---
library_name: transformers
base_model:
- moonshotai/Kimi-K3
pipeline_tag: image-text-to-text
---
This tiny model is intended for debugging. It is randomly initialized using the configuration adapted from [moonshotai/Kimi-K3](https://huggingface.co/moonshotai/Kimi-K3).
Note:
- This is the **BF16** (no quantization) sibling of `kimi-k3-tiny-random`.
Routed expert `w1/w2/w3` stay plain BF16 weights; there is no MXFP4 packing
and no `quantization_config`.
- Structural comparison with the original `moonshotai/Kimi-K3`:
| Structural feature | Original K3 | This tiny model |
|---|---|---|
| Attention cycle | 3 KDA + 1 MLA per group | Same |
| Ending | Final layer is MLA | Same |
| KDA : MLA ratio | 69:24 (~3:1) | 12:5 (~3:1) |
| Total layers | 93 | 17 (4 groups + final MLA) |
| FFN layout | Layer 0 Dense MLP, others MoE | Same |
| MoE routing | top-16, 2 shared experts, group=1 | Same |
| Routed experts | 896 | 64 |
| AttnRes checkpoint | Every 12 layers (3 groups) | Every 8 layers (2 groups), proportionally scaled |
| KDA kernel params | num_heads=96, head_dim=128, conv=4, gate_lower_bound=-5 | heads 96 -> 8; per-head dims same |
| MLA kernel params | 96 heads, q/kv LoRA ranks 1536/512, nope/rope/v head dims | heads 96 -> 8, q rank 1536 -> 256, kv rank stays 512; kernel dims same |
| MoE quantization | Only routed expert w1/w2/w3 MXFP4 | **None (full BF16)** |
| Expert linear dims | moe_intermediate=3072, routed_hidden=3584 | moe_intermediate=32, routed_hidden=32 |
| Vision head_dim | 32 | Same |
| MTP | None | None |
- What is shrunk: layer count (93 -> 17), residual width (hidden_size=8),
attention heads (96 -> 8) and MLA q LoRA rank (1536 -> 256). The
kv_lora_rank stays 512 because vLLM's Kimi fused MLA decode kernel requires
cache/query head size 512 + 64 = 576. Expert FFN widths are further reduced
vs the MXFP4 tiny variant (256 -> 32) so the unquantized BF16 checkpoint
stays <= ~150MB.
- The creation of this model was assisted by Cursor Grok 4.5.
| File path | Size |
|------|------|
| model.safetensors | 31.9MB |
### Example usage:
```python
import numpy as np
import torch
from PIL import Image
from transformers import AutoModel, AutoProcessor
model_id = "tiny-random/kimi-k3-bf16"
processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True)
model = AutoModel.from_pretrained(
model_id,
dtype=torch.bfloat16,
device_map='cuda',
trust_remote_code=True,
attn_implementation='eager',
).eval()
image = Image.fromarray(np.random.default_rng(42).integers(0, 256, (56, 56, 3), dtype=np.uint8))
tools = [{
'type': 'function',
'function': {
'name': 'get_image_size',
'description': 'Return the width and height of an image.',
'parameters': {
'type': 'object',
'properties': {
'image_index': {'type': 'integer', 'description': 'Zero-based image index.'},
},
'required': ['image_index'],
},
},
}]
messages = [{
'role': 'user',
'content': [
{'type': 'image', 'image': image},
{'type': 'text', 'text': 'Use the available tool to get this image size.'},
],
}]
inputs = processor(
messages=messages,
tools=tools,
tool_choice='required',
return_tensors='pt',
).to(model.device)
with torch.no_grad():
outputs = model.generate(**inputs, max_new_tokens=32)
generated_ids = outputs.sequences if hasattr(outputs, 'sequences') else outputs
print(processor.decode(generated_ids[0].detach().cpu().tolist()))
```
### Codes to create this repo:
Click to expand
```python
import json
from pathlib import Path
import accelerate
import torch
from huggingface_hub import file_exists, hf_hub_download, list_repo_files
from safetensors.torch import load_file, save_file
from transformers import AutoConfig, AutoModel, GenerationConfig, set_seed
source_model_id = "moonshotai/Kimi-K3"
save_folder = "/tmp/tiny-random/kimi-k3-bf16" # pyright: ignore[reportUnusedExpression] # codegen marker
Path(save_folder).mkdir(parents=True, exist_ok=True)
suffixes = ['.json', '.py', '.model', '.jinja']
for filename in list_repo_files(
source_model_id,
repo_type='model',
revision=source_revision,
):
if any(filename.endswith(suffix) for suffix in suffixes) and not filename.endswith('.index.json'):
hf_hub_download(
repo_id=source_model_id,
filename=filename,
repo_type='model',
revision=source_revision,
local_dir=save_folder,
)
def replace_file(filepath, replacements):
with open(filepath, 'r', encoding='utf-8') as f:
code = f.read()
for old_string, new_string in replacements:
if old_string not in code:
if new_string in code:
continue
raise ValueError(f'Expected code was not found in {filepath}: {old_string}')
code = code.replace(old_string, new_string)
with open(filepath, 'w', encoding='utf-8') as f:
f.write(code)
