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---
license: other
library_name: transformers
base_model: Qwen/Qwen2.5-14B-Instruct
tags:
- nanollm
- qwen2.5
- safetensors
- text-generation
---
# NanoLLM Qwen2.5-14B-Instruct v3.1
Compact self-contained NanoLLM format is in `nano_compact/`.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
repo_id = "RthItalia/NanoLLM-Qwen2.5-14B-v3.1"
tokenizer = AutoTokenizer.from_pretrained(repo_id, subfolder="nano_compact", use_fast=True)
model = AutoModelForCausalLM.from_pretrained(repo_id, subfolder="nano_compact", trust_remote_code=True, device_map="auto")
```
Validation against 8-bit reference:
- avg cosine: 0.98984375
- min cosine: 0.9765625
- gate: avg >= 0.985
`nano_compact/model.safetensors` contains Nano quantized tensors and does not require downloading the Qwen base weights.

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{
"architectures": [
"Qwen2ForCausalLM"
],
"attention_dropout": 0.0,
"bos_token_id": 151643,
"eos_token_id": 151645,
"hidden_act": "silu",
"hidden_size": 5120,
"initializer_range": 0.02,
"intermediate_size": 13824,
"max_position_embeddings": 32768,
"max_window_layers": 70,
"model_type": "qwen2",
"num_attention_heads": 40,
"num_hidden_layers": 48,
"num_key_value_heads": 8,
"rms_norm_eps": 1e-06,
"rope_theta": 1000000.0,
"sliding_window": 131072,
"tie_word_embeddings": false,
"torch_dtype": "bfloat16",
"transformers_version": "4.43.1",
"use_cache": true,
"use_sliding_window": false,
"vocab_size": 152064
}

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{
"bos_token_id": 151643,
"pad_token_id": 151643,
"do_sample": true,
"eos_token_id": [
151645,
151643
],
"repetition_penalty": 1.05,
"temperature": 0.7,
"top_p": 0.8,
"top_k": 20,
"transformers_version": "4.37.0"
}

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"""Loader NANO-v3.1 UNIVERSAL (Inference Only)"""
import os
import json
from pathlib import Path
import torch
import torch.nn as nn
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
class TrueQuantLinear(nn.Module):
def __init__(self, pq, ps, pi, dq, ds, di, out_features, bias=None, bits=8, device="cuda:0"):
super().__init__()
self.out_features = out_features
self.bits = int(bits)
self.register_buffer("pq", pq.to(device=device, dtype=torch.int8))
self.register_buffer("ps", ps.to(device=device, dtype=torch.float16))
self.register_buffer("pi", pi.to(device=device, dtype=torch.long))
self.register_buffer("dq", dq.to(device=device, dtype=torch.int8))
self.register_buffer("ds", ds.to(device=device, dtype=torch.float16))
self.register_buffer("di", di.to(device=device, dtype=torch.long))
if bias is not None:
self.register_buffer("bias", bias.to(device=device, dtype=torch.float16))
else:
self.bias = None
def forward(self, x):
d, dt = x.device, x.dtype
f = x.to(torch.float16).reshape(-1, x.shape[-1])
o = torch.zeros(f.shape[0], self.out_features, dtype=torch.float16, device=d)
if self.pq.shape[0] > 0:
o.index_copy_(-1, self.pi.to(d), f @ (self.pq.to(d, torch.float16) * self.ps.to(d).unsqueeze(1)).t())
if self.dq.shape[0] > 0:
o.index_copy_(-1, self.di.to(d), f @ (self.dq.to(d, torch.float16) * self.ds.to(d).unsqueeze(1)).t())
if self.bias is not None:
o = o + self.bias.to(d)
return o.reshape(*x.shape[:-1], self.out_features).to(dt)
def _set(root, name, value):
parts = name.split(".")
