upload

.gitattributes

@@ -33,3 +33,8 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*.gguf filter=lfs diff=lfs merge=lfs -text
+1.pretrain/tokenizer.json filter=lfs diff=lfs merge=lfs -text
+2.sft/tokenizer.json filter=lfs diff=lfs merge=lfs -text
+3.prune/tokenizer.json filter=lfs diff=lfs merge=lfs -text
+4.prune-finetune/tokenizer.json filter=lfs diff=lfs merge=lfs -text

1.pretrain.py

@@ -0,0 +1,99 @@
+from unsloth import FastVisionModel # FastLanguageModel for LLMs
+import torch
+from datasets import load_dataset
+from unsloth import is_bf16_supported, UnslothTrainer, UnslothTrainingArguments
+from unsloth.trainer import UnslothVisionDataCollator
+from trl import SFTTrainer, SFTConfig
+from transformers import TextStreamer
+import datetime
+timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
+
+model, tokenizer = FastVisionModel.from_pretrained(
+    model_name = "/home/share/rzhong/model/Qwen2.5-VL-3B-Instruct",
+    load_in_4bit = False, # Use 4bit to reduce memory use. False for 16bit LoRA.
+                          # 模型已经选了4bit量化后的，这里还需不需要再以4bit加载？建议实验一下 | 应该是一个意思
+    use_gradient_checkpointing = "unsloth", # True or "unsloth" for long context
+    max_seq_length = 2048, # unsloth支持4x的上下文微调，如果原模型支持8192的上下文，这里只需要设置为2048
+    dtype = torch.bfloat16, # A100支持bfloat16，可以减少显存占用。默认为None，也可以选择torch.float16
+)
+
+model = FastVisionModel.get_peft_model(
+    model,
+    finetune_vision_layers     = True, # False if not finetuning vision layers
+    finetune_language_layers   = True, # False if not finetuning language layers
+    finetune_attention_modules = True, # False if not finetuning attention layers
+    finetune_mlp_modules       = True, # False if not finetuning MLP layers
+    
+    r = 16,           # The larger, the higher the accuracy, but might overfit
+    lora_alpha = 16,  # Recommended alpha == r at least
+    lora_dropout = 0,
+    bias = "none",
+    random_state = 3407,
+    use_rslora = False,  # We support rank stabilized LoRA
+    loftq_config = None, # And LoftQ
+    # target_modules = "all-linear", # Optional now! Can specify a list if needed
+    target_modules = ["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj",],
+)
+
+dataset = load_dataset("/home/share/rzhong/dataset/google-landmark/dataset_5/dataset_file", split = "train")
+print(dataset)
+
+instruction = "这张图片是什么地点或文物？"
+# instruction = "Write the LaTeX representation for this image."
+
+def convert_to_conversation(sample):
+    conversation = [
+        { "role": "user",
+          "content" : [
+            {"type" : "text",  "text"  : instruction},
+            {"type" : "image", "image" : sample["image"]} ]
+        },
+        { "role" : "assistant",
+          "content" : [
+            {"type" : "text",  "text"  : sample["text"]} ]
+        },
+    ]
+    return { "messages" : conversation }
+pass
+
+converted_dataset = [convert_to_conversation(sample) for sample in dataset]
+
+print(converted_dataset[0])
+
+FastVisionModel.for_training(model) # Enable for training!
+
+trainer = UnslothTrainer(
+    model = model,
+    tokenizer = tokenizer,
+    data_collator = UnslothVisionDataCollator(model, tokenizer), # Must use!
+    train_dataset = converted_dataset,
+    args = UnslothTrainingArguments(
+        per_device_train_batch_size = 2,
+        gradient_accumulation_steps = 4, # 原来是4。可以增加，相当于提高batch size，但不会影响内存消耗。增加会使loss曲线更平滑
+        warmup_steps = 5,
+        # max_steps = None,
+        num_train_epochs = 15, # Set this instead of max_steps for full training runs
+        learning_rate = 2e-5, # 2e-4 1e-4 5e-5 2e-5
+        fp16 = not is_bf16_supported(),
+        bf16 = is_bf16_supported(),
+        logging_steps = 1,
+        optim = "adamw_8bit",
+        weight_decay = 0.01,
+        lr_scheduler_type = "linear",
+        seed = 3407,
+        output_dir = f"outputs_pretrain_{timestamp}",
+        report_to = "none",     # For Weights and Biases
+
+        # You MUST put the below items for vision finetuning:
+        remove_unused_columns = False,
+        dataset_text_field = "",
+        dataset_kwargs = {"skip_prepare_dataset": True},
+        dataset_num_proc = 4,
+        max_seq_length = 2048,
+    ),
+)
+
+trainer_stats = trainer.train()
+
+model.save_pretrained(f"lora_model_pretrain_{timestamp}")  # Local saving
+tokenizer.save_pretrained(f"lora_model_pretrain_{timestamp}")

1.pretrain/Modelfile

@@ -0,0 +1,16 @@
+# ollama modelfile auto-generated by llamafactory
+
+FROM .
+
+TEMPLATE """{{ if .System }}<|im_start|>system
+{{ .System }}<|im_end|>
+{{ end }}{{ range .Messages }}{{ if eq .Role "user" }}<|im_start|>user
+{{ .Content }}<|im_end|>
+<|im_start|>assistant
+{{ else if eq .Role "assistant" }}{{ .Content }}<|im_end|>
+{{ end }}{{ end }}"""
+
+SYSTEM """You are a helpful assistant."""
+
+PARAMETER stop "<|im_end|>"
+PARAMETER num_ctx 4096

