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---
library_name: transformers
pipeline_tag: text-generation
inference: true
widget:
- text: Hello!
example_title: Hello world
group: Python
base_model:
- swiss-ai/Apertus-70B-Instruct-2509
---
This tiny model is intended for debugging. It is randomly initialized using the configuration adapted from [swiss-ai/Apertus-70B-Instruct-2509](https://huggingface.co/swiss-ai/Apertus-70B-Instruct-2509).
### Example usage:
- vLLM
```bash
vllm serve tiny-random/apertus
```
- Transformers
```python
import os
import re
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "tiny-random/apertus"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", torch_dtype=torch.bfloat16)
messages = [
{"role": "user", "content": "How to make pasta?"},
]
tokenized_chat = tokenizer.apply_chat_template(
messages,
tokenize=True,
add_generation_prompt=True,
return_tensors="pt",
)
outputs = model.generate(tokenized_chat.to(model.device), max_new_tokens=128)
output_text = tokenizer.decode(outputs[0])
print(output_text)
```
### 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 = "swiss-ai/Apertus-70B-Instruct-2509"
save_folder = "/tmp/tiny-random/apertus"
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'] = 8
config_json['head_dim'] = 32 # vllm requirement
config_json['intermediate_size'] = 32
config_json['num_attention_heads'] = 8
config_json['num_hidden_layers'] = 2
config_json['num_key_value_heads'] = 4 # better support tensor parallel
config_json['tie_word_embeddings'] = False
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,
)
model.generation_config.do_sample = 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)
```
### Printing the model:
```text
ApertusForCausalLM(
(model): ApertusModel(
(embed_tokens): Embedding(131072, 8, padding_idx=3)
(layers): ModuleList(
(0-1): 2 x ApertusDecoderLayer(
(self_attn): ApertusAttention(
(q_proj): Linear(in_features=8, out_features=256, bias=False)
(k_proj): Linear(in_features=8, out_features=128, bias=False)
(v_proj): Linear(in_features=8, out_features=128, bias=False)
(o_proj): Linear(in_features=256, out_features=8, bias=False)
(q_norm): ApertusRMSNorm((32,), eps=1e-05)
(k_norm): ApertusRMSNorm((32,), eps=1e-05)
)
(mlp): ApertusMLP(
(up_proj): Linear(in_features=8, out_features=32, bias=False)
(down_proj): Linear(in_features=32, out_features=8, bias=False)
(act_fn): XIELUActivation()
)
(attention_layernorm): ApertusRMSNorm((8,), eps=1e-05)
(feedforward_layernorm): ApertusRMSNorm((8,), eps=1e-05)
)
)
(norm): ApertusRMSNorm((8,), eps=1e-05)
(rotary_emb): ApertusRotaryEmbedding()
)
(lm_head): Linear(in_features=8, out_features=131072, bias=False)
)
``` |