Delete README.md

README.md

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----
-library_name: transformers
-pipeline_tag: image-text-to-text
-inference: true
-widget:
-  - text: Hello!
-    example_title: Hello world
-    group: Python
-base_model:
-- THUDM/GLM-4.1V-9B-Thinking
----
-
-This tiny model is for debugging. It is randomly initialized with the config adapted from [THUDM/GLM-4.1V-9B-Thinking](https://huggingface.co/THUDM/GLM-4.1V-9B-Thinking).
-
-### Example usage:
-
-```python
-import os
-import re
-
-import torch
-
-from transformers import AutoProcessor, Glm4vForConditionalGeneration
-
-model_id = "yujiepan/glm-4.1v-tiny-random"
-messages = [
-    {
-        "role": "user",
-        "content": [
-            {
-                "type": "image",
-                "url": "https://upload.wikimedia.org/wikipedia/commons/f/fa/Grayscale_8bits_palette_sample_image.png"
-            },
-            {
-                "type": "text",
-                "text": "describe this image"
-            }
-        ],
-    }
-]
-processor = AutoProcessor.from_pretrained(model_id)
-model = Glm4vForConditionalGeneration.from_pretrained(
-    pretrained_model_name_or_path=model_id,
-    torch_dtype=torch.bfloat16,
-    device_map="auto",
-)
-inputs = processor.apply_chat_template(
-    messages,
-    tokenize=True,
-    add_generation_prompt=True,
-    return_dict=True,
-    return_tensors="pt"
-).to(model.device)
-generated_ids = model.generate(**inputs, max_new_tokens=16)
-output_text = processor.decode(generated_ids[0][inputs["input_ids"].shape[1]:], skip_special_tokens=False)
-print(output_text)
-```
-
-### Codes to create this repo:
-
-```python
-import json
-from pathlib import Path
-
-import torch
-
-import accelerate
-from huggingface_hub import file_exists, hf_hub_download
-from transformers import (
-    AutoConfig,
-    AutoModelForCausalLM,
-    AutoProcessor,
-    GenerationConfig,
-    set_seed,
-)
-from transformers import AutoProcessor, Glm4vForConditionalGeneration
-
-source_model_id = "THUDM/GLM-4.1V-9B-Thinking"
-save_folder = "/tmp/yujiepan/glm-4.1v-tiny-random"
-
-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'] = 64
-config_json['intermediate_size'] = 128
-config_json['num_attention_heads'] = 2
-config_json['num_hidden_layers'] = 2
-config_json['num_key_value_heads'] = 1
-config_json['tie_word_embeddings'] = True
-config_json['vision_config']['hidden_size'] = 64
-config_json['vision_config']['depth'] = 2
-config_json['vision_config']['num_heads'] = 2
-config_json['vision_config']['intermediate_size'] = 128
-config_json['vision_config']['out_hidden_size'] = 64
-config_json['rope_scaling']['mrope_section'] = [2, 2, 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,
-)
-print(config)
-torch.set_default_dtype(torch.bfloat16)
-model = Glm4vForConditionalGeneration(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
-Glm4vForConditionalGeneration(
-  (model): Glm4vModel(
-    (visual): Glm4vVisionModel(
-      (embeddings): Glm4vVisionEmbeddings(
-        (position_embedding): Embedding(576, 64)
-      )
-      (patch_embed): Glm4vVisionPatchEmbed(
-        (proj): Conv3d(3, 64, kernel_size=(2, 14, 14), stride=(2, 14, 14))
-      )
-      (rotary_pos_emb): Glm4vVisionRotaryEmbedding()
-      (blocks): ModuleList(
-        (0-1): 2 x Glm4vVisionBlock(
-          (norm1): Glm4vRMSNorm((64,), eps=1e-05)
-          (norm2): Glm4vRMSNorm((64,), eps=1e-05)
-          (attn): Glm4vVisionAttention(
-            (qkv): Linear(in_features=64, out_features=192, bias=False)
-            (proj): Linear(in_features=64, out_features=64, bias=False)
-          )
-          (mlp): Glm4VisionMlp(
-            (gate_proj): Linear(in_features=64, out_features=64, bias=False)
-            (up_proj): Linear(in_features=64, out_features=64, bias=False)
-            (down_proj): Linear(in_features=64, out_features=64, bias=False)
-            (act_fn): SiLU()
-          )
-        )
-      )
-      (merger): Glm4vVisionPatchMerger(
-        (proj): Linear(in_features=64, out_features=64, bias=False)
-        (post_projection_norm): LayerNorm((64,), eps=1e-05, elementwise_affine=True)
-        (gate_proj): Linear(in_features=64, out_features=128, bias=False)
-        (up_proj): Linear(in_features=64, out_features=128, bias=False)
-        (down_proj): Linear(in_features=128, out_features=64, bias=False)
-        (act1): GELU(approximate='none')
-        (act_fn): SiLU()
-      )
-      (post_conv_layernorm): Glm4vRMSNorm((64,), eps=1e-05)
-      (downsample): Conv2d(64, 64, kernel_size=(2, 2), stride=(2, 2))
-      (post_layernorm): Glm4vRMSNorm((64,), eps=1e-05)
-    )
-    (language_model): Glm4vTextModel(
-      (embed_tokens): Embedding(151552, 64, padding_idx=151329)
-      (layers): ModuleList(
-        (0-1): 2 x Glm4vTextDecoderLayer(
-          (self_attn): Glm4vTextAttention(
-            (q_proj): Linear(in_features=64, out_features=64, bias=True)
-            (k_proj): Linear(in_features=64, out_features=32, bias=True)
-            (v_proj): Linear(in_features=64, out_features=32, bias=True)
-            (o_proj): Linear(in_features=64, out_features=64, bias=False)
-          )
-          (mlp): Glm4vTextMLP(
-            (gate_up_proj): Linear(in_features=64, out_features=256, bias=False)
-            (down_proj): Linear(in_features=128, out_features=64, bias=False)
-            (activation_fn): SiLU()
-          )
-          (input_layernorm): Glm4vRMSNorm((64,), eps=1e-05)
-          (post_attention_layernorm): Glm4vRMSNorm((64,), eps=1e-05)
-          (post_self_attn_layernorm): Glm4vRMSNorm((64,), eps=1e-05)
-          (post_mlp_layernorm): Glm4vRMSNorm((64,), eps=1e-05)
-        )
-      )
-      (norm): Glm4vRMSNorm((64,), eps=1e-05)
-      (rotary_emb): Glm4vTextRotaryEmbedding()
-    )
-  )
-  (lm_head): Linear(in_features=64, out_features=151552, bias=False)
-)
-```