# The upstream reference implementation forces FlashAttention for MLA even
# when eager attention is requested. Allow the tiny MLA layer to use eager
# attention without requiring the separate flash-attn package.
force_flash_code = ''' if getattr(config, "_attn_implementation", None) is not None:
if config._attn_implementation != "flash_attention_2":
logger.warning_once(
f"Ignoring the provided attention implementation {config._attn_implementation}")
logger.warning_once("Using flash_attention_2 backend instead.")
config._attn_implementation = "flash_attention_2"
else:
config._attn_implementation = "flash_attention_2"'''
per_channel_gate_code = ''' g = self.f_b_proj(self.f_a_proj(hidden_states))
g = rearrange(g, '... (h d) -> ... h d', d=self.head_dim)
beta = self.b_proj(hidden_states).float()'''
per_channel_gate_compat_code = ''' g = self.f_b_proj(self.f_a_proj(hidden_states))
g = rearrange(g, '... (h d) -> ... h d', d=self.head_dim)
# The released K3 checkpoint stores per-channel decay shared by all heads.
g = self.gate_lower_bound * torch.sigmoid(
self.A_log.float().exp().view(1, 1, 1, self.head_dim)
* (g.float() + self.dt_bias.float().view(1, 1, self.num_heads, self.head_dim))
).to(g.dtype)
beta = self.b_proj(hidden_states).float()'''
causal_mask_code = ''' causal_mask = create_causal_mask(
config=self.config,
input_embeds=inputs_embeds,
attention_mask=attention_mask,
cache_position=cache_position,
past_key_values=past_key_values,
position_ids=position_ids,
)'''
causal_mask_compat_code = ''' if version.parse(transformers.__version__) >= version.parse("5.0.0"):
causal_mask = create_causal_mask(
config=self.config,
inputs_embeds=inputs_embeds,
attention_mask=attention_mask,
past_key_values=past_key_values,
position_ids=position_ids,
)
else:
causal_mask = create_causal_mask(
config=self.config,
input_embeds=inputs_embeds,
attention_mask=attention_mask,
cache_position=cache_position,
past_key_values=past_key_values,
position_ids=position_ids,
)'''
cache_api_code = ''' def get_mask_sizes(self, cache_position: torch.Tensor, layer_idx: int) -> tuple[int, int]:
"""
Return a tuple (kv_length, kv_offset) corresponding to the length and offset that will be returned for
the given layer at `layer_idx`.
The masks are then prepared according to the given lengths (kv_length, kv_offset) and patterns for each layer.
"""
kv_offset = 0
query_length = cache_position.shape[0]'''
cache_api_compat_code = ''' def get_query_offset(self, layer_idx: int) -> int:
return self.get_seq_length(layer_idx)
def get_mask_sizes(self, cache_position: torch.Tensor | int, layer_idx: int) -> tuple[int, int]:
"""
Return a tuple (kv_length, kv_offset) corresponding to the length and offset that will be returned for
the given layer at `layer_idx`.
The masks are then prepared according to the given lengths (kv_length, kv_offset) and patterns for each layer.
"""
kv_offset = 0
query_length = cache_position if isinstance(cache_position, int) else cache_position.shape[0]'''
replace_file(f'{save_folder}/modeling_kimi_linear.py', [
# Transformers 5 compatibility.
(
'from transformers.modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel',
'from transformers.modeling_utils import ALL_ATTENTION_FUNCTIONS, OutputRecorder, PreTrainedModel',
),
(
'from transformers.utils.generic import OutputRecorder, check_model_inputs',
'from transformers.utils.generic import check_model_inputs',
),
(
' _tied_weights_keys = ["lm_head.weight"]',
''' _tied_weights_keys = (
{"lm_head.weight": "model.embed_tokens.weight"}
if version.parse(transformers.__version__) >= version.parse("5.0.0")
else ["lm_head.weight"]
)''',
),
(causal_mask_code, causal_mask_compat_code),
(cache_api_code, cache_api_compat_code),
# Allow eager MLA instead of forcing the optional flash-attn package.
(
force_flash_code,
' config._attn_implementation = getattr(config, "_attn_implementation", "eager")',
