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library_name: transformers
base_model:
- Qwen/Qwen2.5-72B-Instruct
---
This tiny model is intended for debugging. It is randomly initialized using the configuration adapted from [Qwen/Qwen2.5-72B-Instruct](https://huggingface.co/Qwen/Qwen2.5-72B-Instruct).
| File path | Size |
|------|------|
| model.safetensors | 4.9MB |
### Example usage:
```python
from transformers import pipeline
model_id = "tiny-random/qwen2.5"
pipe = pipeline(
"text-generation", model=model_id,
trust_remote_code=True, max_new_tokens=8,
)
print(pipe("Hello World!"))
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
dtype="auto",
device_map="auto"
)
prompt = "Give me a short introduction to large language model."
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
generated_ids = model.generate(
**model_inputs,
max_new_tokens=32
)
output_ids = generated_ids[0].tolist()
content = tokenizer.decode(output_ids, skip_special_tokens=False)
print(content)
```
### Codes to create this repo:
<details>
<summary>Click to expand</summary>
```python
import json
from pathlib import Path
import torch
from huggingface_hub import hf_hub_download
from transformers import (
AutoConfig,
AutoModelForCausalLM,
AutoTokenizer,
GenerationConfig,
pipeline,
set_seed,
)
source_model_id = "Qwen/Qwen2.5-72B-Instruct"
save_folder = "/tmp/tiny-random/qwen25"
tokenizer = AutoTokenizer.from_pretrained(
source_model_id, trust_remote_code=True,
)
tokenizer.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: dict = json.load(f)
config_json.update({
"num_hidden_layers": 4,
"hidden_size": 8,
"intermediate_size": 32,
"max_window_layers": 2,
"head_dim": 32,
"num_attention_heads": 8,
"num_key_value_heads": 4,
})
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,
)
model = AutoModelForCausalLM.from_config(
config,
torch_dtype=torch.bfloat16,
trust_remote_code=True,
)
model.generation_config = GenerationConfig.from_pretrained(
source_model_id, trust_remote_code=True,
)
set_seed(42)
with torch.no_grad():
for name, p in sorted(model.named_parameters()):
torch.nn.init.normal_(p, 0, 0.2)
print(name, p.shape)
model.save_pretrained(save_folder)
```
</details>
### Printing the model:
<details><summary>Click to expand</summary>
```text
Qwen2ForCausalLM(
(model): Qwen2Model(
(embed_tokens): Embedding(152064, 8)
(layers): ModuleList(
(0-3): 4 x Qwen2DecoderLayer(
(self_attn): Qwen2Attention(
(q_proj): Linear(in_features=8, out_features=256, bias=True)
(k_proj): Linear(in_features=8, out_features=128, bias=True)
(v_proj): Linear(in_features=8, out_features=128, bias=True)
(o_proj): Linear(in_features=256, out_features=8, bias=False)
)
(mlp): Qwen2MLP(
(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()
)
(input_layernorm): Qwen2RMSNorm((8,), eps=1e-06)
(post_attention_layernorm): Qwen2RMSNorm((8,), eps=1e-06)
)
)
(norm): Qwen2RMSNorm((8,), eps=1e-06)
(rotary_emb): Qwen2RotaryEmbedding()
)
(lm_head): Linear(in_features=8, out_features=152064, bias=False)
)
```
</details>
### Test environment:
- torch: 2.11.0
- transformers: 5.5.0 |