parent = root
for p in parts[:-1]:
parent = parent[int(p)] if p.isdigit() else getattr(parent, p)
if parts[-1].isdigit():
parent[int(parts[-1])] = value
else:
setattr(parent, parts[-1], value)
def get_module(root, name):
cur = root
for p in name.split("."):
cur = cur[int(p)] if p.isdigit() else getattr(cur, p)
return cur
def load_artifact(artifact_dir):
d = Path(artifact_dir)
spec = json.loads((d / "spec.json").read_text("utf-8"))
state = torch.load(d / "quantized_modules.pt", map_location="cpu")
use_4bit = os.getenv("NANO_LOAD_4BIT", "0").strip().lower() in {"1", "true", "yes", "on"}
qcfg = (
BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_compute_dtype=torch.float16,
)
if use_4bit
else BitsAndBytesConfig(load_in_8bit=True)
)
model = AutoModelForCausalLM.from_pretrained(
str(d),
quantization_config=qcfg,
device_map="auto",
)
tokenizer = AutoTokenizer.from_pretrained(str(d), use_fast=True)
if tokenizer.pad_token_id is None:
tokenizer.pad_token = tokenizer.eos_token
for name, s in state.items():
dev = next(get_module(model, name).parameters()).device
bits = s["bits"]
if "deg_q_packed" in s:
pk, pad = s["deg_q_packed"], s["pad"]
if bits == 2:
dq = torch.stack([pk & 3, (pk >> 2) & 3, (pk >> 4) & 3, (pk >> 6) & 3], dim=-1).view(pk.shape[0], -1)
if pad > 0:
dq = dq[:, :-pad]
dq = dq.to(torch.int8) - 1
else:
dq = torch.stack([pk & 15, (pk >> 4) & 15], dim=-1).view(pk.shape[0], -1)
if pad > 0:
dq = dq[:, :-pad]
dq = dq.to(torch.int8) - 7
else:
dq = s.get("deg_q", torch.zeros(0, dtype=torch.int8))
_set(
model,
name,
TrueQuantLinear(
s["prot_q"],
s["prot_scale"],
s["prot_idx"],
dq,
s["deg_scale"],
s["deg_idx"],
s["out_features"],
s.get("bias"),
bits,
device=str(dev),
),
)
return model.eval(), tokenizer, spec

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import torch
import torch.nn as nn
from transformers.models.qwen2.configuration_qwen2 import Qwen2Config
from transformers.models.qwen2.modeling_qwen2 import Qwen2ForCausalLM
class NanoInt8Linear(nn.Module):
def __init__(self, in_features, out_features, has_bias=False):
super().__init__()
self.in_features = int(in_features)
self.out_features = int(out_features)
self.has_bias = bool(has_bias)
self.register_buffer("q", torch.empty((self.out_features, self.in_features), dtype=torch.int8))
self.register_buffer("scale", torch.empty((self.out_features,), dtype=torch.float16))
if self.has_bias:
self.register_buffer("bias", torch.empty((self.out_features,), dtype=torch.float16))
def forward(self, x):
dt = x.dtype
f = x.to(torch.float16).reshape(-1, x.shape[-1])
w = self.q.to(f.device, torch.float16) * self.scale.to(f.device).unsqueeze(1)
y = f @ w.t()
if self.has_bias:
y = y + self.bias.to(f.device)
return y.reshape(*x.shape[:-1], self.out_features).to(dt)
class NanoTrueQuantLinear(nn.Module):
def __init__(self, in_features, out_features, prot_rows, deg_rows, has_bias=False):
super().__init__()
self.in_features = int(in_features)
self.out_features = int(out_features)
self.has_bias = bool(has_bias)
self.register_buffer("prot_q", torch.empty((prot_rows, self.in_features), dtype=torch.int8))
self.register_buffer("prot_scale", torch.empty((prot_rows,), dtype=torch.float16))
self.register_buffer("prot_idx", torch.empty((prot_rows,), dtype=torch.long))
self.register_buffer("deg_q", torch.empty((deg_rows, self.in_features), dtype=torch.int8))
self.register_buffer("deg_scale", torch.empty((deg_rows,), dtype=torch.float16))
self.register_buffer("deg_idx", torch.empty((deg_rows,), dtype=torch.long))
if self.has_bias:
self.register_buffer("bias", torch.empty((self.out_features,), dtype=torch.float16))
def forward(self, x):
dt = x.dtype
f = x.to(torch.float16).reshape(-1, x.shape[-1])
y = torch.zeros((f.shape[0], self.out_features), dtype=torch.float16, device=f.device)
if self.prot_q.shape[0] > 0:
w = self.prot_q.to(f.device, torch.float16) * self.prot_scale.to(f.device).unsqueeze(1)
y.index_copy_(-1, self.prot_idx.to(f.device), f @ w.t())
if self.deg_q.shape[0] > 0:
w = self.deg_q.to(f.device, torch.float16) * self.deg_scale.to(f.device).unsqueeze(1)
y.index_copy_(-1, self.deg_idx.to(f.device), f @ w.t())
if self.has_bias:
y = y + self.bias.to(f.device)
return y.reshape(*x.shape[:-1], self.out_features).to(dt)
class NanoEmbedding(nn.Module):
def __init__(self, num_embeddings, embedding_dim):
super().__init__()
self.num_embeddings = int(num_embeddings)
self.embedding_dim = int(embedding_dim)
self.register_buffer("q", torch.empty((self.num_embeddings, self.embedding_dim), dtype=torch.int8))
self.register_buffer("scale", torch.empty((self.num_embeddings,), dtype=torch.float16))
def forward(self, input_ids):
return self.q[input_ids].to(torch.float16) * self.scale[input_ids].to(torch.float16).unsqueeze(-1)
class NanoTiedLMHead(nn.Module):
def __init__(self, embedding):
super().__init__()
self.register_buffer("q", embedding.q.detach().clone())
self.register_buffer("scale", embedding.scale.detach().clone())
def forward(self, x):
w = self.q.to(x.device, torch.float16) * self.scale.to(x.device).unsqueeze(1)
return x.to(torch.float16) @ w.t()
def _set_module(root, name, module):
cur = root
parts = name.split(".")
