Delete README.md

README.md

@@ -1,211 +0,0 @@
----
-library_name: transformers
-pipeline_tag: image-text-to-text
-inference: true
-widget:
-  - text: Hello!
-    example_title: Hello world
-    group: Python
-base_model:
-- Qwen/Qwen3-VL-8B-Thinking
----
-
-This tiny model is intended for debugging. It is randomly initialized using the configuration adapted from [Qwen/Qwen3-VL-8B-Thinking](https://huggingface.co/Qwen/Qwen3-VL-8B-Thinking).
-
-### Example usage:
-
-```python
-import numpy as np
-import torch
-import transformers
-from PIL import Image
-from transformers import (
-    AutoModel,
-    AutoModelForCausalLM,
-    AutoProcessor,
-    AutoTokenizer,
-    Qwen3VLForConditionalGeneration,
-)
-
-model_id = "tiny-random/qwen3-vl"
-model = Qwen3VLForConditionalGeneration.from_pretrained(
-    model_id, dtype=torch.bfloat16, device_map="cuda",
-    attn_implementation="flash_attention_2",
-)
-processor = AutoProcessor.from_pretrained(model_id)
-messages = [
-    {
-        "role": "user",
-        "content": [
-            {
-                "type": "image",
-                "image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg",
-            },
-            {"type": "text", "text": "Describe this image."},
-        ],
-    }
-]
-
-# Preparation for inference
-inputs = processor.apply_chat_template(
-    messages,
-    tokenize=True,
-    add_generation_prompt=True,
-    return_dict=True,
-    return_tensors="pt"
-).to(model.device)
-
-# Inference: Generation of the output
-generated_ids = model.generate(**inputs, max_new_tokens=32)
-generated_ids_trimmed = [
-    out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
-]
-output_text = processor.batch_decode(
-    generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
-)
-print(output_text)
-```
-
-### 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,
-    # Qwen3VLMoeForConditionalGeneration,
-    Qwen3VLForConditionalGeneration,
-    set_seed,
-)
-
-source_model_id = "Qwen/Qwen3-VL-8B-Thinking"
-save_folder = "/tmp/tiny-random/qwen3-vl"
-
-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['text_config'].update({
-    'head_dim': 32,
-    'hidden_size': 8,
-    'intermediate_size': 64,
-    'moe_intermediate_size': 64,
-    'num_hidden_layers': 2,
-    'num_attention_heads': 8,
-    'num_key_value_heads': 4,
-})
-config_json['text_config']['rope_scaling']['mrope_section'] = [8, 4, 4]
-config_json['vision_config'].update(
-    {
-        'hidden_size': 32 * 4,
-        'intermediate_size': 64,
-        'num_heads': 4,
-        'out_hidden_size': 8,
-        'depth': 6,
-        'deepstack_visual_indexes': [1, 3, 5],
-    }
-)
-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 = Qwen3VLForConditionalGeneration(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,
-    )
-    model.generation_config.do_sample = True
-    print(model.generation_config)
-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)
-```
-
-### Printing the model:
-
-```text
-Qwen3VLForConditionalGeneration(
-  (model): Qwen3VLModel(
-    (visual): Qwen3VLVisionModel(
-      (patch_embed): Qwen3VLVisionPatchEmbed(
-        (proj): Conv3d(3, 128, kernel_size=(2, 16, 16), stride=(2, 16, 16))
-      )
-      (pos_embed): Embedding(2304, 128)
-      (rotary_pos_emb): Qwen3VLVisionRotaryEmbedding()
-      (blocks): ModuleList(
-        (0-5): 6 x Qwen3VLVisionBlock(
-          (norm1): LayerNorm((128,), eps=1e-06, elementwise_affine=True)
-          (norm2): LayerNorm((128,), eps=1e-06, elementwise_affine=True)
-          (attn): Qwen3VLVisionAttention(
-            (qkv): Linear(in_features=128, out_features=384, bias=True)
-            (proj): Linear(in_features=128, out_features=128, bias=True)
-          )
-          (mlp): Qwen3VLVisionMLP(
-            (linear_fc1): Linear(in_features=128, out_features=64, bias=True)
-            (linear_fc2): Linear(in_features=64, out_features=128, bias=True)
-            (act_fn): PytorchGELUTanh()
-          )
-        )
-      )
-      (merger): Qwen3VLVisionPatchMerger(
-        (norm): LayerNorm((128,), eps=1e-06, elementwise_affine=True)
-        (linear_fc1): Linear(in_features=512, out_features=512, bias=True)
-        (act_fn): GELU(approximate='none')
-        (linear_fc2): Linear(in_features=512, out_features=8, bias=True)
-      )
-      (deepstack_merger_list): ModuleList(
-        (0-2): 3 x Qwen3VLVisionPatchMerger(
-          (norm): LayerNorm((512,), eps=1e-06, elementwise_affine=True)
-          (linear_fc1): Linear(in_features=512, out_features=512, bias=True)
-          (act_fn): GELU(approximate='none')
-          (linear_fc2): Linear(in_features=512, out_features=8, bias=True)
-        )
-      )
-    )
-    (language_model): Qwen3VLTextModel(
-      (embed_tokens): Embedding(151936, 8)
-      (layers): ModuleList(
-        (0-1): 2 x Qwen3VLTextDecoderLayer(
-          (self_attn): Qwen3VLTextAttention(
-            (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)
-            (q_norm): Qwen3VLTextRMSNorm((32,), eps=1e-06)
-            (k_norm): Qwen3VLTextRMSNorm((32,), eps=1e-06)
-          )
-          (mlp): Qwen3VLTextMLP(
-            (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): SiLU()
-          )
-          (input_layernorm): Qwen3VLTextRMSNorm((8,), eps=1e-06)
-          (post_attention_layernorm): Qwen3VLTextRMSNorm((8,), eps=1e-06)
-        )
-      )
-      (norm): Qwen3VLTextRMSNorm((8,), eps=1e-06)
-      (rotary_emb): Qwen3VLTextRotaryEmbedding()
-    )
-  )
-  (lm_head): Linear(in_features=8, out_features=151936, bias=False)
-)
-```