),
# Fix stale K3 reference code: released shards store A_log as
# [head_dim], and the decay is applied per channel across all heads.
(
' self.num_heads, dtype=torch.float32).uniform_(1, 16)))',
' self.head_dim, dtype=torch.float32).uniform_(1, 16)))',
),
(per_channel_gate_code, per_channel_gate_compat_code),
(
''' use_qk_l2norm_in_kernel=True,
use_gate_in_kernel=True,
use_beta_sigmoid_in_kernel=True,''',
''' use_qk_l2norm_in_kernel=True,
use_gate_in_kernel=False,
use_beta_sigmoid_in_kernel=True,''',
),
(
' safe_gate=self.gate_lower_bound is not None,',
' safe_gate=False,',
),
])
replace_file(f'{save_folder}/modeling_kimi_k3.py', [
(' def tie_weights(self):', ' def tie_weights(self, *args, **kwargs):'),
(" _supports_sdpa = True", " _supports_sdpa = False"),
(
' first_layer_past_key_value = past_key_values[0][0][:, :, :, 0]',
''' if hasattr(past_key_values, "key_cache"):
first_key_cache = next(
key_cache for key_cache in past_key_values.key_cache if key_cache is not None
)
first_layer_past_key_value = first_key_cache[:, :, :, 0]
else:
first_layer_past_key_value = past_key_values[0][0][:, :, :, 0]''',
),
])
with open(f'{save_folder}/config.json', encoding='utf-8') as f:
config_json = json.load(f)
# Pure BF16: drop all quantization metadata (no MXFP4 / no quant_method).
config_json['text_config'].pop('quantization_config', None)
config_json.pop('quantization_config', None)
# Preserve the kernel-sensitive dims from upstream: KDA head_dim=128,
# MLA kv_lora_rank=512, qk_nope=128, qk_rope=64, v=128, conv kernel=4.
# vLLM's Kimi fused MLA decode kernel requires latent KV rank 512 and
# cache/query head size 512 + 64 = 576, so only shrink head count and
# q_lora_rank; q_b_proj still drops 54MB->0.75MB.
# Keep the upstream cadence: four 4-layer groups plus the final MLA layer.
# One attention-residual checkpoint every two groups (block_size=8), so
# block boundaries land on layers 0, 8, 16 (0-based).
# Without MXFP4, expert linear dims no longer need group_size=32 / TP
# padding floors; shrink moe_intermediate_size and routed_expert_hidden_size
# so BF16 experts stay small (target total checkpoint <= ~150MB).
config_json['text_config'].update({
'attn_res_block_size': 8,
'first_k_dense_replace': 1,
'hidden_size': 8,
'intermediate_size': 32,
'kv_lora_rank': 512,
'moe_intermediate_size': 32,
'num_attention_heads': 8,
'num_experts': 64,
'num_hidden_layers': 17,
'num_key_value_heads': 8,
'q_lora_rank': 256,
'routed_expert_hidden_size': 32,
'_attn_implementation': 'eager',
})
config_json['text_config']['linear_attn_config'].update({
'full_attn_layers': [4, 8, 12, 16, 17],
'kda_layers': [1, 2, 3, 5, 6, 7, 9, 10, 11, 13, 14, 15],
'num_heads': 8,
})
config_json['vision_config'].update({
'_attn_implementation': 'eager',
'init_pos_emb_height': 8,
'init_pos_emb_width': 8,
'mm_hidden_size': 64,
'qkv_hidden_size': 64,
'text_hidden_size': 8,
'vt_hidden_size': 64,
'vt_intermediate_size': 128,
# Vision attention head size = qkv_hidden_size / heads = 64 / 2 = 32.
'vt_num_attention_heads': 2,
'vt_num_hidden_layers': 2,
})
with open(f'{save_folder}/config.json', 'w', encoding='utf-8') as f:
json.dump(config_json, f, indent=2)
config = AutoConfig.from_pretrained(save_folder, trust_remote_code=True)
print(config)
torch.set_default_dtype(torch.bfloat16)
model = AutoModel.from_config(
config,
trust_remote_code=True,
attn_implementation='eager',
)
torch.set_default_dtype(torch.float32)
if file_exists(
filename='generation_config.json',
repo_id=source_model_id,
repo_type='model',
revision=source_revision,
):
model.generation_config = GenerationConfig.from_pretrained(
source_model_id,
trust_remote_code=True,
revision=source_revision,
)
set_seed(42)
model = model.cpu()
num_params = sum(p.numel() for p in model.parameters())
with torch.no_grad():
for name, parameter in sorted(model.named_parameters()):
torch.nn.init.normal_(parameter, 0, 0.1)
print(name, parameter.shape, parameter.dtype, f'{parameter.numel() / num_params:.2%}')
model.save_pretrained(save_folder)
