Instructions to use pruna-test/test-save-tiny-random-llama4-smashed with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use pruna-test/test-save-tiny-random-llama4-smashed with Transformers:

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

pipe = pipeline("text-generation", model="pruna-test/test-save-tiny-random-llama4-smashed")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("pruna-test/test-save-tiny-random-llama4-smashed")
model = AutoModelForCausalLM.from_pretrained("pruna-test/test-save-tiny-random-llama4-smashed")

Pruna AI

How to use pruna-test/test-save-tiny-random-llama4-smashed with Pruna AI:

# Use a pipeline as a high-level helper
from pruna import PrunaModel

pipe = PrunaModel.from_pretrained("pruna-test/test-save-tiny-random-llama4-smashed")

from pruna import PrunaModel
# Load model directly
from transformers import AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("pruna-test/test-save-tiny-random-llama4-smashed")
model = PrunaModel.from_pretrained("pruna-test/test-save-tiny-random-llama4-smashed")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use pruna-test/test-save-tiny-random-llama4-smashed with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "pruna-test/test-save-tiny-random-llama4-smashed"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "pruna-test/test-save-tiny-random-llama4-smashed",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/pruna-test/test-save-tiny-random-llama4-smashed

SGLang

How to use pruna-test/test-save-tiny-random-llama4-smashed 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 "pruna-test/test-save-tiny-random-llama4-smashed" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "pruna-test/test-save-tiny-random-llama4-smashed",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "pruna-test/test-save-tiny-random-llama4-smashed" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "pruna-test/test-save-tiny-random-llama4-smashed",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use pruna-test/test-save-tiny-random-llama4-smashed with Docker Model Runner:
```
docker model run hf.co/pruna-test/test-save-tiny-random-llama4-smashed
```

test-save-tiny-random-llama4-smashed / README.md

johnrachwanpruna

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	---
	library_name: transformers
	tags:
	- safetensors
	- pruna-ai
	---

	# Model Card for pruna-test/test-save-tiny-random-llama4-smashed

	This model was created using the [pruna](https://github.com/PrunaAI/pruna) library. Pruna is a model optimization framework built for developers, enabling you to deliver more efficient models with minimal implementation overhead.

	## Usage

	First things first, you need to install the pruna library:

	```bash
	pip install pruna
	```

	You can [use the transformers library to load the model](https://huggingface.co/pruna-test/test-save-tiny-random-llama4-smashed?library=transformers) but this might not include all optimizations by default.

	To ensure that all optimizations are applied, use the pruna library to load the model using the following code:

	```python
	from pruna import PrunaModel

	loaded_model = PrunaModel.from_pretrained(
	"pruna-test/test-save-tiny-random-llama4-smashed"
	)
	# we can then run inference using the methods supported by the base model
	```


	For inference, you can use the inference methods of the original model like shown in [the original model card](https://huggingface.co/hf-internal-testing/tiny-random-llama4?library=transformers).
	Alternatively, you can visit [the Pruna documentation](https://docs.pruna.ai/en/stable/) for more information.

	## Smash Configuration

	The compression configuration of the model is stored in the `smash_config.json` file, which describes the optimization methods that were applied to the model.

	```bash
	{
	"awq": false,
	"c_generate": false,
	"c_translate": false,
	"c_whisper": false,
	"deepcache": false,
	"diffusers_int8": false,
	"fastercache": false,
	"flash_attn3": false,
	"fora": false,
	"gptq": false,
	"half": false,
	"hqq": false,
	"hqq_diffusers": false,
	"hyper": false,
	"ifw": false,
	"img2img_denoise": false,
	"kvpress": false,
	"llm_int8": false,
	"moe_kernel_tuner": false,
	"pab": false,
	"padding_pruning": false,
	"qkv_diffusers": false,
	"quanto": false,
	"realesrgan_upscale": false,
	"reduce_noe": false,
	"ring_attn": false,
	"sage_attn": false,
	"stable_fast": false,
	"text_to_image_distillation_inplace_perp": false,
	"text_to_image_distillation_lora": false,
	"text_to_image_distillation_perp": false,
	"text_to_image_inplace_perp": false,
	"text_to_image_lora": false,
	"text_to_image_perp": false,
	"text_to_text_inplace_perp": false,
	"text_to_text_lora": false,
	"text_to_text_perp": false,
	"token_merging": false,
	"torch_compile": false,
	"torch_dynamic": false,
	"torch_structured": false,
	"torch_unstructured": false,
	"torchao": false,
	"whisper_s2t": false,
	"x_fast": false,
	"zipar": false,
	"batch_size": 1,
	"device": "cpu",
	"device_map": null,
	"save_fns": [],
	"save_artifacts_fns": [],
	"load_fns": [
	"transformers"
	],
	"load_artifacts_fns": [],
	"reapply_after_load": {}
	}
	```

	## 🌍 Join the Pruna AI community!

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