1.pretrain/added_tokens.json

@@ -0,0 +1,24 @@
+{
+  "</tool_call>": 151658,
+  "<tool_call>": 151657,
+  "<|box_end|>": 151649,
+  "<|box_start|>": 151648,
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+  "<|file_sep|>": 151664,
+  "<|fim_middle|>": 151660,
+  "<|fim_pad|>": 151662,
+  "<|fim_prefix|>": 151659,
+  "<|fim_suffix|>": 151661,
+  "<|im_end|>": 151645,
+  "<|im_start|>": 151644,
+  "<|image_pad|>": 151655,
+  "<|object_ref_end|>": 151647,
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+  "<|quad_end|>": 151651,
+  "<|quad_start|>": 151650,
+  "<|repo_name|>": 151663,
+  "<|video_pad|>": 151656,
+  "<|vision_end|>": 151653,
+  "<|vision_pad|>": 151654,
+  "<|vision_start|>": 151652
+}

1.pretrain/chat_template.json

@@ -0,0 +1,3 @@
+{
+  "chat_template": "{% set image_count = namespace(value=0) %}{% set video_count = namespace(value=0) %}{% for message in messages %}{% if loop.first and message['role'] != 'system' %}<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n{% endif %}<|im_start|>{{ message['role'] }}\n{% if message['content'] is string %}{{ message['content'] }}<|im_end|>\n{% else %}{% for content in message['content'] %}{% if content['type'] == 'image' or 'image' in content or 'image_url' in content %}{% set image_count.value = image_count.value + 1 %}{% if add_vision_id %}Picture {{ image_count.value }}: {% endif %}<|vision_start|><|image_pad|><|vision_end|>{% elif content['type'] == 'video' or 'video' in content %}{% set video_count.value = video_count.value + 1 %}{% if add_vision_id %}Video {{ video_count.value }}: {% endif %}<|vision_start|><|video_pad|><|vision_end|>{% elif 'text' in content %}{{ content['text'] }}{% endif %}{% endfor %}<|im_end|>\n{% endif %}{% endfor %}{% if add_generation_prompt %}<|im_start|>assistant\n{% endif %}"
+}

1.pretrain/config.json

@@ -0,0 +1,51 @@
+{
+  "_name_or_path": "/home/share/rzhong/model/Qwen2.5-VL-3B-Instruct",
+  "architectures": [
+    "Qwen2_5_VLForConditionalGeneration"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "eos_token_id": 151645,
+  "hidden_act": "silu",
+  "hidden_size": 2048,
+  "image_token_id": 151655,
+  "initializer_range": 0.02,
+  "intermediate_size": 11008,
+  "max_position_embeddings": 128000,
+  "max_window_layers": 70,
+  "model_type": "qwen2_5_vl",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 36,
+  "num_key_value_heads": 2,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": {
+    "mrope_section": [
+      16,
+      24,
+      24
+    ],
+    "rope_type": "default",
+    "type": "default"
+  },
+  "rope_theta": 1000000.0,
+  "sliding_window": 32768,
+  "tie_word_embeddings": true,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.49.0",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "video_token_id": 151656,
+  "vision_config": {
+    "hidden_size": 1280,
+    "in_chans": 3,
+    "model_type": "qwen2_5_vl",
+    "out_hidden_size": 2048,
+    "spatial_patch_size": 14,
+    "tokens_per_second": 2,
+    "torch_dtype": "bfloat16"
+  },
+  "vision_end_token_id": 151653,
+  "vision_start_token_id": 151652,
+  "vision_token_id": 151654,
+  "vocab_size": 151936
+}

1.pretrain/generation_config.json

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+  "temperature": 0.1,
+  "top_k": 1,
+  "top_p": 0.001,
+  "transformers_version": "4.49.0"
+}

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+    "visual.blocks.9.attn.qkv.weight": "model-00001-of-00002.safetensors",
+    "visual.blocks.9.mlp.down_proj.bias": "model-00001-of-00002.safetensors",
+    "visual.blocks.9.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
+    "visual.blocks.9.mlp.gate_proj.bias": "model-00001-of-00002.safetensors",
+    "visual.blocks.9.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
+    "visual.blocks.9.mlp.up_proj.bias": "model-00001-of-00002.safetensors",
+    "visual.blocks.9.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
+    "visual.blocks.9.norm1.weight": "model-00001-of-00002.safetensors",
+    "visual.blocks.9.norm2.weight": "model-00001-of-00002.safetensors",
+    "visual.merger.ln_q.weight": "model-00001-of-00002.safetensors",
+    "visual.merger.mlp.0.bias": "model-00001-of-00002.safetensors",
+    "visual.merger.mlp.0.weight": "model-00001-of-00002.safetensors",
+    "visual.merger.mlp.2.bias": "model-00001-of-00002.safetensors",
+    "visual.merger.mlp.2.weight": "model-00001-of-00002.safetensors",
+    "visual.patch_embed.proj.weight": "model-00001-of-00002.safetensors"
+  }
+}

1.pretrain/preprocessor_config.json

@@ -0,0 +1,29 @@
+{
+  "do_convert_rgb": true,
+  "do_normalize": true,
+  "do_rescale": true,
+  "do_resize": true,
+  "image_mean": [
+    0.48145466,
+    0.4578275,
+    0.40821073
+  ],
+  "image_processor_type": "Qwen2VLImageProcessor",
+  "image_std": [
+    0.26862954,
+    0.26130258,
+    0.27577711
+  ],
+  "max_pixels": 12845056,
+  "merge_size": 2,
+  "min_pixels": 3136,
+  "patch_size": 14,
+  "processor_class": "Qwen2_5_VLProcessor",
+  "resample": 3,
+  "rescale_factor": 0.00392156862745098,
+  "size": {
+    "longest_edge": 12845056,
+    "shortest_edge": 3136
+  },
+  "temporal_patch_size": 2
+}