# Match the official checkpoint schema for non-quantized tensors: keep
# KDA / MoE gate bias buffers in float32.
model_path = Path(save_folder) / 'model.safetensors'
state_dict = load_file(str(model_path))
for name in list(state_dict):
if name.endswith((
'.block_sparse_moe.gate.e_score_correction_bias',
'.self_attn.A_log',
'.self_attn.dt_bias',
'.self_attn.k_conv1d.weight',
'.self_attn.o_norm.weight',
'.self_attn.q_conv1d.weight',
'.self_attn.v_conv1d.weight',
)):
state_dict[name] = state_dict[name].float()
dtype_counts = {
dtype: sum(tensor.dtype == dtype for tensor in state_dict.values())
for dtype in (torch.bfloat16, torch.float32, torch.uint8)
}
text_cfg = config_json['text_config']
num_moe_layers = text_cfg['num_hidden_layers'] - text_cfg['first_k_dense_replace']
num_kda_layers = len(text_cfg['linear_attn_config']['kda_layers'])
expected_f32 = num_moe_layers + num_kda_layers * 6
assert dtype_counts == {
torch.bfloat16: len(state_dict) - expected_f32,
torch.float32: expected_f32,
torch.uint8: 0,
}, dtype_counts
assert not any('mtp' in name.lower() for name in state_dict)
total_bytes = sum(t.numel() * t.element_size() for t in state_dict.values())
print(f'Total checkpoint size: {total_bytes / 1024**2:.2f} MB')
assert total_bytes / 1024**2 <= 150, total_bytes / 1024**2
print('Top 20 keys with largest storage size:')
for name, tensor in sorted(state_dict.items(), key=lambda x: x[1].numel() * x[1].element_size(), reverse=True)[:20]:
print(f'{name}: {tensor.numel()} elements, {tensor.numel() * tensor.element_size() / 1024**2:.2f} MB')
save_file(state_dict, str(model_path), metadata={'format': 'pt'})
```
Click to expand
```text
KimiK3ForConditionalGeneration(
(vision_tower): MoonViT3dPretrainedModel(
(patch_embed): MoonVision3dPatchEmbed(
(proj): Conv2d(3, 64, kernel_size=(14, 14), stride=(14, 14), bias=False)
(pos_emb): Learnable2DInterpPosEmbDivided_fixed()
)
(encoder): MoonViT3dEncoder(
(rope_2d): Rope2DPosEmbRepeated(dim=32, max_height=512, max_width=512, theta_base=10000)
(blocks): ModuleList(
(0-1): 2 x MoonViTEncoderLayer(
(norm0): RMSNorm((64,), eps=None, elementwise_affine=True)
(norm1): RMSNorm((64,), eps=None, elementwise_affine=True)
(mlp): MLP2(
(fc0): Linear(in_features=64, out_features=128, bias=False)
(fc1): Linear(in_features=128, out_features=64, bias=False)
(activation): GELUTanh()
)
(wqkv): Linear(in_features=64, out_features=192, bias=False)
(wo): Linear(in_features=64, out_features=64, bias=False)
)
)
(final_layernorm): RMSNorm((64,), eps=None, elementwise_affine=True)
)
)
(mm_projector): PatchMergerMLPV2(
(proj): Sequential(
(0): Linear(in_features=256, out_features=256, bias=False)
(1): GELU(approximate='none')
(2): Linear(in_features=256, out_features=8, bias=False)
)
(post_norm): RMSNorm((8,), eps=1e-05, elementwise_affine=True)
)
(language_model): KimiLinearForCausalLM(
(model): KimiLinearModel(
(embed_tokens): Embedding(163840, 8, padding_idx=163839)
(layers): ModuleList(
(0): KimiDecoderLayer(
(self_attn): KimiDeltaAttention(
(q_proj): Linear(in_features=8, out_features=1024, bias=False)
(k_proj): Linear(in_features=8, out_features=1024, bias=False)
(v_proj): Linear(in_features=8, out_features=1024, bias=False)
(q_conv1d): ShortConvolution(1024, 1024, kernel_size=(4,), stride=(1,), padding=(3,), groups=1024, bias=False, activation=silu, backend=triton)
(k_conv1d): ShortConvolution(1024, 1024, kernel_size=(4,), stride=(1,), padding=(3,), groups=1024, bias=False, activation=silu, backend=triton)
(v_conv1d): ShortConvolution(1024, 1024, kernel_size=(4,), stride=(1,), padding=(3,), groups=1024, bias=False, activation=silu, backend=triton)
(f_a_proj): Linear(in_features=8, out_features=128, bias=False)
(f_b_proj): Linear(in_features=128, out_features=1024, bias=False)
(b_proj): Linear(in_features=8, out_features=8, bias=False)
(g_proj): Linear(in_features=8, out_features=1024, bias=False)
