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Model: tiny-random/gemma-2
Source: Original Platform

.gitattributes

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README.md

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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)
+)
+```

chat_template.jinja

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+{{ bos_token }}{% if messages[0]['role'] == 'system' %}{{ raise_exception('System role not supported') }}{% endif %}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if (message['role'] == 'assistant') %}{% set role = 'model' %}{% else %}{% set role = message['role'] %}{% endif %}{{ '<start_of_turn>' + role + '
+' + message['content'] | trim + '<end_of_turn>
+' }}{% endfor %}{% if add_generation_prompt %}{{'<start_of_turn>model
+'}}{% endif %}

config.json

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+{
+  "architectures": [
+    "Gemma2ForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "attn_logit_softcapping": 50.0,
+  "bos_token_id": 2,
+  "cache_implementation": "hybrid",
+  "dtype": "bfloat16",
+  "eos_token_id": 1,
+  "final_logit_softcapping": 30.0,
+  "head_dim": 32,
+  "hidden_act": "gelu_pytorch_tanh",
+  "hidden_activation": "gelu_pytorch_tanh",
+  "hidden_size": 8,
+  "initializer_range": 0.02,
+  "intermediate_size": 64,
+  "layer_types": [
+    "sliding_attention",
+    "full_attention"
+  ],
+  "max_position_embeddings": 8192,
+  "model_type": "gemma2",
+  "num_attention_heads": 8,
+  "num_hidden_layers": 2,
+  "num_key_value_heads": 4,
+  "pad_token_id": 0,
+  "query_pre_attn_scalar": 144,
+  "rms_norm_eps": 1e-06,
+  "rope_theta": 10000.0,
+  "sliding_window": 4096,
+  "sliding_window_size": 4096,
+  "transformers_version": "4.57.1",
+  "use_cache": true,
+  "vocab_size": 256000
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 2,
+  "cache_implementation": "hybrid",
+  "eos_token_id": 1,
+  "pad_token_id": 0,
+  "transformers_version": "4.57.1"
+}

model.safetensors

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special_tokens_map.json

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+{
+  "additional_special_tokens": [
+    "<start_of_turn>",
+    "<end_of_turn>"
+  ],
+  "bos_token": {
+    "content": "<bos>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<eos>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<pad>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer.json

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tokenizer.model

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