1.pretrain/special_tokens_map.json

@@ -0,0 +1,31 @@
+{
+  "additional_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>",
+    "<|object_ref_start|>",
+    "<|object_ref_end|>",
+    "<|box_start|>",
+    "<|box_end|>",
+    "<|quad_start|>",
+    "<|quad_end|>",
+    "<|vision_start|>",
+    "<|vision_end|>",
+    "<|vision_pad|>",
+    "<|image_pad|>",
+    "<|video_pad|>"
+  ],
+  "eos_token": {
+    "content": "<|im_end|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

1.pretrain/tokenizer.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9c5ae00e602b8860cbd784ba82a8aa14e8feecec692e7076590d014d7b7fdafa
+size 11421896

1.pretrain/tokenizer_config.json

@@ -0,0 +1,210 @@
+{
+  "add_bos_token": false,
+  "add_prefix_space": false,
+  "added_tokens_decoder": {
+    "151643": {
+      "content": "<|endoftext|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151644": {
+      "content": "<|im_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151645": {
+      "content": "<|im_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151646": {
+      "content": "<|object_ref_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151647": {
+      "content": "<|object_ref_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151648": {
+      "content": "<|box_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151649": {
+      "content": "<|box_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151650": {
+      "content": "<|quad_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151651": {
+      "content": "<|quad_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151652": {
+      "content": "<|vision_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151653": {
+      "content": "<|vision_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151654": {
+      "content": "<|vision_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151655": {
+      "content": "<|image_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151656": {
+      "content": "<|video_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151657": {
+      "content": "<tool_call>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151658": {
+      "content": "</tool_call>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151659": {
+      "content": "<|fim_prefix|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151660": {
+      "content": "<|fim_middle|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151661": {
+      "content": "<|fim_suffix|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151662": {
+      "content": "<|fim_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151663": {
+      "content": "<|repo_name|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151664": {
+      "content": "<|file_sep|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    }
+  },
+  "additional_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>",
+    "<|object_ref_start|>",
+    "<|object_ref_end|>",
+    "<|box_start|>",
+    "<|box_end|>",
+    "<|quad_start|>",
+    "<|quad_end|>",
+    "<|vision_start|>",
+    "<|vision_end|>",
+    "<|vision_pad|>",
+    "<|image_pad|>",
+    "<|video_pad|>"
+  ],
+  "bos_token": null,
+  "chat_template": "{%- if tools %}\n    {{- '<|im_start|>system\\n' }}\n    {%- if messages[0]['role'] == 'system' %}\n        {{- messages[0]['content'] }}\n    {%- else %}\n        {{- 'You are a helpful assistant.' }}\n    {%- endif %}\n    {{- \"\\n\\n# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n    {%- for tool in tools %}\n        {{- \"\\n\" }}\n        {{- tool | tojson }}\n    {%- endfor %}\n    {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><|im_end|>\\n\" }}\n{%- else %}\n    {%- if messages[0]['role'] == 'system' %}\n        {{- '<|im_start|>system\\n' + messages[0]['content'] + '<|im_end|>\\n' }}\n    {%- else %}\n        {{- '<|im_start|>system\\nYou are a helpful assistant.<|im_end|>\\n' }}\n    {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n    {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and not message.tool_calls) %}\n        {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n    {%- elif message.role == \"assistant\" %}\n        {{- '<|im_start|>' + message.role }}\n        {%- if message.content %}\n            {{- '\\n' + message.content }}\n        {%- endif %}\n        {%- for tool_call in message.tool_calls %}\n            {%- if tool_call.function is defined %}\n                {%- set tool_call = tool_call.function %}\n            {%- endif %}\n            {{- '\\n<tool_call>\\n{\"name\": \"' }}\n            {{- tool_call.name }}\n            {{- '\", \"arguments\": ' }}\n            {{- tool_call.arguments | tojson }}\n            {{- '}\\n</tool_call>' }}\n        {%- endfor %}\n        {{- '<|im_end|>\\n' }}\n    {%- elif message.role == \"tool\" %}\n        {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != \"tool\") %}\n            {{- '<|im_start|>user' }}\n        {%- endif %}\n        {{- '\\n<tool_response>\\n' }}\n        {{- message.content }}\n        {{- '\\n</tool_response>' }}\n        {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n            {{- '<|im_end|>\\n' }}\n        {%- endif %}\n    {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n    {{- '<|im_start|>assistant\\n' }}\n{%- endif %}\n",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "extra_special_tokens": {},
+  "model_max_length": 2048,
+  "pad_token": "<|endoftext|>",
+  "padding_side": "left",
+  "processor_class": "Qwen2_5_VLProcessor",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null
+}