(o_norm): FusedRMSNormGated(128, eps=1e-05, activation=sigmoid)
(o_proj): Linear(in_features=1024, out_features=8, bias=False)
)
(mlp): KimiMLP(
(gate_proj): Linear(in_features=8, out_features=32, bias=False)
(up_proj): Linear(in_features=8, out_features=32, bias=False)
(down_proj): Linear(in_features=32, out_features=8, bias=False)
(act_fn): SituAndMul()
)
(input_layernorm): KimiRMSNorm()
(post_attention_layernorm): KimiRMSNorm()
(self_attention_res_norm): KimiRMSNorm()
(mlp_res_norm): KimiRMSNorm()
(self_attention_res_proj): Linear(in_features=8, out_features=1, bias=False)
(mlp_res_proj): Linear(in_features=8, out_features=1, bias=False)
)
(1-2): 2 x KimiDecoderLayer(
(self_attn): KimiDeltaAttention(
(q_proj): Linear(in_features=8, out_features=1024, bias=False)
(k_proj): Linear(in_features=8, out_features=1024, bias=False)
(v_proj): Linear(in_features=8, out_features=1024, bias=False)
(q_conv1d): ShortConvolution(1024, 1024, kernel_size=(4,), stride=(1,), padding=(3,), groups=1024, bias=False, activation=silu, backend=triton)
(k_conv1d): ShortConvolution(1024, 1024, kernel_size=(4,), stride=(1,), padding=(3,), groups=1024, bias=False, activation=silu, backend=triton)
(v_conv1d): ShortConvolution(1024, 1024, kernel_size=(4,), stride=(1,), padding=(3,), groups=1024, bias=False, activation=silu, backend=triton)
(f_a_proj): Linear(in_features=8, out_features=128, bias=False)
(f_b_proj): Linear(in_features=128, out_features=1024, bias=False)
(b_proj): Linear(in_features=8, out_features=8, bias=False)
(g_proj): Linear(in_features=8, out_features=1024, bias=False)
(o_norm): FusedRMSNormGated(128, eps=1e-05, activation=sigmoid)
(o_proj): Linear(in_features=1024, out_features=8, bias=False)
)
(block_sparse_moe): KimiSparseMoeBlock(
(experts): ModuleList(
(0-63): 64 x KimiBlockSparseMLP(
(w1): Linear(in_features=32, out_features=32, bias=False)
(w2): Linear(in_features=32, out_features=32, bias=False)
(w3): Linear(in_features=32, out_features=32, bias=False)
(act_fn): SituAndMul()
)
)
(gate): KimiMoEGate()
(shared_experts): KimiMLP(
(gate_proj): Linear(in_features=8, out_features=64, bias=False)
(up_proj): Linear(in_features=8, out_features=64, bias=False)
(down_proj): Linear(in_features=64, out_features=8, bias=False)
(act_fn): SituAndMul()
)
(routed_expert_down_proj): Linear(in_features=8, out_features=32, bias=False)
(routed_expert_up_proj): Linear(in_features=32, out_features=8, bias=False)
(routed_expert_norm): KimiRMSNorm()
)
(input_layernorm): KimiRMSNorm()
(post_attention_layernorm): KimiRMSNorm()
(self_attention_res_norm): KimiRMSNorm()
(mlp_res_norm): KimiRMSNorm()
(self_attention_res_proj): Linear(in_features=8, out_features=1, bias=False)
(mlp_res_proj): Linear(in_features=8, out_features=1, bias=False)
)
(3): KimiDecoderLayer(
(self_attn): KimiMLAAttention(
(q_a_proj): Linear(in_features=8, out_features=256, bias=False)
(q_a_layernorm): KimiRMSNorm()
(q_b_proj): Linear(in_features=256, out_features=1536, bias=False)
(kv_a_proj_with_mqa): Linear(in_features=8, out_features=576, bias=False)
(kv_a_layernorm): KimiRMSNorm()
(kv_b_proj): Linear(in_features=512, out_features=2048, bias=False)
(o_proj): Linear(in_features=1024, out_features=8, bias=False)
(g_proj): Linear(in_features=8, out_features=1024, bias=False)
)
(block_sparse_moe): KimiSparseMoeBlock(
(experts): ModuleList(
(0-63): 64 x KimiBlockSparseMLP(
(w1): Linear(in_features=32, out_features=32, bias=False)
(w2): Linear(in_features=32, out_features=32, bias=False)
(w3): Linear(in_features=32, out_features=32, bias=False)
(act_fn): SituAndMul()
)
)
(gate): KimiMoEGate()
(shared_experts): KimiMLP(
(gate_proj): Linear(in_features=8, out_features=64, bias=False)
(up_proj): Linear(in_features=8, out_features=64, bias=False)
(down_proj): Linear(in_features=64, out_features=8, bias=False)
(act_fn): SituAndMul()
)
(routed_expert_down_proj): Linear(in_features=8, out_features=32, bias=False)
