---
library_name: transformers
base_model:
- Qwen/Qwen3.8-27B-FP8
---
This tiny model is intended for debugging. It is randomly initialized using the configuration adapted from [Qwen/Qwen3.8-27B-FP8](https://huggingface.co/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 (✅ verified)
- 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.
```bash
model_id=tiny-random/qwen3.8-fp8
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 (✅ verified)
```python
import numpy as np
import torch
from PIL import Image
from transformers import AutoProcessor, Qwen3_5ForConditionalGeneration
from transformers.video_utils import VideoMetadata
model_id = "tiny-random/qwen3.8-fp8"
model = Qwen3_5ForConditionalGeneration.from_pretrained(
model_id, dtype=torch.bfloat16, device_map="cuda", attn_implementation="sdpa",
)
processor = AutoProcessor.from_pretrained(model_id)
image = Image.new("RGB", (64, 64), (220, 30, 30))
# Four predecoded RGB frames, with timestamps supplied through video_metadata.
video = np.stack([np.asarray(Image.new("RGB", (64, 64), color)) for color in
[(220, 30, 30), (30, 220, 30), (30, 30, 220), (220, 220, 30)]])
messages = [{"role": "user", "content": [
{"type": "image", "image": image},
{"type": "video", "video": video},
{"type": "text", "text": "Describe the image and the video."},
]}]
inputs = processor.apply_chat_template(
messages, tokenize=True, add_generation_prompt=True,
return_dict=True, return_tensors="pt",
do_sample_frames=False,
video_metadata=[VideoMetadata(total_num_frames=4, fps=2, frames_indices=[0, 1, 2, 3])],
).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 (✅ verified):
Click to expand
```python
import json
from copy import deepcopy
from pathlib import Path
from shutil import copyfile
import torch
from huggingface_hub import hf_hub_download, list_repo_files
from safetensors.torch import save_file
from transformers import AutoConfig, AutoModelForImageTextToText, set_seed
source_model_id = "Qwen/Qwen3.8-27B-FP8"
save_folder = "/tmp/tiny-random/qwen38-fp8"
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)
backbone = model.model.language_model
text = backbone.config
def norm():
return type(backbone.norm)(text.hidden_size, text.rms_norm_eps).to(torch.bfloat16)
model.mtp = torch.nn.ModuleDict({
"pre_fc_norm_embedding": norm(),
"pre_fc_norm_hidden": norm(),
"fc": torch.nn.Linear(2 * text.hidden_size, text.hidden_size, bias=False, dtype=torch.bfloat16),
"layers": torch.nn.ModuleList([deepcopy(backbone.layers[-1])]),
"norm": norm(),
})
with torch.no_grad():
for name, param in sorted(model.named_parameters()):
if name.endswith("A_log"):
param.zero_()
elif name.endswith("dt_bias"):
param.fill_(1)
elif "norm" in name and name.endswith("weight"):
param.fill_(1 if "visual" in name or ".linear_attn.norm." in name else 0)
elif name.endswith("bias"):
param.zero_()
else:
torch.nn.init.normal_(param, mean=0, std=0.02)
state = {name: value.detach().cpu().contiguous() for name, value in model.state_dict().items()}
# Prune source exclusions absent from this architecture before FP8 loading.
names = set(dict(model.named_modules())) | set(dict(model.named_parameters()))
exclusions = [name for name in quantization["modules_to_not_convert"] if name in names]
quantization["modules_to_not_convert"] = exclusions
def pack(name, weight):
n, k = weight.shape
bn, bk = quantization["weight_block_size"]
blocks = weight.float().reshape(n // bn, bn, k // bk, bk)
scales = (blocks.abs().amax(dim=(1, 3)) / 448).clamp_min(1e-12).to(torch.bfloat16)
packed = (blocks / scales.float()[:, None, :, None]).clamp(-448, 448)
state[name] = packed.reshape(n, k).to(torch.float8_e4m3fn).contiguous()
state[name.removesuffix("weight") + "weight_scale_inv"] = scales.contiguous()
for name, module in model.named_modules():
excluded = any(name == prefix or name.startswith(prefix + ".") for prefix in exclusions)
if isinstance(module, torch.nn.Linear) and not excluded:
pack(name + ".weight", state[name + ".weight"])
save_file(state, folder / "model.safetensors", metadata={"format": "pt"})
raw["quantization_config"] = quantization
(folder / "config.json").write_text(json.dumps(raw, indent=2))