for p in parts[:-1]:
cur = cur[int(p)] if p.isdigit() else getattr(cur, p)
setattr(cur, parts[-1], module)
class NanoQwenForCausalLM(Qwen2ForCausalLM):
config_class = Qwen2Config
def tie_weights(self, *args, **kwargs):
return None
def mark_tied_weights_as_initialized(self, *args, **kwargs):
return None
def __init__(self, config):
config.tie_word_embeddings = False
super().__init__(config)
self.config.tie_word_embeddings = False
self._tied_weights_keys = []
self.all_tied_weights_keys = {}
mods = getattr(config, "nanollm_modules", {})
for name, spec in mods.items():
kind = spec["kind"]
if kind == "embedding":
mod = NanoEmbedding(spec["num_embeddings"], spec["embedding_dim"])
elif kind == "int8_linear":
mod = NanoInt8Linear(spec["in_features"], spec["out_features"], spec.get("has_bias", False))
elif kind == "truequant_linear":
mod = NanoTrueQuantLinear(
spec["in_features"], spec["out_features"],
spec["prot_rows"], spec["deg_rows"],
spec.get("has_bias", False),
)
else:
raise ValueError(f"Unknown Nano module kind: {kind}")
_set_module(self, name, mod)
if "lm_head" not in mods and isinstance(self.model.embed_tokens, NanoEmbedding):
self.lm_head = NanoTiedLMHead(self.model.embed_tokens)

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{%- if tools %}
{{- '<|im_start|>system\n' }}
{%- if messages[0]['role'] == 'system' %}
{{- messages[0]['content'] }}
{%- else %}
{{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
{%- endif %}
{{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
{%- for tool in tools %}
{{- "\n" }}
{{- tool | tojson }}
{%- endfor %}
{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
{%- else %}
{%- if messages[0]['role'] == 'system' %}
{{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
{%- else %}
{{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
{%- endif %}
{%- endif %}
{%- for message in messages %}
{%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
{%- elif message.role == "assistant" %}
{{- '<|im_start|>' + message.role }}
{%- if message.content %}
{{- '\n' + message.content }}
{%- endif %}
{%- for tool_call in message.tool_calls %}
{%- if tool_call.function is defined %}
{%- set tool_call = tool_call.function %}
{%- endif %}
{{- '\n<tool_call>\n{"name": "' }}
{{- tool_call.name }}
{{- '", "arguments": ' }}
{{- tool_call.arguments | tojson }}
{{- '}\n</tool_call>' }}
{%- endfor %}
{{- '<|im_end|>\n' }}
{%- elif message.role == "tool" %}
{%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
{{- '<|im_start|>user' }}
{%- endif %}
{{- '\n<tool_response>\n' }}
{{- message.content }}
{{- '\n</tool_response>' }}
{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
{{- '<|im_end|>\n' }}
{%- endif %}
{%- endif %}
{%- endfor %}
{%- if add_generation_prompt %}
{{- '<|im_start|>assistant\n' }}
{%- endif %}

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version https://git-lfs.github.com/spec/v1
oid sha256:d052d5b20bebde4836c9559d5c9baf6b65ad2af02bb10da9d23a7416a10e6970
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import torch
import torch.nn as nn
from transformers.models.qwen2.configuration_qwen2 import Qwen2Config
from transformers.models.qwen2.modeling_qwen2 import Qwen2ForCausalLM
