Instructions to use yujiepan/qwen3.8-fp8-tiny-random with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use yujiepan/qwen3.8-fp8-tiny-random with Transformers:

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

pipe = pipeline("image-text-to-text", model="yujiepan/qwen3.8-fp8-tiny-random")
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)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("yujiepan/qwen3.8-fp8-tiny-random")
model = AutoModelForMultimodalLM.from_pretrained("yujiepan/qwen3.8-fp8-tiny-random", 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=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use yujiepan/qwen3.8-fp8-tiny-random with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "yujiepan/qwen3.8-fp8-tiny-random"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "yujiepan/qwen3.8-fp8-tiny-random",
		"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/yujiepan/qwen3.8-fp8-tiny-random

SGLang

How to use yujiepan/qwen3.8-fp8-tiny-random 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 "yujiepan/qwen3.8-fp8-tiny-random" \
    --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": "yujiepan/qwen3.8-fp8-tiny-random",
		"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 "yujiepan/qwen3.8-fp8-tiny-random" \
        --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": "yujiepan/qwen3.8-fp8-tiny-random",
		"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 yujiepan/qwen3.8-fp8-tiny-random with Docker Model Runner:
```
docker model run hf.co/yujiepan/qwen3.8-fp8-tiny-random
```

This tiny model is intended for debugging. It is randomly initialized using the configuration adapted from Qwen/Qwen3.8-27B-FP8.

Structural comparison with Qwen/Qwen3.8-27B-FP8:

Feature	Source	Tiny
Input modalities	Text, image, video	Same
Vocabulary	248,320	Same; special-token IDs preserved
Backbone layers	64	8
Hidden size	5,120	32
Attention Q/KV heads; head dim	24/4; 256	12/2; 128
Attention cycle	3 linear + 1 full	Same; two complete cycles
Linear-attention key/value heads	16/48	2/6
Linear-attention key/value head dim	128/128	Same
MLP intermediate size	17,408	512
MTP	1 full-attention layer; shared embeddings	Same
Vision layers / hidden size	27 / 1,152	2 / 64
Vision patch / temporal patch / merge	16 / 2 / 2	Same
Vision output size	5,120	32
Checkpoint format	FP8 language/MTP projections; BF16 vision/gates; 128x128 blocks	Same FP8/BF16 layout; 32x32 blocks

File path	Size
model.safetensors	34.1MB

Example usage:

SGLang
- Runs with TP=2, the embedded NEXTN/MTP layer, and CUDA Graph.
- Verified for text, image-plus-text and video-plus-text on two NVIDIA H20 GPUs; see Test environment for dependency versions.

model_id=yujiepan/qwen3.8-fp8-tiny-random
sglang serve \
  --model-path $model_id \
  --tp-size 2 \
  --fp8-gemm-backend triton \
  --moe-runner-backend triton \
  --speculative-algorithm NEXTN \
  --speculative-num-steps 3 \
  --speculative-eagle-topk 1 \
  --speculative-num-draft-tokens 4 \
  --context-length 4096 \
  --max-total-tokens 4096 \
  --chunked-prefill-size 512 \
  --mem-fraction-static 0.4 \
  --cuda-graph-config '{"decode": {"bs": [1, 2, 4, 8]}}' \
  --disable-custom-all-reduce

Transformers

import torch
from PIL import Image
from transformers import AutoProcessor, Qwen3_5ForConditionalGeneration

model_id = "yujiepan/qwen3.8-fp8-tiny-random"
model = Qwen3_5ForConditionalGeneration.from_pretrained(
    model_id, dtype=torch.bfloat16, device_map="cuda", attn_implementation="sdpa",
)
processor = AutoProcessor.from_pretrained(model_id)
messages = [{"role": "user", "content": [
    {"type": "image", "image": Image.new("RGB", (64, 64), (220, 30, 30))},
    {"type": "text", "text": "Describe this image."},
]}]
inputs = processor.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True,
    return_dict=True, return_tensors="pt",
).to(model.device)
output = model.generate(**inputs, do_sample=False, max_new_tokens=10)
print(processor.decode(output[0, inputs["input_ids"].shape[1]:], skip_special_tokens=True))

Codes to create this repo:

Click to expand

import json
from pathlib import Path

import torch
from huggingface_hub import hf_hub_download
from transformers import AutoConfig, AutoModelForImageTextToText, AutoProcessor, GenerationConfig, set_seed
# Creation helpers: pip install git+https://github.com/yujiepan/tiny-random.git
from tiny_random.qwen_utils import add_qwen_mtp, save_random_qwen_checkpoint, compact_qwen_tokenizer, check_qwen_repo_size

source_model_id = "Qwen/Qwen3.8-27B-FP8"
save_folder = "/tmp/yujiepan/qwen38-fp8-tiny-random"
source_revision = '017b9c7af6b5689d5dd426a76e0bc077eb5ca20a'
set_seed(42)
folder = Path(save_folder)
folder.mkdir(parents=True, exist_ok=True)
raw = json.loads(Path(hf_hub_download(source_model_id, "config.json", revision=source_revision)).read_text())
quantization = raw.pop("quantization_config")
quantization["weight_block_size"] = [32, 32]
raw["text_config"].update(
    hidden_size=32, head_dim=128, num_hidden_layers=8,
    layer_types=(["linear_attention"] * 3 + ["full_attention"]) * 2,
    num_attention_heads=12, num_key_value_heads=2,
    linear_num_key_heads=2, linear_num_value_heads=6,
    linear_key_head_dim=128, linear_value_head_dim=128,
    intermediate_size=512,
)
raw["text_config"]["rope_parameters"]["mrope_section"] = [6, 5, 5]
raw["vision_config"].update(depth=2, hidden_size=64, intermediate_size=128, num_heads=2, out_hidden_size=32)
(folder / "config.json").write_text(json.dumps(raw, indent=2))
config = AutoConfig.from_pretrained(folder)
model = AutoModelForImageTextToText.from_config(config, dtype=torch.bfloat16)
add_qwen_mtp(model, model.model.language_model)
save_random_qwen_checkpoint(model, folder, quantization)
raw["quantization_config"] = quantization
(folder / "config.json").write_text(json.dumps(raw, indent=2))
AutoProcessor.from_pretrained(source_model_id, revision=source_revision).save_pretrained(folder)
GenerationConfig.from_pretrained(source_model_id, revision=source_revision).save_pretrained(folder)
compact_qwen_tokenizer(folder)
check_qwen_repo_size(folder)

Printing the model:

Click to expand

Qwen3.8 VL: 8 backbone layers + 1 MTP layer; hidden_size=32

Test environment:

Pillow: 12.2.0
accelerate: 1.15.0
av: 19.0.2
flashinfer-python: 0.7.2
huggingface_hub: 1.33.0
kernels: 0.17.0
numpy: 2.3.5
safetensors: 0.8.0
sglang: 0.5.23.dev384+g9ab74b851
tiny-random: 0.2.0
torch: 2.14.1
torchcodec: 0.17.0
transformers: 5.19.0

Change log:

2026-10-23: Initial version.
2026-10-23: Reduced all repo files below 50 MB; GPU-validated TP=2, MTP and CUDA Graph.

This repo is generated by GPT-6.1-Sol

Downloads last month: 29

Safetensors

Model size

17.8M params

Tensor type

BF16

F8_E4M3

Model tree for yujiepan/qwen3.8-fp8-tiny-random

Base model

Qwen/Qwen3.8-27B

Quantized

Qwen/Qwen3.8-27B-FP8

Quantized

(9)

this model