2.sft.py

@@ -0,0 +1,99 @@
+from unsloth import FastVisionModel # FastLanguageModel for LLMs
+import torch
+from datasets import load_dataset
+from unsloth import is_bf16_supported
+from unsloth.trainer import UnslothVisionDataCollator
+from trl import SFTTrainer, SFTConfig
+from transformers import TextStreamer
+import datetime
+timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
+
+model, tokenizer = FastVisionModel.from_pretrained(
+    model_name = "/home/rzhong/project/unsloth/model_pretrain_20250301_113944",
+    load_in_4bit = False, # Use 4bit to reduce memory use. False for 16bit LoRA.
+                          # 模型已经选了4bit量化后的，这里还需不需要再以4bit加载？建议实验一下 | 应该是一个意思
+    use_gradient_checkpointing = "unsloth", # True or "unsloth" for long context
+    max_seq_length = 2048, # unsloth支持4x的上下文微调，如果原模型支持8192的上下文，这里只需要设置为2048
+    dtype = torch.bfloat16, # A100支持bfloat16，可以减少显存占用。默认为None，也可以选择torch.float16
+)
+
+model = FastVisionModel.get_peft_model(
+    model,
+    finetune_vision_layers     = True, # False if not finetuning vision layers
+    finetune_language_layers   = True, # False if not finetuning language layers
+    finetune_attention_modules = True, # False if not finetuning attention layers
+    finetune_mlp_modules       = True, # False if not finetuning MLP layers
+    
+    r = 16,           # The larger, the higher the accuracy, but might overfit
+    lora_alpha = 16,  # Recommended alpha == r at least
+    lora_dropout = 0,
+    bias = "none",
+    random_state = 3407,
+    use_rslora = False,  # We support rank stabilized LoRA
+    loftq_config = None, # And LoftQ
+    # target_modules = "all-linear", # Optional now! Can specify a list if needed
+    target_modules = ["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"],
+)
+
+dataset = load_dataset("/home/share/rzhong/dataset/google-landmark/dataset_4/dataset_file", split = "train")
+print(dataset)
+
+instruction = "描述这张图片。"
+# instruction = "Write the LaTeX representation for this image."
+
+def convert_to_conversation(sample):
+    conversation = [
+        { "role": "user",
+          "content" : [
+            {"type" : "text",  "text"  : instruction},
+            {"type" : "image", "image" : sample["image"]} ]
+        },
+        { "role" : "assistant",
+          "content" : [
+            {"type" : "text",  "text"  : sample["text"]} ]
+        },
+    ]
+    return { "messages" : conversation }
+pass
+
+converted_dataset = [convert_to_conversation(sample) for sample in dataset]
+
+print(converted_dataset[0])
+
+FastVisionModel.for_training(model) # Enable for training!
+
+trainer = SFTTrainer(
+    model = model,
+    tokenizer = tokenizer,
+    data_collator = UnslothVisionDataCollator(model, tokenizer), # Must use!
+    train_dataset = converted_dataset,
+    args = SFTConfig(
+        per_device_train_batch_size = 2,
+        gradient_accumulation_steps = 4, # 原来是4。可以增加，相当于提高batch size，但不会影响内存消耗。增加会使loss曲线更平滑
+        warmup_steps = 5,
+        # max_steps = None,
+        num_train_epochs = 10, # Set this instead of max_steps for full training runs
+        learning_rate = 5e-5, # 2e-4 1e-4 5e-5 2e-5
+        fp16 = not is_bf16_supported(),
+        bf16 = is_bf16_supported(),
+        logging_steps = 1,
+        optim = "adamw_8bit",
+        weight_decay = 0.01,
+        lr_scheduler_type = "linear",
+        seed = 3407,
+        output_dir = f"outputs_pretrain_sft_{timestamp}",
+        report_to = "none",     # For Weights and Biases
+
+        # You MUST put the below items for vision finetuning:
+        remove_unused_columns = False,
+        dataset_text_field = "",
+        dataset_kwargs = {"skip_prepare_dataset": True},
+        dataset_num_proc = 4,
+        max_seq_length = 2048,
+    ),
+)
+
+trainer_stats = trainer.train()
+
+model.save_pretrained(f"lora_model_pretrain_sft_{timestamp}")  # Local saving
+tokenizer.save_pretrained(f"lora_model_pretrain_sft_{timestamp}")

2.sft/Modelfile

@@ -0,0 +1,16 @@
+# ollama modelfile auto-generated by llamafactory
+
+FROM .
+
+TEMPLATE """{{ if .System }}<|im_start|>system
+{{ .System }}<|im_end|>
+{{ end }}{{ range .Messages }}{{ if eq .Role "user" }}<|im_start|>user
+{{ .Content }}<|im_end|>
+<|im_start|>assistant
+{{ else if eq .Role "assistant" }}{{ .Content }}<|im_end|>
+{{ end }}{{ end }}"""
+
+SYSTEM """You are a helpful assistant."""
+
+PARAMETER stop "<|im_end|>"
+PARAMETER num_ctx 4096

2.sft/added_tokens.json

@@ -0,0 +1,24 @@
+{
+  "</tool_call>": 151658,
+  "<tool_call>": 151657,
+  "<|box_end|>": 151649,
+  "<|box_start|>": 151648,
+  "<|endoftext|>": 151643,
+  "<|file_sep|>": 151664,
+  "<|fim_middle|>": 151660,
+  "<|fim_pad|>": 151662,
+  "<|fim_prefix|>": 151659,
+  "<|fim_suffix|>": 151661,
+  "<|im_end|>": 151645,
+  "<|im_start|>": 151644,
+  "<|image_pad|>": 151655,
+  "<|object_ref_end|>": 151647,
+  "<|object_ref_start|>": 151646,
+  "<|quad_end|>": 151651,
+  "<|quad_start|>": 151650,
+  "<|repo_name|>": 151663,
+  "<|video_pad|>": 151656,
+  "<|vision_end|>": 151653,
+  "<|vision_pad|>": 151654,
+  "<|vision_start|>": 151652
+}

2.sft/chat_template.json

@@ -0,0 +1,3 @@
+{
+  "chat_template": "{% set image_count = namespace(value=0) %}{% set video_count = namespace(value=0) %}{% for message in messages %}{% if loop.first and message['role'] != 'system' %}<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n{% endif %}<|im_start|>{{ message['role'] }}\n{% if message['content'] is string %}{{ message['content'] }}<|im_end|>\n{% else %}{% for content in message['content'] %}{% if content['type'] == 'image' or 'image' in content or 'image_url' in content %}{% set image_count.value = image_count.value + 1 %}{% if add_vision_id %}Picture {{ image_count.value }}: {% endif %}<|vision_start|><|image_pad|><|vision_end|>{% elif content['type'] == 'video' or 'video' in content %}{% set video_count.value = video_count.value + 1 %}{% if add_vision_id %}Video {{ video_count.value }}: {% endif %}<|vision_start|><|video_pad|><|vision_end|>{% elif 'text' in content %}{{ content['text'] }}{% endif %}{% endfor %}<|im_end|>\n{% endif %}{% endfor %}{% if add_generation_prompt %}<|im_start|>assistant\n{% endif %}"
+}

