Instructions to use Altworld/Astrea-R8-Chat-9B-FP8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Altworld/Astrea-R8-Chat-9B-FP8 with Transformers:

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

pipe = pipeline("image-text-to-text", model="Altworld/Astrea-R8-Chat-9B-FP8")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("Altworld/Astrea-R8-Chat-9B-FP8")
model = AutoModelForMultimodalLM.from_pretrained("Altworld/Astrea-R8-Chat-9B-FP8", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.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(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Altworld/Astrea-R8-Chat-9B-FP8 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Altworld/Astrea-R8-Chat-9B-FP8"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Altworld/Astrea-R8-Chat-9B-FP8",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/Altworld/Astrea-R8-Chat-9B-FP8

SGLang

How to use Altworld/Astrea-R8-Chat-9B-FP8 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 "Altworld/Astrea-R8-Chat-9B-FP8" \
    --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": "Altworld/Astrea-R8-Chat-9B-FP8",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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 "Altworld/Astrea-R8-Chat-9B-FP8" \
        --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": "Altworld/Astrea-R8-Chat-9B-FP8",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use Altworld/Astrea-R8-Chat-9B-FP8 with Docker Model Runner:
```
docker model run hf.co/Altworld/Astrea-R8-Chat-9B-FP8
```

Astrea-R8-Chat-9B-FP8 / README.md

Lukack

Upload portable FP8 release (#1)

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	---
	license: apache-2.0
	base_model: Altworld/Astrea-R8-Chat-9B
	base_model_relation: quantized
	pipeline_tag: image-text-to-text
	library_name: transformers
	language:
	- en
	tags:
	- qwen3.5
	- chat
	- creative-writing
	- altworld
	- fp8
	- compressed-tensors
	---

	# Altworld Astrea R8 Chat 9B — FP8

	This is the portable FP8 release of
	[Altworld/Astrea-R8-Chat-9B](https://huggingface.co/Altworld/Astrea-R8-Chat-9B),
	Altworld's open 9B creative-writing and chat model.

	[Try Astrea](https://chat.altworld.io) ·
	[API](https://altworld.io/developer) ·
	[Documentation](https://astrea.altworld.io/docs/)

	The checkpoint uses LLM Compressor's `compressed-tensors` format and can be
	served directly by compatible versions of vLLM. It contains six safetensor
	shards totalling 11.09 GiB, compared with 17.53 GiB for the BF16 weights.

	## Quantization

	The checkpoint was generated from the released merged BF16 model with:

	- LLM Compressor 0.12.0;
	- compressed-tensors 0.17.1;
	- `FP8_DYNAMIC`, using E4M3 weights and BF16 per-output-channel scales;
	- dynamic per-token FP8 activation quantization at inference time;
	- data-free round-to-nearest post-training quantization.

	All 248 two-dimensional language-model linear weights are stored in FP8. The
	following sensitive or non-linear components remain in BF16:

	- input token embeddings;
	- `lm_head`;
	- Qwen3.5 linear-attention convolution kernels;
	- the complete vision tower.

	No calibration or training dataset was used during conversion.

	## Running with vLLM

	Use a recent vLLM release with `compressed-tensors` support and an accelerator
	with supported FP8 kernels:

	```bash
	vllm serve Altworld/Astrea-R8-Chat-9B-FP8 \
	--max-model-len 32768 \
	--gpu-memory-utilization 0.90
	```

	The quantization configuration is embedded in `config.json`; no separate
	`--quantization` argument should be necessary.

	Recommended sampling for writing:

	```json
	{
	"temperature": 0.8,
	"min_p": 0.025,
	"repetition_penalty": 1.08
	}
	```

	Use `temperature: 0.2` for ordinary factual chat. The bundled chat template
	works without a system prompt and defaults to hidden thinking.

	## Validation

	Before upload, this artifact passed the following local checks:

	- every original tensor is present with the expected shape;
	- all 248 targeted weights are E4M3 with matching BF16 channel scales;
	- all excluded components retain their original BF16 dtype;
	- the sharded safetensor index exactly matches all 1,008 stored tensors;
	- `AutoConfig`, `AutoTokenizer`, and `AutoProcessor` load successfully;
	- `Qwen3_5ForConditionalGeneration` loads the compressed checkpoint and
	completes a text-generation smoke test.

	The local Transformers smoke test dequantizes weights for its CPU fallback and
	therefore does not measure FP8 serving speed. Quantization changes model
	numerics, so applications should evaluate this checkpoint on their own prompts
	before replacing the BF16 release. Quant-specific Altworldbench results are not
	claimed on this card.

	## Model quality and limitations

	See the [BF16 model card](https://huggingface.co/Altworld/Astrea-R8-Chat-9B)
	for training lineage, benchmark methodology, samples, usage guidance, and
	limitations. In particular:

	- Astrea is text-first; inherited vision behavior was not part of its release
	evaluation;
	- English dominates training and evaluation;
	- outputs can be confidently wrong and must not be treated as authoritative in
	high-stakes settings.

	## License

	Apache-2.0, matching the source model. See `LICENSE` and `NOTICE`.