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
```

Power-point-agent-2b

File size: 1,627 Bytes

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---
library_name: transformers
pipeline_tag: text-generation
base_model: unsloth/Qwen3.5-2B
tags:
  - powerpoint
  - slide-generation
  - python-pptx
  - qwen3.5
  - merged-lora
license: apache-2.0
---

# 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

```python
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`