2.sft/config.json

@@ -0,0 +1,51 @@
+{
+  "_name_or_path": "/home/rzhong/project/unsloth/model_pretrain_20250301_113944",
+  "architectures": [
+    "Qwen2_5_VLForConditionalGeneration"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "eos_token_id": 151645,
+  "hidden_act": "silu",
+  "hidden_size": 2048,
+  "image_token_id": 151655,
+  "initializer_range": 0.02,
+  "intermediate_size": 11008,
+  "max_position_embeddings": 128000,
+  "max_window_layers": 70,
+  "model_type": "qwen2_5_vl",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 36,
+  "num_key_value_heads": 2,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": {
+    "mrope_section": [
+      16,
+      24,
+      24
+    ],
+    "rope_type": "default",
+    "type": "default"
+  },
+  "rope_theta": 1000000.0,
+  "sliding_window": 32768,
+  "tie_word_embeddings": true,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.49.0",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "video_token_id": 151656,
+  "vision_config": {
+    "hidden_size": 1280,
+    "in_chans": 3,
+    "model_type": "qwen2_5_vl",
+    "out_hidden_size": 2048,
+    "spatial_patch_size": 14,
+    "tokens_per_second": 2,
+    "torch_dtype": "bfloat16"
+  },
+  "vision_end_token_id": 151653,
+  "vision_start_token_id": 151652,
+  "vision_token_id": 151654,
+  "vocab_size": 151936
+}

2.sft/generation_config.json

@@ -0,0 +1,14 @@
+{
+  "bos_token_id": 151643,
+  "do_sample": true,
+  "eos_token_id": [
+    151645,
+    151643
+  ],
+  "pad_token_id": 151643,
+  "repetition_penalty": 1.05,
+  "temperature": 0.1,
+  "top_k": 1,
+  "top_p": 0.001,
+  "transformers_version": "4.49.0"
+}

2.sft/model-00001-of-00002.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0ace795f82fee51ceff72f3ddd3510bd1a1219b4de0288c2227c14ef00fe7dcf
+size 4997750760

2.sft/model-00002-of-00002.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:48b8ae05de641845750d7060aa1b6c6c67bd0f9d5bff14fa265345c81883daa1
+size 2511587184

2.sft/model.safetensors.index.json

@@ -0,0 +1,831 @@
+{
+  "metadata": {
+    "total_size": 7509245952
+  },
+  "weight_map": {
+    "model.embed_tokens.weight": "model-00001-of-00002.safetensors",
+    "model.layers.0.input_layernorm.weight": "model-00001-of-00002.safetensors",
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+  "chat_template": "{%- if tools %}\n    {{- '<|im_start|>system\\n' }}\n    {%- if messages[0]['role'] == 'system' %}\n        {{- messages[0]['content'] }}\n    {%- else %}\n        {{- 'You are a helpful assistant.' }}\n    {%- endif %}\n    {{- \"\\n\\n# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n    {%- for tool in tools %}\n        {{- \"\\n\" }}\n        {{- tool | tojson }}\n    {%- endfor %}\n    {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><|im_end|>\\n\" }}\n{%- else %}\n    {%- if messages[0]['role'] == 'system' %}\n        {{- '<|im_start|>system\\n' + messages[0]['content'] + '<|im_end|>\\n' }}\n    {%- else %}\n        {{- '<|im_start|>system\\nYou are a helpful assistant.<|im_end|>\\n' }}\n    {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n    {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and not message.tool_calls) %}\n        {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n    {%- elif message.role == \"assistant\" %}\n        {{- '<|im_start|>' + message.role }}\n        {%- if message.content %}\n            {{- '\\n' + message.content }}\n        {%- endif %}\n        {%- for tool_call in message.tool_calls %}\n            {%- if tool_call.function is defined %}\n                {%- set tool_call = tool_call.function %}\n            {%- endif %}\n            {{- '\\n<tool_call>\\n{\"name\": \"' }}\n            {{- tool_call.name }}\n            {{- '\", \"arguments\": ' }}\n            {{- tool_call.arguments | tojson }}\n            {{- '}\\n</tool_call>' }}\n        {%- endfor %}\n        {{- '<|im_end|>\\n' }}\n    {%- elif message.role == \"tool\" %}\n        {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != \"tool\") %}\n            {{- '<|im_start|>user' }}\n        {%- endif %}\n        {{- '\\n<tool_response>\\n' }}\n        {{- message.content }}\n        {{- '\\n</tool_response>' }}\n        {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n            {{- '<|im_end|>\\n' }}\n        {%- endif %}\n    {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n    {{- '<|im_start|>assistant\\n' }}\n{%- endif %}\n",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "extra_special_tokens": {},
+  "model_max_length": 2048,
+  "pad_token": "<|endoftext|>",
+  "padding_side": "left",
+  "processor_class": "Qwen2_5_VLProcessor",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null
+}