(routed_expert_up_proj): Linear(in_features=32, out_features=8, bias=False)
(routed_expert_norm): KimiRMSNorm()
)
(input_layernorm): KimiRMSNorm()
(post_attention_layernorm): KimiRMSNorm()
(self_attention_res_norm): KimiRMSNorm()
(mlp_res_norm): KimiRMSNorm()
(self_attention_res_proj): Linear(in_features=8, out_features=1, bias=False)
(mlp_res_proj): Linear(in_features=8, out_features=1, bias=False)
)
(4-6): 3 x KimiDecoderLayer(
(self_attn): KimiDeltaAttention(
(q_proj): Linear(in_features=8, out_features=1024, bias=False)
(k_proj): Linear(in_features=8, out_features=1024, bias=False)
(v_proj): Linear(in_features=8, out_features=1024, bias=False)
(q_conv1d): ShortConvolution(1024, 1024, kernel_size=(4,), stride=(1,), padding=(3,), groups=1024, bias=False, activation=silu, backend=triton)
(k_conv1d): ShortConvolution(1024, 1024, kernel_size=(4,), stride=(1,), padding=(3,), groups=1024, bias=False, activation=silu, backend=triton)
(v_conv1d): ShortConvolution(1024, 1024, kernel_size=(4,), stride=(1,), padding=(3,), groups=1024, bias=False, activation=silu, backend=triton)
(f_a_proj): Linear(in_features=8, out_features=128, bias=False)
(f_b_proj): Linear(in_features=128, out_features=1024, bias=False)
(b_proj): Linear(in_features=8, out_features=8, bias=False)
(g_proj): Linear(in_features=8, out_features=1024, bias=False)
(o_norm): FusedRMSNormGated(128, eps=1e-05, activation=sigmoid)
(o_proj): Linear(in_features=1024, out_features=8, bias=False)
)
(block_sparse_moe): KimiSparseMoeBlock(
(experts): ModuleList(
(0-63): 64 x KimiBlockSparseMLP(
(w1): Linear(in_features=32, out_features=32, bias=False)
(w2): Linear(in_features=32, out_features=32, bias=False)
(w3): Linear(in_features=32, out_features=32, bias=False)
(act_fn): SituAndMul()
)
)
(gate): KimiMoEGate()
(shared_experts): KimiMLP(
(gate_proj): Linear(in_features=8, out_features=64, bias=False)
(up_proj): Linear(in_features=8, out_features=64, bias=False)
(down_proj): Linear(in_features=64, out_features=8, bias=False)
(act_fn): SituAndMul()
)
(routed_expert_down_proj): Linear(in_features=8, out_features=32, bias=False)
(routed_expert_up_proj): Linear(in_features=32, out_features=8, bias=False)
(routed_expert_norm): KimiRMSNorm()
)
(input_layernorm): KimiRMSNorm()
(post_attention_layernorm): KimiRMSNorm()
(self_attention_res_norm): KimiRMSNorm()
(mlp_res_norm): KimiRMSNorm()
(self_attention_res_proj): Linear(in_features=8, out_features=1, bias=False)
(mlp_res_proj): Linear(in_features=8, out_features=1, bias=False)
)
(7): KimiDecoderLayer(
(self_attn): KimiMLAAttention(
(q_a_proj): Linear(in_features=8, out_features=256, bias=False)
(q_a_layernorm): KimiRMSNorm()
(q_b_proj): Linear(in_features=256, out_features=1536, bias=False)
(kv_a_proj_with_mqa): Linear(in_features=8, out_features=576, bias=False)
(kv_a_layernorm): KimiRMSNorm()
(kv_b_proj): Linear(in_features=512, out_features=2048, bias=False)
(o_proj): Linear(in_features=1024, out_features=8, bias=False)
(g_proj): Linear(in_features=8, out_features=1024, bias=False)
)
(block_sparse_moe): KimiSparseMoeBlock(
(experts): ModuleList(
(0-63): 64 x KimiBlockSparseMLP(
(w1): Linear(in_features=32, out_features=32, bias=False)
(w2): Linear(in_features=32, out_features=32, bias=False)
(w3): Linear(in_features=32, out_features=32, bias=False)
(act_fn): SituAndMul()
)
)
(gate): KimiMoEGate()
(shared_experts): KimiMLP(
(gate_proj): Linear(in_features=8, out_features=64, bias=False)
(up_proj): Linear(in_features=8, out_features=64, bias=False)
(down_proj): Linear(in_features=64, out_features=8, bias=False)
(act_fn): SituAndMul()
)
(routed_expert_down_proj): Linear(in_features=8, out_features=32, bias=False)
(routed_expert_up_proj): Linear(in_features=32, out_features=8, bias=False)
(routed_expert_norm): KimiRMSNorm()
)
(input_layernorm): KimiRMSNorm()
(post_attention_layernorm): KimiRMSNorm()
