File size: 4,188 Bytes
d463a50 e49686a d463a50 | 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 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 | ---
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)
)
``` |