3.prune.py

@@ -0,0 +1,124 @@
+import torch
+from transformers import Qwen2VLForConditionalGeneration, Qwen2_5_VLForConditionalGeneration, AutoProcessor
+import torch_pruning as tp
+from qwen_vl_utils import process_vision_info
+from transformers.models.qwen2_5_vl.modeling_qwen2_5_vl import Qwen2_5_VLPatchMerger
+from torch import nn
+from typing import Sequence
+import os
+
+def prune_model(model, processor, pruning_ratio):
+    """同步剪枝LM和视觉模块，确保维度对齐"""
+
+    num_heads = {}
+    for name, module in model.named_modules():
+        if name.endswith("self_attn"):
+            num_heads[module.q_proj] = model.config.num_attention_heads
+            num_heads[module.k_proj] = model.config.num_key_value_heads
+            num_heads[module.v_proj] = model.config.num_key_value_heads
+
+    importance = tp.importance.GroupNormImportance(p=2, group_reduction='mean') #tp.importance.ActivationImportance(p=2, target_types=[torch.nn.Linear])
+    
+    # 处理未封装的参数
+    unwrapped_parameters = []
+
+    # 忽略最后的lm_head和LM部分的embedding 
+    ignored_layers = []
+    for m in model.modules():
+        if isinstance(m, torch.nn.Linear) and m.out_features == 151936:
+            ignored_layers.append(m)
+        if isinstance(m, torch.nn.Embedding):
+            ignored_layers.append(m)
+    print("ignored_layers", ignored_layers)
+
+    # 构建输入
+    # example_inputs = torch.randint(0, 100000, (3, 56, 56), dtype=torch.long, device='cuda:1')
+    
+    text = "描述这张图片。"
+    example_inputs = torch.tensor(processor.tokenizer.encode(text)).unsqueeze(0).to(model.device)
+    print(example_inputs.shape)
+
+    # 创建剪枝器
+    model.config.use_cache = False
+    pruner = tp.pruner.MetaPruner(
+        model,
+        example_inputs=example_inputs,
+        importance=importance,
+        global_pruning=False,
+        pruning_ratio=pruning_ratio,
+        ignored_layers=ignored_layers,
+        num_heads=num_heads,
+        prune_num_heads=False,
+        prune_head_dims=False,
+        head_pruning_ratio=pruning_ratio,
+        round_to=4,
+        unwrapped_parameters=unwrapped_parameters,
+    )
+    
+    # 执行剪枝
+    for g in pruner.step(interactive=True):
+        # print(g)
+        g.prune()
+
+    model.config.hidden_size = model.lm_head.in_features
+    for name, m in model.model.named_modules():
+        if name.endswith("self_attn"):
+            print(name)
+            m.hidden_size = m.q_proj.out_features
+            m.num_heads = m.hidden_size // m.head_dim
+            model.config.num_attention_heads = m.num_heads
+            m.num_key_value_groups = m.num_heads // m.num_key_value_heads
+        elif name.endswith("mlp"):
+            if hasattr(m, "gate_proj"):
+                print(name)
+                m.hidden_size = m.gate_proj.in_features
+                model.config.intermediate_size = m.gate_proj.out_features
+
+    return model
+            
+def main():
+    model_path = "/home/rzhong/project/unsloth/model_pretrain_sft_20250303_125849"
+    # model_path = "/home/rzhong/project/FSTSPrune/Qwen2.5-VL-3B-Instruct-LatexOCR"
+    model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
+        model_path,
+        torch_dtype=torch.bfloat16,
+        device_map="cuda:1"
+    )
+    processor = AutoProcessor.from_pretrained(model_path)
+
+    print("========= Before Pruning =========")
+    print(model)
+    ori_size = tp.utils.count_params(model)
+
+    print("Starting pruning process...")
+    pruned_model = prune_model(model, processor, pruning_ratio=0.5)
+    print("========= After Pruning =========")
+    print(pruned_model)
+
+    print("  Params: %.2f M => %.2f M" %
+          (ori_size / 1e6, tp.utils.count_params(pruned_model) / 1e6))
+    
+    # pruned_model.zero_grad()
+    # save_path = "/home/rzhong/project/FSTSPrune/model_pretrain_sft_20250303_125849-Pruned"
+    # os.makedirs(save_path, exist_ok=True)
+    # # pruned_model.save_pretrained(save_path)
+    # torch.save(pruned_model, os.path.join(save_path, "pytorch_model.bin"))
+    # processor.save_pretrained(save_path)
+    # pruned_model.config.save_pretrained(save_path)
+
+    # pruned_model.zero_grad()
+    # save_path = "/home/rzhong/project/FSTSPrune/model_pretrain_sft_20250303_125849-Pruned-hf"
+    # os.makedirs(save_path, exist_ok=True)
+    # pruned_model.save_pretrained(save_path)
+    # # torch.save(pruned_model, os.path.join(save_path, "pytorch_model.bin"))
+    # processor.save_pretrained(save_path)
+    # # pruned_model.config.save_pretrained(save_path)
+
+    # load_test_model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
+    #     save_path,
+    #     device_map="cpu"
+    # )
+    # print("load test pass!")
+
+if __name__ == "__main__":
+    main()

3.prune/added_tokens.json

@@ -0,0 +1,24 @@
+{
+  "</tool_call>": 151658,
+  "<tool_call>": 151657,
+  "<|box_end|>": 151649,
+  "<|box_start|>": 151648,
+  "<|endoftext|>": 151643,
+  "<|file_sep|>": 151664,
+  "<|fim_middle|>": 151660,
+  "<|fim_pad|>": 151662,
+  "<|fim_prefix|>": 151659,
+  "<|fim_suffix|>": 151661,
+  "<|im_end|>": 151645,
+  "<|im_start|>": 151644,
+  "<|image_pad|>": 151655,
+  "<|object_ref_end|>": 151647,
+  "<|object_ref_start|>": 151646,
+  "<|quad_end|>": 151651,
+  "<|quad_start|>": 151650,
+  "<|repo_name|>": 151663,
+  "<|video_pad|>": 151656,
+  "<|vision_end|>": 151653,
+  "<|vision_pad|>": 151654,
+  "<|vision_start|>": 151652
+}

