Instructions to use SK0988/phi3-hr-assistant with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SK0988/phi3-hr-assistant with Transformers:

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

pipe = pipeline("text-generation", model="SK0988/phi3-hr-assistant", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("SK0988/phi3-hr-assistant", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("SK0988/phi3-hr-assistant", trust_remote_code=True, device_map="auto")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use SK0988/phi3-hr-assistant with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SK0988/phi3-hr-assistant"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SK0988/phi3-hr-assistant",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/SK0988/phi3-hr-assistant

SGLang

How to use SK0988/phi3-hr-assistant 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 "SK0988/phi3-hr-assistant" \
    --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": "SK0988/phi3-hr-assistant",
		"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 "SK0988/phi3-hr-assistant" \
        --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": "SK0988/phi3-hr-assistant",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use SK0988/phi3-hr-assistant with Docker Model Runner:
```
docker model run hf.co/SK0988/phi3-hr-assistant
```

SK0988 commited on Dec 18, 2025

Commit

c34b4fd

verified ·

1 Parent(s): d281c57

Update README.md

Browse files

Files changed (1) hide show

README.md +5 -3

README.md CHANGED Viewed

@@ -1,5 +1,8 @@
 ---
 base_model: microsoft/Phi-3-mini-4k-instruct
 tags:
 - phi3
 - lora
@@ -8,8 +11,7 @@ tags:
 - text-generation
 language:
 - en
-pipeline_tag: text-generation
-library_name: transformers
 ---
 # Phi-3 HR Assistant
@@ -138,4 +140,4 @@ If you use this model, please cite:
   publisher={Hugging Face},
   url={https://huggingface.co/SK0988/phi3-hr-assistant}
 }
-```

 ---
+license: mit
 base_model: microsoft/Phi-3-mini-4k-instruct
+library_name: transformers
+pipeline_tag: text-generation
 tags:
 - phi3
 - lora
 - text-generation
 language:
 - en
+inference: true
 ---
 # Phi-3 HR Assistant
   publisher={Hugging Face},
   url={https://huggingface.co/SK0988/phi3-hr-assistant}
 }
+```