Instructions to use Grogros/phi2-Instruct-reg01-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Grogros/phi2-Instruct-reg01-2 with Transformers:

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

pipe = pipeline("text-generation", model="Grogros/phi2-Instruct-reg01-2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Grogros/phi2-Instruct-reg01-2")
model = AutoModelForCausalLM.from_pretrained("Grogros/phi2-Instruct-reg01-2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Grogros/phi2-Instruct-reg01-2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Grogros/phi2-Instruct-reg01-2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Grogros/phi2-Instruct-reg01-2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Grogros/phi2-Instruct-reg01-2

SGLang

How to use Grogros/phi2-Instruct-reg01-2 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 "Grogros/phi2-Instruct-reg01-2" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Grogros/phi2-Instruct-reg01-2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "Grogros/phi2-Instruct-reg01-2" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Grogros/phi2-Instruct-reg01-2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Grogros/phi2-Instruct-reg01-2 with Docker Model Runner:
```
docker model run hf.co/Grogros/phi2-Instruct-reg01-2
```

Grogros commited on Nov 19, 2025

Commit

b2853d2

verified ·

1 Parent(s): 8570935

Model save

Browse files

Files changed (1) hide show

README.md +55 -0

README.md ADDED Viewed

	@@ -0,0 +1,55 @@

+---
+library_name: transformers
+license: mit
+base_model: microsoft/phi-2
+tags:
+- generated_from_trainer
+model-index:
+- name: phi2-Instruct-reg01-2
+  results: []
+---
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+# phi2-Instruct-reg01-2
+This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) on the None dataset.
+## Model description
+More information needed
+## Intended uses & limitations
+More information needed
+## Training and evaluation data
+More information needed
+## Training procedure
+### Training hyperparameters
+The following hyperparameters were used during training:
+- learning_rate: 2e-05
+- train_batch_size: 32
+- eval_batch_size: 8
+- seed: 42
+- optimizer: Use OptimizerNames.ADAFACTOR and the args are:
+No additional optimizer arguments
+- lr_scheduler_type: cosine
+- lr_scheduler_warmup_ratio: 0.1
+- training_steps: 2000
+### Training results
+### Framework versions
+- Transformers 4.57.1
+- Pytorch 2.9.1+cu129
+- Datasets 3.6.0
+- Tokenizers 0.22.1