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---
library_name: transformers
base_model:
- allenai/Olmo-3-32B-Think
---
This tiny model is intended for debugging. It is randomly initialized using the configuration adapted from [allenai/Olmo-3-32B-Think](https://huggingface.co/allenai/Olmo-3-32B-Think).
### Example usage:
```python
import os
import re
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "yujiepan/olmo-3-tiny-random"
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?" * 1500},
]
inputs = tokenizer.apply_chat_template(
messages,
tokenize=True,
add_generation_prompt=True,
return_tensors="pt",
)['input_ids']
print(inputs.shape)
outputs = model.generate(inputs.to(
model.device), max_new_tokens=32)
print(outputs)
```
### 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 = "allenai/Olmo-3-32B-Think"
save_folder = "/tmp/yujiepan/olmo-3-tiny-random"
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
config_json['layer_types'] = ['sliding_attention', 'full_attention']
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
Olmo3ForCausalLM(
(model): Olmo3Model(
(embed_tokens): Embedding(100278, 8, padding_idx=100277)
(layers): ModuleList(
(0-1): 2 x Olmo3DecoderLayer(
(self_attn): Olmo3Attention(
(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): Olmo3RMSNorm((256,), eps=1e-06)
(k_norm): Olmo3RMSNorm((128,), eps=1e-06)
)
(mlp): Olmo3MLP(
(gate_proj): Linear(in_features=8, out_features=32, bias=False)
(up_proj): Linear(in_features=8, out_features=32, bias=False)
(down_proj): Linear(in_features=32, out_features=8, bias=False)
(act_fn): SiLUActivation()
)
(post_attention_layernorm): Olmo3RMSNorm((8,), eps=1e-06)
(post_feedforward_layernorm): Olmo3RMSNorm((8,), eps=1e-06)
)
)
(norm): Olmo3RMSNorm((8,), eps=1e-06)
(rotary_emb): Olmo3RotaryEmbedding()
)
(lm_head): Linear(in_features=8, out_features=100278, bias=False)
)
``` |