Instructions to use Threatthriver/phi4-finetuned-16bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Threatthriver/phi4-finetuned-16bit with Transformers:

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

pipe = pipeline("text-generation", model="Threatthriver/phi4-finetuned-16bit")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Threatthriver/phi4-finetuned-16bit", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Threatthriver/phi4-finetuned-16bit with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Threatthriver/phi4-finetuned-16bit"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Threatthriver/phi4-finetuned-16bit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Threatthriver/phi4-finetuned-16bit

SGLang

How to use Threatthriver/phi4-finetuned-16bit 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 "Threatthriver/phi4-finetuned-16bit" \
    --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": "Threatthriver/phi4-finetuned-16bit",
		"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 "Threatthriver/phi4-finetuned-16bit" \
        --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": "Threatthriver/phi4-finetuned-16bit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Desktop
Docker Model Runner
How to use Threatthriver/phi4-finetuned-16bit with Docker Model Runner:
```
docker model run hf.co/Threatthriver/phi4-finetuned-16bit
```

Threatthriver commited on Feb 23, 2025

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@@ -25,7 +25,7 @@ language:
 **Model Description:**
-This Llama model, named Threatthriver/phi4-finetuned-16bit, was fine-tuned by Threatthriver, potentially for applications in cybersecurity, threat intelligence, or related domains. It was trained using Unsloth (https://github.com/unslothai/unsloth) and Hugging Face's TRL library, which allowed for a 2x faster training process. The model is based on the unsloth/phi-4-unsloth-bnb-4bit base model. It was fine-tuned and saved in 16-bit precision.
 **Intended Use:**

 **Model Description:**
+This PHI-4 model, named Threatthriver/phi4-finetuned-16bit, was fine-tuned by Threatthriver, potentially for applications in cybersecurity, threat intelligence, or related domains. It was trained using Unsloth (https://github.com/unslothai/unsloth) and Hugging Face's TRL library, which allowed for a 2x faster training process. The model is based on the unsloth/phi-4-unsloth-bnb-4bit base model. It was fine-tuned and saved in 16-bit precision.
 **Intended Use:**