(self_attention_res_norm): KimiRMSNorm()
(mlp_res_norm): KimiRMSNorm()
(self_attention_res_proj): Linear(in_features=8, out_features=1, bias=False)
(mlp_res_proj): Linear(in_features=8, out_features=1, bias=False)
)
(8-10): 3 x KimiDecoderLayer(
(self_attn): KimiDeltaAttention(
(q_proj): Linear(in_features=8, out_features=1024, bias=False)
(k_proj): Linear(in_features=8, out_features=1024, bias=False)
(v_proj): Linear(in_features=8, out_features=1024, bias=False)
(q_conv1d): ShortConvolution(1024, 1024, kernel_size=(4,), stride=(1,), padding=(3,), groups=1024, bias=False, activation=silu, backend=triton)
(k_conv1d): ShortConvolution(1024, 1024, kernel_size=(4,), stride=(1,), padding=(3,), groups=1024, bias=False, activation=silu, backend=triton)
(v_conv1d): ShortConvolution(1024, 1024, kernel_size=(4,), stride=(1,), padding=(3,), groups=1024, bias=False, activation=silu, backend=triton)
(f_a_proj): Linear(in_features=8, out_features=128, bias=False)
(f_b_proj): Linear(in_features=128, out_features=1024, bias=False)
(b_proj): Linear(in_features=8, out_features=8, bias=False)
(g_proj): Linear(in_features=8, out_features=1024, bias=False)
(o_norm): FusedRMSNormGated(128, eps=1e-05, activation=sigmoid)
(o_proj): Linear(in_features=1024, out_features=8, bias=False)
)
(block_sparse_moe): KimiSparseMoeBlock(
(experts): ModuleList(
(0-63): 64 x KimiBlockSparseMLP(
(w1): Linear(in_features=32, out_features=32, bias=False)
(w2): Linear(in_features=32, out_features=32, bias=False)
(w3): Linear(in_features=32, out_features=32, bias=False)
(act_fn): SituAndMul()
)
)
(gate): KimiMoEGate()
(shared_experts): KimiMLP(
(gate_proj): Linear(in_features=8, out_features=64, bias=False)
(up_proj): Linear(in_features=8, out_features=64, bias=False)
(down_proj): Linear(in_features=64, out_features=8, bias=False)
(act_fn): SituAndMul()
)
(routed_expert_down_proj): Linear(in_features=8, out_features=32, bias=False)
(routed_expert_up_proj): Linear(in_features=32, out_features=8, bias=False)
(routed_expert_norm): KimiRMSNorm()
)
(input_layernorm): KimiRMSNorm()
(post_attention_layernorm): KimiRMSNorm()
(self_attention_res_norm): KimiRMSNorm()
(mlp_res_norm): KimiRMSNorm()
(self_attention_res_proj): Linear(in_features=8, out_features=1, bias=False)
(mlp_res_proj): Linear(in_features=8, out_features=1, bias=False)
)
(11): KimiDecoderLayer(
(self_attn): KimiMLAAttention(
(q_a_proj): Linear(in_features=8, out_features=256, bias=False)
(q_a_layernorm): KimiRMSNorm()
(q_b_proj): Linear(in_features=256, out_features=1536, bias=False)
(kv_a_proj_with_mqa): Linear(in_features=8, out_features=576, bias=False)
(kv_a_layernorm): KimiRMSNorm()
(kv_b_proj): Linear(in_features=512, out_features=2048, bias=False)
(o_proj): Linear(in_features=1024, out_features=8, bias=False)
(g_proj): Linear(in_features=8, out_features=1024, bias=False)
)
(block_sparse_moe): KimiSparseMoeBlock(
(experts): ModuleList(
(0-63): 64 x KimiBlockSparseMLP(
(w1): Linear(in_features=32, out_features=32, bias=False)
(w2): Linear(in_features=32, out_features=32, bias=False)
(w3): Linear(in_features=32, out_features=32, bias=False)
(act_fn): SituAndMul()
)
)
(gate): KimiMoEGate()
(shared_experts): KimiMLP(
(gate_proj): Linear(in_features=8, out_features=64, bias=False)
(up_proj): Linear(in_features=8, out_features=64, bias=False)
(down_proj): Linear(in_features=64, out_features=8, bias=False)
(act_fn): SituAndMul()
)
(routed_expert_down_proj): Linear(in_features=8, out_features=32, bias=False)
(routed_expert_up_proj): Linear(in_features=32, out_features=8, bias=False)
(routed_expert_norm): KimiRMSNorm()
)
(input_layernorm): KimiRMSNorm()
(post_attention_layernorm): KimiRMSNorm()
(self_attention_res_norm): KimiRMSNorm()
(mlp_res_norm): KimiRMSNorm()
(self_attention_res_proj): Linear(in_features=8, out_features=1, bias=False)
(mlp_res_proj): Linear(in_features=8, out_features=1, bias=False)
)
