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HuggingFace Text Generation Inference (TGI). High-performance LLM serving with continuous batching, tensor parallelism, watermarking, and OpenAI-compatible API. Native HF model hub integration.
Passed 20 of 22 test cases with the skill, and 15 of 22 without it.
and 2 more in the full report
5 fixed15 passed either way2 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 5 test cases the skill fixed. Same model, same prompt, with and without the skill.
Which default model identifier from Hugging Face Hub is specified in the standard Docker deployment snippet for Text Generation Inference?
| Checked | Without skill | With skill |
|---|---|---|
| The model identifier is Qwen/Qwen2.5-1.5B-Instruct | Missed | Passed |
The instructions your model reads when the skill loads.
Text Generation Inference (TGI) is a production-ready LLM serving solution from Hugging Face. It provides optimized inference with continuous batching, quantization (GPTQ, AWQ), tensor parallelism, flash attention, and an OpenAI-compatible API.
# Docker deployment (recommended)
docker run --gpus all -p 8080:80 -v $HOME/models:/data ghcr.io/huggingface/text-generation-inference:latest --model-id Qwen/Qwen2.5-1.5B-Instruct
from openai import OpenAI
client = OpenAI(base_url="http://localhost:8080/v1", api_key="none")
response = client.chat.completions.create(
model="tgi",
messages=[{"role": "user", "content": "Hello!"}],
)
print(response.choices[0].message.content)
stream = client.chat.completions.create(
model="tgi",
messages=[{"role": "user", "content": "Write a poem"}],
stream=True,
)
for chunk in stream:
print(chunk.choices[0].delta.content or "", end="")
test cases passed, vs 15 / 22 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.