# Preserve tokenizer, processor and generation assets exactly as published.
assets = {
"tokenizer.json", "tokenizer_config.json", "special_tokens_map.json",
"vocab.json", "merges.txt", "added_tokens.json", "chat_template.jinja",
"chat_template.json", "preprocessor_config.json", "processor_config.json",
"video_preprocessor_config.json", "generation_config.json",
}
for name in sorted(set(list_repo_files(source_model_id, revision=source_revision)) & assets):
copyfile(hf_hub_download(source_model_id, name, revision=source_revision), folder / name)
```
Click to expand
```text
Qwen3_5ForConditionalGeneration(
(model): Qwen3_5Model(
(visual): Qwen3_5VisionModel(
(patch_embed): Qwen3_5VisionPatchEmbed(
(proj): Conv3d(3, 64, kernel_size=(2, 16, 16), stride=(2, 16, 16))
)
(pos_embed): Embedding(2304, 64)
(rotary_pos_emb): Qwen3_5VisionRotaryEmbedding()
(blocks): ModuleList(
(0-1): 2 x Qwen3_5VisionBlock(
(norm1): LayerNorm((64,), eps=1e-06, elementwise_affine=True, bias=True)
(norm2): LayerNorm((64,), eps=1e-06, elementwise_affine=True, bias=True)
(attn): Qwen3_5VisionAttention(
(qkv): Linear(in_features=64, out_features=192, bias=True)
(proj): Linear(in_features=64, out_features=64, bias=True)
)
(mlp): Qwen3_5VisionMLP(
(linear_fc1): Linear(in_features=64, out_features=128, bias=True)
(linear_fc2): Linear(in_features=128, out_features=64, bias=True)
(act_fn): GELUTanh()
)
)
)
(merger): Qwen3_5VisionPatchMerger(
(norm): LayerNorm((64,), eps=1e-06, elementwise_affine=True, bias=True)
(linear_fc1): Linear(in_features=256, out_features=256, bias=True)
(act_fn): GELU(approximate='none')
(linear_fc2): Linear(in_features=256, out_features=32, bias=True)
)
)
(language_model): Qwen3_5TextModel(
(embed_tokens): Embedding(248320, 32)
(layers): ModuleList(
(0-2): 3 x Qwen3_5DecoderLayer(
(linear_attn): Qwen3_5GatedDeltaNet(
(conv1d): Conv1d(1280, 1280, kernel_size=(4,), stride=(1,), padding=(3,), groups=1280, bias=False)
(norm): Qwen3_5RMSNormGated()
(out_proj): Linear(in_features=768, out_features=32, bias=False)
(in_proj_qkv): Linear(in_features=32, out_features=1280, bias=False)
(in_proj_z): Linear(in_features=32, out_features=768, bias=False)
(in_proj_b): Linear(in_features=32, out_features=6, bias=False)
(in_proj_a): Linear(in_features=32, out_features=6, bias=False)
)
(mlp): Qwen3_5MLP(
(gate_proj): Linear(in_features=32, out_features=512, bias=False)
(up_proj): Linear(in_features=32, out_features=512, bias=False)
(down_proj): Linear(in_features=512, out_features=32, bias=False)
(act_fn): SiLUActivation()
)
(input_layernorm): Qwen3_5RMSNorm((32,), eps=1e-06)
(post_attention_layernorm): Qwen3_5RMSNorm((32,), eps=1e-06)
)
(3): Qwen3_5DecoderLayer(
(self_attn): Qwen3_5Attention(
(q_proj): Linear(in_features=32, out_features=3072, bias=False)
(k_proj): Linear(in_features=32, out_features=256, bias=False)
(v_proj): Linear(in_features=32, out_features=256, bias=False)
(o_proj): Linear(in_features=1536, out_features=32, bias=False)
(q_norm): Qwen3_5RMSNorm((128,), eps=1e-06)
(k_norm): Qwen3_5RMSNorm((128,), eps=1e-06)
)
(mlp): Qwen3_5MLP(
(gate_proj): Linear(in_features=32, out_features=512, bias=False)
