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---
library_name: transformers
pipeline_tag: text-generation
inference: true
widget:
- text: Hello!
example_title: Hello world
group: Python
base_model:
- tencent/Hunyuan-A13B-Instruct
---
This tiny model is for debugging. It is randomly initialized with the config adapted from [tencent/Hunyuan-A13B-Instruct](https://huggingface.co/tencent/Hunyuan-A13B-Instruct).
### Example usage:
```python
import os
import re
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "tiny-random/hunyuan-moe"
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
# You may want to use bfloat16 and/or move to GPU here
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", trust_remote_code=True)
messages = [
{"role": "user", "content": "Write a short summary of the benefits of regular exercise"},
]
tokenized_chat = tokenizer.apply_chat_template(
messages, tokenize=True, return_tensors="pt",
enable_thinking=True, # Toggle thinking mode (default: True)
)
outputs = model.generate(tokenized_chat.to(model.device), max_new_tokens=32)
output_text = tokenizer.decode(outputs[0])
print(output_text)
```
### Codes to create this repo:
```python
import json
from pathlib import Path
import torch
import accelerate
from huggingface_hub import file_exists, hf_hub_download
from transformers import (
AutoConfig,
AutoModelForCausalLM,
AutoTokenizer,
GenerationConfig,
set_seed,
)
source_model_id = "tencent/Hunyuan-A13B-Instruct"
save_folder = "/tmp/tiny-random/hunyuan-moe"
processor = AutoTokenizer.from_pretrained(source_model_id, trust_remote_code=True)
processor.save_pretrained(save_folder)
hf_hub_download(source_model_id, filename='hy.tiktoken', repo_type='model', local_dir=save_folder, local_dir_use_symlinks=False)
with open(hf_hub_download(source_model_id, filename='config.json', repo_type='model'), 'r', encoding='utf-8') as f:
config_json = json.load(f)
for k, v in config_json['auto_map'].items():
config_json['auto_map'][k] = f'{source_model_id}--{v}'
config_json['attention_head_dim'] = 32
config_json['hidden_size'] = 64
config_json['intermediate_size'] = 128
config_json['moe_intermediate_size'] = [128, 128]
config_json['moe_topk'] = [2, 2]
config_json['num_attention_heads'] = 2
config_json['num_experts'] = 8
config_json['num_hidden_layers'] = 2
config_json['num_key_value_heads'] = 1
config_json['num_shared_expert'] = [1, 1]
config_json['tie_word_embeddings'] = True
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)
automap = config_json['auto_map']
torch.set_default_dtype(torch.bfloat16)
model = AutoModelForCausalLM.from_config(config, trust_remote_code=True)
torch.set_default_dtype(torch.float32)
if file_exists(filename="generation_config.json", repo_id=source_model_id, repo_type='model'):
model.generation_config = GenerationConfig.from_pretrained(
source_model_id, trust_remote_code=True,
)
set_seed(42)
model = model.cpu() # cpu is more stable for random initialization across machines
with torch.no_grad():
for name, p in sorted(model.named_parameters()):
torch.nn.init.normal_(p, 0, 0.2)
print(name, p.shape)
model.save_pretrained(save_folder)
print(model)
with open(f"{save_folder}/config.json", "r", encoding='utf-8') as f:
config_json = json.load(f)
config_json['auto_map'] = automap
with open(f"{save_folder}/config.json", "w", encoding='utf-8') as f:
json.dump(config_json, f, indent=2)
for python_file in Path(save_folder).glob('*.py'):
if python_file.name.startswith('modeling_') or python_file.name.startswith('configuration_'):
python_file.unlink()
```
### Printing the model:
```text
HunYuanMoEV1ForCausalLM(
(model): HunYuanModel(
(embed_tokens): Embedding(128167, 64, padding_idx=127961)
(layers): ModuleList(
(0-1): 2 x HunYuanDecoderLayer(
(self_attn): HunYuanSdpaAttention(
(q_proj): Linear(in_features=64, out_features=64, bias=False)
(k_proj): Linear(in_features=64, out_features=32, bias=False)
(v_proj): Linear(in_features=64, out_features=32, bias=False)
(o_proj): Linear(in_features=64, out_features=64, bias=False)
(query_layernorm): HunYuanRMSNorm()
(key_layernorm): HunYuanRMSNorm()
(rotary_emb): HunYuanDynamicNTKAlphaRotaryEmbedding()
)
(mlp): HunYuanMoE(
(shared_mlp): HunYuanMLP(
(gate_proj): Linear(in_features=64, out_features=128, bias=False)
(up_proj): Linear(in_features=64, out_features=128, bias=False)
(down_proj): Linear(in_features=128, out_features=64, bias=False)
(act_fn): SiLU()
)
(gate): HunYuanTopKGate(
(wg): Linear(in_features=64, out_features=8, bias=False)
)
(experts): ModuleList(
(0-7): 8 x HunYuanMLP(
(gate_proj): Linear(in_features=64, out_features=128, bias=False)
(up_proj): Linear(in_features=64, out_features=128, bias=False)
(down_proj): Linear(in_features=128, out_features=64, bias=False)
(act_fn): SiLU()
)
)
)
(input_layernorm): HunYuanRMSNorm()
(post_attention_layernorm): HunYuanRMSNorm()
)
)
(norm): HunYuanRMSNorm()
)
(lm_head): Linear(in_features=64, out_features=128167, bias=False)
)
``` |