---
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)
)
```