3.prune/chat_template.json

@@ -0,0 +1,3 @@
+{
+  "chat_template": "{% set image_count = namespace(value=0) %}{% set video_count = namespace(value=0) %}{% for message in messages %}{% if loop.first and message['role'] != 'system' %}<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n{% endif %}<|im_start|>{{ message['role'] }}\n{% if message['content'] is string %}{{ message['content'] }}<|im_end|>\n{% else %}{% for content in message['content'] %}{% if content['type'] == 'image' or 'image' in content or 'image_url' in content %}{% set image_count.value = image_count.value + 1 %}{% if add_vision_id %}Picture {{ image_count.value }}: {% endif %}<|vision_start|><|image_pad|><|vision_end|>{% elif content['type'] == 'video' or 'video' in content %}{% set video_count.value = video_count.value + 1 %}{% if add_vision_id %}Video {{ video_count.value }}: {% endif %}<|vision_start|><|video_pad|><|vision_end|>{% elif 'text' in content %}{{ content['text'] }}{% endif %}{% endfor %}<|im_end|>\n{% endif %}{% endfor %}{% if add_generation_prompt %}<|im_start|>assistant\n{% endif %}"
+}

3.prune/config.json

@@ -0,0 +1,51 @@
+{
+  "_name_or_path": "/home/rzhong/project/unsloth/model_pretrain_sft_20250303_125849",
+  "architectures": [
+    "Qwen2_5_VLForConditionalGeneration"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "eos_token_id": 151645,
+  "hidden_act": "silu",
+  "hidden_size": 2048,
+  "image_token_id": 151655,
+  "initializer_range": 0.02,
+  "intermediate_size": 5504,
+  "max_position_embeddings": 128000,
+  "max_window_layers": 70,
+  "model_type": "qwen2_5_vl",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 36,
+  "num_key_value_heads": 2,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": {
+    "mrope_section": [
+      16,
+      24,
+      24
+    ],
+    "rope_type": "default",
+    "type": "default"
+  },
+  "rope_theta": 1000000.0,
+  "sliding_window": 32768,
+  "tie_word_embeddings": true,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.49.0",
+  "use_cache": false,
+  "use_sliding_window": false,
+  "video_token_id": 151656,
+  "vision_config": {
+    "hidden_size": 1280,
+    "in_chans": 3,
+    "model_type": "qwen2_5_vl",
+    "out_hidden_size": 2048,
+    "spatial_patch_size": 14,
+    "tokens_per_second": 2,
+    "torch_dtype": "bfloat16"
+  },
+  "vision_end_token_id": 151653,
+  "vision_start_token_id": 151652,
+  "vision_token_id": 151654,
+  "vocab_size": 151936
+}

3.prune/generation_config.json

@@ -0,0 +1,14 @@
+{
+  "bos_token_id": 151643,
+  "do_sample": true,
+  "eos_token_id": [
+    151645,
+    151643
+  ],
+  "pad_token_id": 151643,
+  "repetition_penalty": 1.05,
+  "temperature": 0.1,
+  "top_k": 1,
+  "top_p": 0.001,
+  "transformers_version": "4.49.0"
+}

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+    "visual.blocks.7.mlp.down_proj.bias": "model-00001-of-00002.safetensors",
+    "visual.blocks.7.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
+    "visual.blocks.7.mlp.gate_proj.bias": "model-00001-of-00002.safetensors",
+    "visual.blocks.7.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
+    "visual.blocks.7.mlp.up_proj.bias": "model-00001-of-00002.safetensors",
+    "visual.blocks.7.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
+    "visual.blocks.7.norm1.weight": "model-00001-of-00002.safetensors",
+    "visual.blocks.7.norm2.weight": "model-00001-of-00002.safetensors",
+    "visual.blocks.8.attn.proj.bias": "model-00001-of-00002.safetensors",
+    "visual.blocks.8.attn.proj.weight": "model-00001-of-00002.safetensors",
+    "visual.blocks.8.attn.qkv.bias": "model-00001-of-00002.safetensors",
+    "visual.blocks.8.attn.qkv.weight": "model-00001-of-00002.safetensors",
+    "visual.blocks.8.mlp.down_proj.bias": "model-00001-of-00002.safetensors",
+    "visual.blocks.8.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
+    "visual.blocks.8.mlp.gate_proj.bias": "model-00001-of-00002.safetensors",
+    "visual.blocks.8.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
+    "visual.blocks.8.mlp.up_proj.bias": "model-00001-of-00002.safetensors",
+    "visual.blocks.8.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
+    "visual.blocks.8.norm1.weight": "model-00001-of-00002.safetensors",
+    "visual.blocks.8.norm2.weight": "model-00001-of-00002.safetensors",
+    "visual.blocks.9.attn.proj.bias": "model-00001-of-00002.safetensors",
+    "visual.blocks.9.attn.proj.weight": "model-00001-of-00002.safetensors",
+    "visual.blocks.9.attn.qkv.bias": "model-00001-of-00002.safetensors",
+    "visual.blocks.9.attn.qkv.weight": "model-00001-of-00002.safetensors",
+    "visual.blocks.9.mlp.down_proj.bias": "model-00001-of-00002.safetensors",
+    "visual.blocks.9.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
+    "visual.blocks.9.mlp.gate_proj.bias": "model-00001-of-00002.safetensors",
+    "visual.blocks.9.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
+    "visual.blocks.9.mlp.up_proj.bias": "model-00001-of-00002.safetensors",
+    "visual.blocks.9.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
+    "visual.blocks.9.norm1.weight": "model-00001-of-00002.safetensors",
+    "visual.blocks.9.norm2.weight": "model-00001-of-00002.safetensors",
+    "visual.merger.ln_q.weight": "model-00001-of-00002.safetensors",
+    "visual.merger.mlp.0.bias": "model-00001-of-00002.safetensors",
+    "visual.merger.mlp.0.weight": "model-00001-of-00002.safetensors",
+    "visual.merger.mlp.2.bias": "model-00001-of-00002.safetensors",
+    "visual.merger.mlp.2.weight": "model-00001-of-00002.safetensors",
+    "visual.patch_embed.proj.weight": "model-00001-of-00002.safetensors"
+  }
+}

