Instructions to use Teluv/Power-point-agent-2b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Teluv/Power-point-agent-2b with Transformers:

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

pipe = pipeline("text-generation", model="Teluv/Power-point-agent-2b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Teluv/Power-point-agent-2b")
model = AutoModelForCausalLM.from_pretrained("Teluv/Power-point-agent-2b", 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]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Teluv/Power-point-agent-2b with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Teluv/Power-point-agent-2b

SGLang

How to use Teluv/Power-point-agent-2b 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 "Teluv/Power-point-agent-2b" \
    --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": "Teluv/Power-point-agent-2b",
		"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 "Teluv/Power-point-agent-2b" \
        --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": "Teluv/Power-point-agent-2b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Teluv/Power-point-agent-2b with Docker Model Runner:
```
docker model run hf.co/Teluv/Power-point-agent-2b
```

AutoPowerPoint Agent — Golden v2 Cycle 33

This is the merged Hugging Face release build for AutoPowerPoint Agent. It combines the unsloth/Qwen3.5-2B base model with the best Reinforcement SFT checkpoint selected by the Golden v2 and challenge promotion gates.

Selection metrics

Metric	Score
Runtime success	85%
Layout success	67%
Studio success	14%
Content success	64%
Strict Studio success	10%
Challenge Strict Studio success	20%

Cycle 33 was promoted because it remained within the Golden regression tolerance and improved the challenge and combined promotion scores over the previous champion.

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "YOUR_ORG/autopowerpoint-agent-golden-v2-cycle33"
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map="auto",
    torch_dtype="auto",
    trust_remote_code=True,
)

The model is intended for local PowerPoint planning, slide-code generation, and repair workflows. Generated Python must still pass compile, static, runtime, PPTX, and layout validation before use.

Build provenance

Reinforcement job: 4622f449-63d7-4305-bdf8-808fc82696ce
Champion cycle: 33
Base model: unsloth/Qwen3.5-2B
Export dtype: bfloat16
Format: merged safetensors

Downloads last month: 2

Safetensors

Model size

2B params

Tensor type

BF16

Model tree for Teluv/Power-point-agent-2b

Base model

Qwen/Qwen3.5-2B-Base

Finetuned

Qwen/Qwen3.5-2B

Finetuned

unsloth/Qwen3.5-2B

Finetuned

(175)

this model