi-2-merge with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Shaleen123/phi-2-merge", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Shaleen123/phi-2-merge", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("Shaleen123/phi-2-merge", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Shaleen123/phi-2-merge with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Shaleen123/phi-2-merge" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Shaleen123/phi-2-merge", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Shaleen123/phi-2-merge
- SGLang
How to use Shaleen123/phi-2-merge 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 "Shaleen123/phi-2-merge" \ --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": "Shaleen123/phi-2-merge", "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 "Shaleen123/phi-2-merge" \ --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": "Shaleen123/phi-2-merge", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Shaleen123/phi-2-merge with Docker Model Runner:
docker model run hf.co/Shaleen123/phi-2-merge
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a164e13 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 | import pytest
from mergekit.common import ModelPath, ModelReference
class TestModelReference:
def test_parse_simple(self):
text = "hf_user/model"
mr = ModelReference.parse(text)
assert mr.model == ModelPath(path="hf_user/model", revision=None)
assert mr.lora is None
assert str(mr) == text
def test_parse_lora(self):
text = "hf_user/model+hf_user/lora"
mr = ModelReference.parse(text)
assert mr.model == ModelPath(path="hf_user/model", revision=None)
assert mr.lora == ModelPath(path="hf_user/lora", revision=None)
assert str(mr) == text
def test_parse_revision(self):
text = "hf_user/model@v0.0.1"
mr = ModelReference.parse(text)
assert mr.model == ModelPath(path="hf_user/model", revision="v0.0.1")
assert mr.lora is None
assert str(mr) == text
def test_parse_lora_plus_revision(self):
text = "hf_user/model@v0.0.1+hf_user/lora@main"
mr = ModelReference.parse(text)
assert mr.model == ModelPath(path="hf_user/model", revision="v0.0.1")
assert mr.lora == ModelPath(path="hf_user/lora", revision="main")
assert str(mr) == text
def test_parse_bad(self):
with pytest.raises(RuntimeError):
ModelReference.parse("@@@@@")
with pytest.raises(RuntimeError):
ModelReference.parse("a+b+c")
with pytest.raises(RuntimeError):
ModelReference.parse("a+b+c@d+e@f@g")
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