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---
library_name: transformers
pipeline_tag: text-generation
inference: true
widget:
- text: Hello!
example_title: Hello world
group: Python
base_model:
- LiquidAI/LFM2-1.2B
---
This tiny model is for debugging. It is randomly initialized with the config adapted from [LiquidAI/LFM2-1.2B](https://huggingface.co/LiquidAI/LFM2-1.2B).
### Example usage:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
# Load model and tokenizer
model_id = "tiny-random/lfm2"
model = AutoModelForCausalLM.from_pretrained(
model_id,
device_map="auto",
torch_dtype="bfloat16",
trust_remote_code=True,
# attn_implementation="flash_attention_2" <- uncomment on compatible GPU
)
tokenizer = AutoTokenizer.from_pretrained(model_id)
# Generate answer
prompt = "What is C. elegans?"
input_ids = tokenizer.apply_chat_template(
[{"role": "user", "content": prompt}],
add_generation_prompt=True,
return_tensors="pt",
tokenize=True,
).to(model.device)
output = model.generate(
input_ids,
do_sample=True,
temperature=0.3,
min_p=0.15,
repetition_penalty=1.05,
max_new_tokens=512,
)
print(tokenizer.decode(output[0], skip_special_tokens=False))
```
### 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 = "LiquidAI/LFM2-1.2B"
save_folder = "/tmp/tiny-random/lfm2"
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['block_dim'] = 64
config_json['block_ff_dim'] = 128
config_json['full_attn_idxs'] = [1]
config_json['conv_dim'] = 64
config_json['conv_dim_out'] = 64
config_json['hidden_size'] = 64
config_json['intermediate_size'] = 128
config_json['num_attention_heads'] = 2
config_json['num_heads'] = 2
config_json['num_hidden_layers'] = 2
config_json['num_key_value_heads'] = 1
config_json['tie_word_embeddings'] = True
config_json['use_cache'] = True
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)
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,
)
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, 0.2)
print(name, p.shape)
model.save_pretrained(save_folder)
print(model)
```
### Printing the model:
```text
Lfm2ForCausalLM(
(model): Lfm2Model(
(embed_tokens): Embedding(65536, 64, padding_idx=0)
(layers): ModuleList(
(0): Lfm2DecoderLayer(
(conv): Lfm2ShortConv(
(conv): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(2,), groups=64, bias=False)
(in_proj): Linear(in_features=64, out_features=192, bias=False)
(out_proj): Linear(in_features=64, out_features=64, bias=False)
)
(feed_forward): Lfm2MLP(
(w1): Linear(in_features=64, out_features=256, bias=False)
(w3): Linear(in_features=64, out_features=256, bias=False)
(w2): Linear(in_features=256, out_features=64, bias=False)
)
(operator_norm): Lfm2RMSNorm((64,), eps=1e-05)
(ffn_norm): Lfm2RMSNorm((64,), eps=1e-05)
)
(1): Lfm2DecoderLayer(
(self_attn): Lfm2Attention(
(q_proj): Linear(in_features=64, out_features=64, bias=False)
(k_proj): Linear(in_features=64, out_features=32, bias=False)
(v_proj): Linear(in_features=64, out_features=32, bias=False)
(out_proj): Linear(in_features=64, out_features=64, bias=False)
(q_layernorm): Lfm2RMSNorm((32,), eps=1e-05)
(k_layernorm): Lfm2RMSNorm((32,), eps=1e-05)
)
(feed_forward): Lfm2MLP(
(w1): Linear(in_features=64, out_features=256, bias=False)
(w3): Linear(in_features=64, out_features=256, bias=False)
(w2): Linear(in_features=256, out_features=64, bias=False)
)
(operator_norm): Lfm2RMSNorm((64,), eps=1e-05)
(ffn_norm): Lfm2RMSNorm((64,), eps=1e-05)
)
)
(rotary_emb): Lfm2RotaryEmbedding()
(pos_emb): Lfm2RotaryEmbedding()
(embedding_norm): Lfm2RMSNorm((64,), eps=1e-05)
)
(lm_head): Linear(in_features=64, out_features=65536, bias=False)
)
``` |