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Model: tiny-random/gemma-2 Source: Original Platform
2026-05-12 01:18:41 +08:00
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+---
+library_name: transformers
+base_model:
+- google/gemma-2-27b-it
+---
+
+This tiny model is intended for debugging. It is randomly initialized using the configuration adapted from [google/gemma-2-27b-it](https://huggingface.co/google/gemma-2-27b-it).
+
+### Example usage:
+
+```python
+from transformers import pipeline
+model_id = "tiny-random/gemma-2"
+pipe = pipeline('text-generation', model=model_id, device='cuda', dtype="bfloat16")
+print(pipe('Hello World!'))
+```
+
+### 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 = "google/gemma-2-27b-it"
+save_folder = "/tmp/tiny-random/gemma-2"
+
+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['intermediate_size'] = 64
+config_json['num_attention_heads'] = 8
+config_json['num_hidden_layers'] = 2
+config_json['num_key_value_heads'] = 4
+config_json['head_dim'] = 32
+config_json['tie_word_embeddings'] = 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()
+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)
+print(model)
+```
+
+### Printing the model:
+
+```text
+Gemma2ForCausalLM(
+  (model): Gemma2Model(
+    (embed_tokens): Embedding(256000, 8, padding_idx=0)
+    (layers): ModuleList(
+      (0-1): 2 x Gemma2DecoderLayer(
+        (self_attn): Gemma2Attention(
+          (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)
+        )
+        (mlp): Gemma2MLP(
+          (gate_proj): Linear(in_features=8, out_features=64, bias=False)
+          (up_proj): Linear(in_features=8, out_features=64, bias=False)
+          (down_proj): Linear(in_features=64, out_features=8, bias=False)
+          (act_fn): GELUTanh()
+        )
+        (input_layernorm): Gemma2RMSNorm((8,), eps=1e-06)
+        (post_attention_layernorm): Gemma2RMSNorm((8,), eps=1e-06)
+        (pre_feedforward_layernorm): Gemma2RMSNorm((8,), eps=1e-06)
+        (post_feedforward_layernorm): Gemma2RMSNorm((8,), eps=1e-06)
+      )
+    )
+    (norm): Gemma2RMSNorm((8,), eps=1e-06)
+    (rotary_emb): Gemma2RotaryEmbedding()
+  )
+  (lm_head): Linear(in_features=8, out_features=256000, bias=False)
+)
+```