Instructions to use Dexy2/Piko-9b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Dexy2/Piko-9b with Transformers:

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

pipe = pipeline("image-text-to-text", model="Dexy2/Piko-9b")
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("Dexy2/Piko-9b")
model = AutoModelForMultimodalLM.from_pretrained("Dexy2/Piko-9b", 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 Dexy2/Piko-9b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Dexy2/Piko-9b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Dexy2/Piko-9b",
		"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/Dexy2/Piko-9b

SGLang

How to use Dexy2/Piko-9b 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 "Dexy2/Piko-9b" \
    --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": "Dexy2/Piko-9b",
		"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 "Dexy2/Piko-9b" \
        --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": "Dexy2/Piko-9b",
		"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 Dexy2/Piko-9b with Docker Model Runner:
```
docker model run hf.co/Dexy2/Piko-9b
```

Piko-9b / docs /hardware.md

Dexy2

Rewrite model card around verified evidence; correct misattributed benchmarks and config path leak

0810902 verified 1 day ago

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	# Hardware requirements

	## The one rule

	The whole model must sit on a single device. Piko-9b's 24 linear-attention layers keep a
	recurrent state that does not survive a CPU/GPU split. `device_map="auto"` on an
	undersized GPU silently offloads and the model then emits a single repeated token — fluent-looking
	garbage, no error. See [troubleshooting.md](troubleshooting.md).

	So the question is not "how little VRAM can I get away with" but "which precision fits entirely".

	## VRAM by configuration

	\| Configuration \| Weights \| Practical VRAM \| Fits on \|
	\|---\|---:\|---:\|---\|
	\| bfloat16 / float16 \| 19.8 GB \| ~22 GB \| RTX 4090, A6000, A100, L40S \|
	\| 8-bit (bnb) \| 10.1 GB \| ~12 GB \| RTX 3090, 4080, A5000 \|
	\| 4-bit NF4 (bnb) \| 5.6 GB \| ~8 GB \| RTX 3060 12 GB, 4060 Ti 16 GB, 5070 Ti \|

	Weights column = 9,653,104,368 parameters × bytes-per-parameter. The practical column adds the
	CUDA context, activations, and KV cache for a short prompt. Long prompts need more — the 8
	full-attention layers carry a KV cache that grows with sequence length, while the 24
	linear-attention layers keep a fixed-size state (which is why this architecture is comparatively
	cheap at long context).

	Measured: 4-bit NF4 on an RTX 5070 Ti (17.1 GB) occupied 8.1 GB with the model resident
	and idle. See [`reports/performance_report.md`](../reports/performance_report.md).

	## Verified configuration

	Everything measured in this repository ran on:

	\| \| \|
	\|---\|---\|
	\| GPU \| NVIDIA GeForce RTX 5070 Ti, 17.1 GB \|
	\| CPU RAM \| 33.9 GB \|
	\| OS \| Windows 11 + WSL2 Ubuntu \|
	\| Python \| 3.12.3 \|
	\| torch \| 2.10.0+cu128 \|
	\| transformers \| 5.5.0 \|
	\| Precision \| bfloat16 weights, 4-bit NF4 at inference \|

	No number in this repository is extrapolated to hardware that was not used.

	## CPU inference

	Technically possible, practically not advisable:

	* float32 weights ≈ 38.6 GB of RAM; bfloat16 on CPU is poorly supported for these kernels.
	* No linear-attention fast path on CPU.
	* Expect well under one token per second.

	It is not a supported configuration and none of the results here were produced that way.

	## Multi-GPU

	Not validated, and not recommended without testing. Splitting layers across two GPUs is the
	same class of operation that breaks under CPU offload. If you try it, run
	`tests/test_text_generation.py::test_generation_is_not_degenerate` first — it exists precisely to
	catch this.

	Tensor parallelism in a serving engine (vLLM, SGLang) is a different mechanism and may be fine,
	but was not tested here.

	## Disk and load time

	\| \| \|
	\|---\|---\|
	\| Checkpoint size \| 21.3 GB (11 shards) \|
	\| Tokenizer \| 20 MB \|
	\| Load from NVMe \| seconds to ~1 minute \|
	\| Load from external USB \| 10–20 minutes (measured) \|

	Copy the checkpoint to internal storage before doing repeated work.

	## Context length and memory

	`max_position_embeddings` is 262,144 and no RoPE scaling is applied, so the context is
	architectural rather than extended. That is a statement about the configuration, not about
	quality: this release does not establish that output remains useful at that length. Long-context
	behaviour was probed only at the lengths recorded in
	[`reports/performance_report.md`](../reports/performance_report.md); everything beyond is
	untested.