class NanoInt8Linear(nn.Module):
def __init__(self, in_features, out_features, has_bias=False):
super().__init__()
self.in_features = int(in_features)
self.out_features = int(out_features)
self.has_bias = bool(has_bias)
self.register_buffer("q", torch.empty((self.out_features, self.in_features), dtype=torch.int8))
self.register_buffer("scale", torch.empty((self.out_features,), dtype=torch.float16))
if self.has_bias:
self.register_buffer("bias", torch.empty((self.out_features,), dtype=torch.float16))
def forward(self, x):
dt = x.dtype
f = x.to(torch.float16).reshape(-1, x.shape[-1])
w = self.q.to(f.device, torch.float16) * self.scale.to(f.device).unsqueeze(1)
y = f @ w.t()
if self.has_bias:
y = y + self.bias.to(f.device)
return y.reshape(*x.shape[:-1], self.out_features).to(dt)
class NanoTrueQuantLinear(nn.Module):
def __init__(self, in_features, out_features, prot_rows, deg_rows, has_bias=False):
super().__init__()
self.in_features = int(in_features)
self.out_features = int(out_features)
self.has_bias = bool(has_bias)
self.register_buffer("prot_q", torch.empty((prot_rows, self.in_features), dtype=torch.int8))
self.register_buffer("prot_scale", torch.empty((prot_rows,), dtype=torch.float16))
self.register_buffer("prot_idx", torch.empty((prot_rows,), dtype=torch.long))
self.register_buffer("deg_q", torch.empty((deg_rows, self.in_features), dtype=torch.int8))
self.register_buffer("deg_scale", torch.empty((deg_rows,), dtype=torch.float16))
self.register_buffer("deg_idx", torch.empty((deg_rows,), dtype=torch.long))
if self.has_bias:
self.register_buffer("bias", torch.empty((self.out_features,), dtype=torch.float16))
def forward(self, x):
dt = x.dtype
f = x.to(torch.float16).reshape(-1, x.shape[-1])
y = torch.zeros((f.shape[0], self.out_features), dtype=torch.float16, device=f.device)
if self.prot_q.shape[0] > 0:
w = self.prot_q.to(f.device, torch.float16) * self.prot_scale.to(f.device).unsqueeze(1)
y.index_copy_(-1, self.prot_idx.to(f.device), f @ w.t())
if self.deg_q.shape[0] > 0:
w = self.deg_q.to(f.device, torch.float16) * self.deg_scale.to(f.device).unsqueeze(1)
y.index_copy_(-1, self.deg_idx.to(f.device), f @ w.t())
if self.has_bias:
y = y + self.bias.to(f.device)
return y.reshape(*x.shape[:-1], self.out_features).to(dt)
class NanoEmbedding(nn.Module):
def __init__(self, num_embeddings, embedding_dim):
super().__init__()
self.num_embeddings = int(num_embeddings)
self.embedding_dim = int(embedding_dim)
self.register_buffer("q", torch.empty((self.num_embeddings, self.embedding_dim), dtype=torch.int8))
self.register_buffer("scale", torch.empty((self.num_embeddings,), dtype=torch.float16))
def forward(self, input_ids):
return self.q[input_ids].to(torch.float16) * self.scale[input_ids].to(torch.float16).unsqueeze(-1)
class NanoTiedLMHead(nn.Module):
def __init__(self, embedding):
super().__init__()
self.register_buffer("q", embedding.q.detach().clone())
self.register_buffer("scale", embedding.scale.detach().clone())
def forward(self, x):
w = self.q.to(x.device, torch.float16) * self.scale.to(x.device).unsqueeze(1)
return x.to(torch.float16) @ w.t()
def _set_module(root, name, module):
cur = root
parts = name.split(".")
