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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-7B-Instruct
---
This tiny model is for debugging. It is randomly initialized with the config adapted from [tencent/Hunyuan-7B-Instruct](https://huggingface.co/tencent/Hunyuan-7B-Instruct).
### Example usage:
```python
import torch
from transformers.pipelines import pipeline
model_id = "tiny-random/hunyuan"
messages = [
{
"role": "user",
"content": "hi",
}
]
pipe = pipeline('text-generation', model_id, device='cuda', torch_dtype=torch.bfloat16, trust_remote_code=True,)
print(pipe(messages, max_new_tokens=32))
```
### Codes to create this repo:
```python
import json
from pathlib import Path
import accelerate
import torch
from huggingface_hub import file_exists, hf_hub_download
from transformers import (
AutoConfig,
AutoModelForCausalLM,
AutoProcessor,
GenerationConfig,
set_seed,
)
source_model_id = "tencent/Hunyuan-7B-Instruct"
save_folder = "/tmp/tiny-random/hunyuan"
processor = AutoProcessor.from_pretrained(source_model_id, trust_remote_code=True)
processor.save_pretrained(save_folder)
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)
config_json['hidden_size'] = 16
config_json['head_dim'] = 32
config_json['intermediate_size'] = 64
config_json['num_attention_heads'] = 2
config_json['num_hidden_layers'] = 2
config_json['num_key_value_heads'] = 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)
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, 0.1)
print(name, p.shape)
model.save_pretrained(save_folder)
print(model)
```
### Printing the model:
```text
HunYuanDenseV1ForCausalLM(
(model): HunYuanDenseV1Model(
(embed_tokens): Embedding(128167, 16, padding_idx=127961)
(layers): ModuleList(
(0-1): 2 x HunYuanDenseV1DecoderLayer(
(self_attn): HunYuanDenseV1Attention(
(q_proj): Linear(in_features=16, out_features=64, bias=False)
(k_proj): Linear(in_features=16, out_features=32, bias=False)
(v_proj): Linear(in_features=16, out_features=32, bias=False)
(o_proj): Linear(in_features=64, out_features=16, bias=False)
(query_layernorm): HunYuanDenseV1RMSNorm((32,), eps=1e-05)
(key_layernorm): HunYuanDenseV1RMSNorm((32,), eps=1e-05)
)
(mlp): HunYuanDenseV1MLP(
(gate_proj): Linear(in_features=16, out_features=64, bias=False)
(up_proj): Linear(in_features=16, out_features=64, bias=False)
(down_proj): Linear(in_features=64, out_features=16, bias=False)
(act_fn): SiLU()
)
(input_layernorm): HunYuanDenseV1RMSNorm((16,), eps=1e-05)
(post_attention_layernorm): HunYuanDenseV1RMSNorm((16,), eps=1e-05)
)
)
(norm): HunYuanDenseV1RMSNorm((16,), eps=1e-05)
(rotary_emb): HunYuanDenseV1RotaryEmbedding()
)
(lm_head): Linear(in_features=16, out_features=128167, bias=False)
)
``` |