This model is for debugging purposes. It is randomly initialized using the config from mistralai/Mamba-Codestral-7B-v0.1 but with a smaller size.
Codes:
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-codestral-v0.1-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!"))
with open(__file__, 'r') as f:
codes = f.read()
with open(f'{save_path}/README.md', 'w') as f:
f.write(
f'''---
library_name: transformers
pipeline_tag: text-generation
inference: true
widget:
- text: Hello!
example_title: Hello world
group: Python
---
This model is for debugging purposes. It is randomly initialized using the config from [{model_id}](https://huggingface.co/{model_id}) but with a smaller size.
Codes:
```python
{codes}
```'''
)
create_repo(repo_id, exist_ok=True)
upload_folder(repo_id=repo_id, folder_path=save_path, repo_type='model')