for p in parts[:-1]:
cur = cur[int(p)] if p.isdigit() else getattr(cur, p)
setattr(cur, parts[-1], module)
class NanoQwenForCausalLM(Qwen2ForCausalLM):
config_class = Qwen2Config
def tie_weights(self, *args, **kwargs):
return None
def mark_tied_weights_as_initialized(self, *args, **kwargs):
return None
def __init__(self, config):
config.tie_word_embeddings = False
super().__init__(config)
self.config.tie_word_embeddings = False
self._tied_weights_keys = []
self.all_tied_weights_keys = {}
mods = getattr(config, "nanollm_modules", {})
for name, spec in mods.items():
kind = spec["kind"]
if kind == "embedding":
mod = NanoEmbedding(spec["num_embeddings"], spec["embedding_dim"])
elif kind == "int8_linear":
mod = NanoInt8Linear(spec["in_features"], spec["out_features"], spec.get("has_bias", False))
elif kind == "truequant_linear":
mod = NanoTrueQuantLinear(
spec["in_features"], spec["out_features"],
spec["prot_rows"], spec["deg_rows"],
spec.get("has_bias", False),
)
else:
raise ValueError(f"Unknown Nano module kind: {kind}")
_set_module(self, name, mod)
if "lm_head" not in mods and isinstance(self.model.embed_tokens, NanoEmbedding):
self.lm_head = NanoTiedLMHead(self.model.embed_tokens)

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{
"format": "compact-safetensors-v1",
"base_model_id": "Qwen/Qwen2.5-14B-Instruct",
"artifact_dir": "/workspace/nano_rebuild/runs_14b/099/final_artifact_Qwen2.5-14B-Instruct",
"requires_trust_remote_code": true
}

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version https://git-lfs.github.com/spec/v1
oid sha256:3fd169731d2cbde95e10bf356d66d5997fd885dd8dbb6fb4684da3f23b2585d8
size 11421892

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nano_compact/tokenizer_config.json Normal file
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{
"add_prefix_space": false,
"backend": "tokenizers",
"bos_token": null,
"clean_up_tokenization_spaces": false,
"eos_token": "<|im_end|>",
"errors": "replace",
"extra_special_tokens": [
"<|im_start|>",
"<|im_end|>",
"<|object_ref_start|>",
"<|object_ref_end|>",
"<|box_start|>",
"<|box_end|>",
"<|quad_start|>",
"<|quad_end|>",
"<|vision_start|>",
"<|vision_end|>",
"<|vision_pad|>",
"<|image_pad|>",
"<|video_pad|>"
],
"is_local": true,
"local_files_only": false,
"model_max_length": 131072,
"pad_token": "<|endoftext|>",
"split_special_tokens": false,
"tokenizer_class": "Qwen2Tokenizer",
"unk_token": null
}

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version https://git-lfs.github.com/spec/v1
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size 1987647762

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spec.json Normal file
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{
"format": "nano-v3.1-multi",
"base_model_id": "Qwen/Qwen2.5-14B-Instruct",
"hidden_layers": 48,
"locked_count": 76,
"pending_8bit": 260,
"elapsed_seconds": 5959,
"build_reference_mode": "8bit",
"reference_scope": "original_baseline",
"pending_policy": "leave_in_base_8bit",
"pruned_from": "/kaggle/working/final_artifact_Qwen2.5-14B-Instruct",
"pruned_strategy": "remove_last_locked",
"pruned_removed_count": 9,
"pruned_removed_modules": [
"model.layers.42.mlp.down_proj",
"model.layers.45.mlp.gate_proj",
"model.layers.46.mlp.down_proj",
"model.layers.28.self_attn.v_proj",
"model.layers.29.mlp.down_proj",
"model.layers.34.mlp.gate_proj",
"model.layers.36.mlp.up_proj",
"model.layers.42.self_attn.k_proj",
"model.layers.46.self_attn.o_proj"
],
"self_contained": true,
"base_model_local_subdir": "."
}

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{
"add_bos_token": false,
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}
},
"additional_special_tokens": [
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"<|vision_start|>",
"<|vision_end|>",
"<|vision_pad|>",
"<|image_pad|>",
"<|video_pad|>"
],
"bos_token": null,
"chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0]['role'] == 'system' %}\n {{- messages[0]['content'] }}\n {%- else %}\n {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}\n {%- endif %}\n {{- \"\\n\\n# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><|im_end|>\\n\" }}\n{%- else %}\n {%- if messages[0]['role'] == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0]['content'] + '<|im_end|>\\n' }}\n {%- else %}\n {{- '<|im_start|>system\\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and not message.tool_calls) %}\n {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {{- '<|im_start|>' + message.role }}\n {%- if message.content %}\n {{- '\\n' + message.content }}\n {%- endif %}\n {%- for tool_call in message.tool_calls %}\n {%- if tool_call.function is defined %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '\\n<tool_call>\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {{- tool_call.arguments | tojson }}\n {{- '}\\n</tool_call>' }}\n {%- endfor %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != \"tool\") %}\n {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n<tool_response>\\n' }}\n {{- message.content }}\n {{- '\\n</tool_response>' }}\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n' }}\n{%- endif %}\n",
"clean_up_tokenization_spaces": false,
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"errors": "replace",
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}

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