Hugging Face's logo

yujiepan
/
mamba2-tiny-random

Text Generation
Transformers
Safetensors
Inference Endpoints
Model card Files Community
mamba2-tiny-random
File size: 1,993 Bytes 
f7da86c
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
 
---
library_name: transformers
pipeline_tag: text-generation
inference: true
widget:
- text: Hello!
  example_title: Hello world
  group: Python
---

This model is for debugging. It is randomly initialized using the config from [mistralai/Mamba-Codestral-7B-v0.1](https://huggingface.co/mistralai/Mamba-Codestral-7B-v0.1) but with smaller size. 

Codes:
```python
import os

import torch

from huggingface_hub import create_repo, upload_folder
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
    GenerationConfig,
    Mamba2Config,
    pipeline,
    set_seed,
)

model_id = "mistralai/Mamba-Codestral-7B-v0.1"
repo_id = "yujiepan/mamba2-tiny-random"
save_path = f"/tmp/{repo_id}"

os.system(f'rm -rf {save_path}')

config = Mamba2Config.from_pretrained(model_id)
config.use_cache = True
config.num_hidden_layers = 2
config.num_heads = 8
config.head_dim = 4
config.hidden_size = 8
config.expand = 4
config.intermediate_size = 32
config.state_size = 8
config.n_groups = 2

assert config.intermediate_size == \
    config.hidden_size * config.expand == config.num_heads * config.head_dim
assert config.num_heads // config.n_groups > 0
assert config.num_heads % 8 == 0

tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
tokenizer.save_pretrained(save_path)

model = AutoModelForCausalLM.from_config(
    config, torch_dtype=torch.bfloat16,
    trust_remote_code=True,
)
model.generation_config = GenerationConfig.from_pretrained(
    model_id,
    trust_remote_code=True,
)

set_seed(42)
with torch.no_grad():
    for name, p in sorted(model.named_parameters()):
        print(name, p.shape)
        torch.nn.init.uniform_(p, -0.5, 0.5)

model.save_pretrained(save_path)

pipe = pipeline(
    "text-generation",
    model=save_path,
    device="cuda",
    trust_remote_code=True,
    max_new_tokens=20,
)
print(pipe("Hello World!"))

create_repo(repo_id, exist_ok=True)
upload_folder(repo_id=repo_id, folder_path=save_path, repo_type='model')
```