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.

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.

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; everything beyond is untested.