Instructions to use Xtra-Computing/XtraGPT-14B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Xtra-Computing/XtraGPT-14B with Transformers:

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

pipe = pipeline("text-generation", model="Xtra-Computing/XtraGPT-14B", 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("Xtra-Computing/XtraGPT-14B", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("Xtra-Computing/XtraGPT-14B", trust_remote_code=True)
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 Xtra-Computing/XtraGPT-14B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Xtra-Computing/XtraGPT-14B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Xtra-Computing/XtraGPT-14B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Xtra-Computing/XtraGPT-14B

SGLang

How to use Xtra-Computing/XtraGPT-14B 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 "Xtra-Computing/XtraGPT-14B" \
    --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": "Xtra-Computing/XtraGPT-14B",
		"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 "Xtra-Computing/XtraGPT-14B" \
        --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": "Xtra-Computing/XtraGPT-14B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Xtra-Computing/XtraGPT-14B with Docker Model Runner:
```
docker model run hf.co/Xtra-Computing/XtraGPT-14B
```

XtraGPT-14B

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Commit History

Update README.md 01dd781 verified

Add _name_or_path: microsoft/phi-4 to clarify base model 579878d verified

Clean remaining training artifacts ac3efac verified

Update to checkpoint-15000: reshard safetensors, clean training artifacts 291fc66 verified

Update README.md 453aa19 verified

Update README.md 839198b verified

Update README.md 8776ad1 verified

Add files using upload-large-folder tool 7273222 verified

Create README.md 3e95cde verified

initial commit 013045f verified

Update README.md

01dd781
verified

Add _name_or_path: microsoft/phi-4 to clarify base model

579878d
verified

Clean remaining training artifacts

ac3efac
verified

Update to checkpoint-15000: reshard safetensors, clean training artifacts

291fc66
verified

Update README.md

453aa19
verified

Update README.md

839198b
verified

Update README.md

8776ad1
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Add files using upload-large-folder tool

7273222
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Create README.md

3e95cde
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initial commit

013045f
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