(12-14): 3 x KimiDecoderLayer(
(self_attn): KimiDeltaAttention(
(q_proj): Linear(in_features=8, out_features=1024, bias=False)
(k_proj): Linear(in_features=8, out_features=1024, bias=False)
(v_proj): Linear(in_features=8, out_features=1024, bias=False)
(q_conv1d): ShortConvolution(1024, 1024, kernel_size=(4,), stride=(1,), padding=(3,), groups=1024, bias=False, activation=silu, backend=triton)
(k_conv1d): ShortConvolution(1024, 1024, kernel_size=(4,), stride=(1,), padding=(3,), groups=1024, bias=False, activation=silu, backend=triton)
(v_conv1d): ShortConvolution(1024, 1024, kernel_size=(4,), stride=(1,), padding=(3,), groups=1024, bias=False, activation=silu, backend=triton)
(f_a_proj): Linear(in_features=8, out_features=128, bias=False)
(f_b_proj): Linear(in_features=128, out_features=1024, bias=False)
(b_proj): Linear(in_features=8, out_features=8, bias=False)
(g_proj): Linear(in_features=8, out_features=1024, bias=False)
(o_norm): FusedRMSNormGated(128, eps=1e-05, activation=sigmoid)
(o_proj): Linear(in_features=1024, out_features=8, bias=False)
)
(block_sparse_moe): KimiSparseMoeBlock(
(experts): ModuleList(
(0-63): 64 x KimiBlockSparseMLP(
(w1): Linear(in_features=32, out_features=32, bias=False)
(w2): Linear(in_features=32, out_features=32, bias=False)
(w3): Linear(in_features=32, out_features=32, bias=False)
(act_fn): SituAndMul()
)
)
(gate): KimiMoEGate()
(shared_experts): KimiMLP(
(gate_proj): Linear(in_features=8, out_features=64, bias=False)
(up_proj): Linear(in_features=8, out_features=64, bias=False)
(down_proj): Linear(in_features=64, out_features=8, bias=False)
(act_fn): SituAndMul()
)
(routed_expert_down_proj): Linear(in_features=8, out_features=32, bias=False)
(routed_expert_up_proj): Linear(in_features=32, out_features=8, bias=False)
(routed_expert_norm): KimiRMSNorm()
)
(input_layernorm): KimiRMSNorm()
(post_attention_layernorm): KimiRMSNorm()
(self_attention_res_norm): KimiRMSNorm()
(mlp_res_norm): KimiRMSNorm()
(self_attention_res_proj): Linear(in_features=8, out_features=1, bias=False)
(mlp_res_proj): Linear(in_features=8, out_features=1, bias=False)
)
(15-16): 2 x KimiDecoderLayer(
(self_attn): KimiMLAAttention(
(q_a_proj): Linear(in_features=8, out_features=256, bias=False)
(q_a_layernorm): KimiRMSNorm()
(q_b_proj): Linear(in_features=256, out_features=1536, bias=False)
(kv_a_proj_with_mqa): Linear(in_features=8, out_features=576, bias=False)
(kv_a_layernorm): KimiRMSNorm()
(kv_b_proj): Linear(in_features=512, out_features=2048, bias=False)
(o_proj): Linear(in_features=1024, out_features=8, bias=False)
(g_proj): Linear(in_features=8, out_features=1024, bias=False)
)
(block_sparse_moe): KimiSparseMoeBlock(
(experts): ModuleList(
(0-63): 64 x KimiBlockSparseMLP(
(w1): Linear(in_features=32, out_features=32, bias=False)
(w2): Linear(in_features=32, out_features=32, bias=False)
(w3): Linear(in_features=32, out_features=32, bias=False)
(act_fn): SituAndMul()
)
)
(gate): KimiMoEGate()
(shared_experts): KimiMLP(
(gate_proj): Linear(in_features=8, out_features=64, bias=False)
(up_proj): Linear(in_features=8, out_features=64, bias=False)
(down_proj): Linear(in_features=64, out_features=8, bias=False)
(act_fn): SituAndMul()
)
(routed_expert_down_proj): Linear(in_features=8, out_features=32, bias=False)
(routed_expert_up_proj): Linear(in_features=32, out_features=8, bias=False)
(routed_expert_norm): KimiRMSNorm()
)
(input_layernorm): KimiRMSNorm()
(post_attention_layernorm): KimiRMSNorm()
(self_attention_res_norm): KimiRMSNorm()
(mlp_res_norm): KimiRMSNorm()
(self_attention_res_proj): Linear(in_features=8, out_features=1, bias=False)
(mlp_res_proj): Linear(in_features=8, out_features=1, bias=False)
)
)
(norm): KimiRMSNorm()
(output_attn_res_norm): KimiRMSNorm()
(output_attn_res_proj): Linear(in_features=8, out_features=1, bias=False)
)
(lm_head): Linear(in_features=8, out_features=163840, bias=False)
)
)
```