(up_proj): Linear(in_features=32, out_features=512, bias=False)
(down_proj): Linear(in_features=512, out_features=32, bias=False)
(act_fn): SiLUActivation()
)
(input_layernorm): Qwen3_5RMSNorm((32,), eps=1e-06)
(post_attention_layernorm): Qwen3_5RMSNorm((32,), eps=1e-06)
)
(4-6): 3 x Qwen3_5DecoderLayer(
(linear_attn): Qwen3_5GatedDeltaNet(
(conv1d): Conv1d(1280, 1280, kernel_size=(4,), stride=(1,), padding=(3,), groups=1280, bias=False)
(norm): Qwen3_5RMSNormGated()
(out_proj): Linear(in_features=768, out_features=32, bias=False)
(in_proj_qkv): Linear(in_features=32, out_features=1280, bias=False)
(in_proj_z): Linear(in_features=32, out_features=768, bias=False)
(in_proj_b): Linear(in_features=32, out_features=6, bias=False)
(in_proj_a): Linear(in_features=32, out_features=6, bias=False)
)
(mlp): Qwen3_5MLP(
(gate_proj): Linear(in_features=32, out_features=512, bias=False)
(up_proj): Linear(in_features=32, out_features=512, bias=False)
(down_proj): Linear(in_features=512, out_features=32, bias=False)
(act_fn): SiLUActivation()
)
(input_layernorm): Qwen3_5RMSNorm((32,), eps=1e-06)
(post_attention_layernorm): Qwen3_5RMSNorm((32,), eps=1e-06)
)
(7): Qwen3_5DecoderLayer(
(self_attn): Qwen3_5Attention(
(q_proj): Linear(in_features=32, out_features=3072, bias=False)
(k_proj): Linear(in_features=32, out_features=256, bias=False)
(v_proj): Linear(in_features=32, out_features=256, bias=False)
(o_proj): Linear(in_features=1536, out_features=32, bias=False)
(q_norm): Qwen3_5RMSNorm((128,), eps=1e-06)
(k_norm): Qwen3_5RMSNorm((128,), eps=1e-06)
)
(mlp): Qwen3_5MLP(
(gate_proj): Linear(in_features=32, out_features=512, bias=False)
(up_proj): Linear(in_features=32, out_features=512, bias=False)
(down_proj): Linear(in_features=512, out_features=32, bias=False)
(act_fn): SiLUActivation()
)
(input_layernorm): Qwen3_5RMSNorm((32,), eps=1e-06)
(post_attention_layernorm): Qwen3_5RMSNorm((32,), eps=1e-06)
)
)
(norm): Qwen3_5RMSNorm((32,), eps=1e-06)
(rotary_emb): Qwen3_5TextRotaryEmbedding()
)
)
(lm_head): Linear(in_features=32, out_features=248320, bias=False)
(mtp): ModuleDict(
(pre_fc_norm_embedding): Qwen3_5RMSNorm((32,), eps=1e-06)
(pre_fc_norm_hidden): Qwen3_5RMSNorm((32,), eps=1e-06)
(fc): Linear(in_features=64, out_features=32, bias=False)
(layers): ModuleList(
(0): Qwen3_5DecoderLayer(
(self_attn): Qwen3_5Attention(
(q_proj): Linear(in_features=32, out_features=3072, bias=False)
(k_proj): Linear(in_features=32, out_features=256, bias=False)
(v_proj): Linear(in_features=32, out_features=256, bias=False)
(o_proj): Linear(in_features=1536, out_features=32, bias=False)
(q_norm): Qwen3_5RMSNorm((128,), eps=1e-06)
(k_norm): Qwen3_5RMSNorm((128,), eps=1e-06)
)
(mlp): Qwen3_5MLP(
(gate_proj): Linear(in_features=32, out_features=512, bias=False)
(up_proj): Linear(in_features=32, out_features=512, bias=False)
(down_proj): Linear(in_features=512, out_features=32, bias=False)
(act_fn): SiLUActivation()
)
(input_layernorm): Qwen3_5RMSNorm((32,), eps=1e-06)
(post_attention_layernorm): Qwen3_5RMSNorm((32,), eps=1e-06)
)
)
(norm): Qwen3_5RMSNorm((32,), eps=1e-06)
)
)
```