3.prune/preprocessor_config.json

@@ -0,0 +1,29 @@
+{
+  "do_convert_rgb": true,
+  "do_normalize": true,
+  "do_rescale": true,
+  "do_resize": true,
+  "image_mean": [
+    0.48145466,
+    0.4578275,
+    0.40821073
+  ],
+  "image_processor_type": "Qwen2VLImageProcessor",
+  "image_std": [
+    0.26862954,
+    0.26130258,
+    0.27577711
+  ],
+  "max_pixels": 12845056,
+  "merge_size": 2,
+  "min_pixels": 3136,
+  "patch_size": 14,
+  "processor_class": "Qwen2_5_VLProcessor",
+  "resample": 3,
+  "rescale_factor": 0.00392156862745098,
+  "size": {
+    "longest_edge": 12845056,
+    "shortest_edge": 3136
+  },
+  "temporal_patch_size": 2
+}

3.prune/special_tokens_map.json

@@ -0,0 +1,31 @@
+{
+  "additional_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>",
+    "<|object_ref_start|>",
+    "<|object_ref_end|>",
+    "<|box_start|>",
+    "<|box_end|>",
+    "<|quad_start|>",
+    "<|quad_end|>",
+    "<|vision_start|>",
+    "<|vision_end|>",
+    "<|vision_pad|>",
+    "<|image_pad|>",
+    "<|video_pad|>"
+  ],
+  "eos_token": {
+    "content": "<|im_end|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

3.prune/tokenizer.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9c5ae00e602b8860cbd784ba82a8aa14e8feecec692e7076590d014d7b7fdafa
+size 11421896

3.prune/tokenizer_config.json

@@ -0,0 +1,210 @@
+{
+  "add_bos_token": false,
+  "add_prefix_space": false,
+  "added_tokens_decoder": {
+    "151643": {
+      "content": "<|endoftext|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151644": {
+      "content": "<|im_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151645": {
+      "content": "<|im_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151646": {
+      "content": "<|object_ref_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151647": {
+      "content": "<|object_ref_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151648": {
+      "content": "<|box_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151649": {
+      "content": "<|box_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151650": {
+      "content": "<|quad_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151651": {
+      "content": "<|quad_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151652": {
+      "content": "<|vision_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151653": {
+      "content": "<|vision_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151654": {
+      "content": "<|vision_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151655": {
+      "content": "<|image_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151656": {
+      "content": "<|video_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151657": {
+      "content": "<tool_call>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151658": {
+      "content": "</tool_call>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151659": {
+      "content": "<|fim_prefix|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151660": {
+      "content": "<|fim_middle|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151661": {
+      "content": "<|fim_suffix|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151662": {
+      "content": "<|fim_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151663": {
+      "content": "<|repo_name|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151664": {
+      "content": "<|file_sep|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    }
+  },
+  "additional_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>",
+    "<|object_ref_start|>",
+    "<|object_ref_end|>",
+    "<|box_start|>",
+    "<|box_end|>",
+    "<|quad_start|>",
+    "<|quad_end|>",
+    "<|vision_start|>",
+    "<|vision_end|>",
+    "<|vision_pad|>",
+    "<|image_pad|>",
+    "<|video_pad|>"
+  ],
+  "bos_token": null,
+  "chat_template": "{%- if tools %}\n    {{- '<|im_start|>system\\n' }}\n    {%- if messages[0]['role'] == 'system' %}\n        {{- messages[0]['content'] }}\n    {%- else %}\n        {{- 'You are a helpful assistant.' }}\n    {%- endif %}\n    {{- \"\\n\\n# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n    {%- for tool in tools %}\n        {{- \"\\n\" }}\n        {{- tool | tojson }}\n    {%- endfor %}\n    {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><|im_end|>\\n\" }}\n{%- else %}\n    {%- if messages[0]['role'] == 'system' %}\n        {{- '<|im_start|>system\\n' + messages[0]['content'] + '<|im_end|>\\n' }}\n    {%- else %}\n        {{- '<|im_start|>system\\nYou are a helpful assistant.<|im_end|>\\n' }}\n    {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n    {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and not message.tool_calls) %}\n        {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n    {%- elif message.role == \"assistant\" %}\n        {{- '<|im_start|>' + message.role }}\n        {%- if message.content %}\n            {{- '\\n' + message.content }}\n        {%- endif %}\n        {%- for tool_call in message.tool_calls %}\n            {%- if tool_call.function is defined %}\n                {%- set tool_call = tool_call.function %}\n            {%- endif %}\n            {{- '\\n<tool_call>\\n{\"name\": \"' }}\n            {{- tool_call.name }}\n            {{- '\", \"arguments\": ' }}\n            {{- tool_call.arguments | tojson }}\n            {{- '}\\n</tool_call>' }}\n        {%- endfor %}\n        {{- '<|im_end|>\\n' }}\n    {%- elif message.role == \"tool\" %}\n        {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != \"tool\") %}\n            {{- '<|im_start|>user' }}\n        {%- endif %}\n        {{- '\\n<tool_response>\\n' }}\n        {{- message.content }}\n        {{- '\\n</tool_response>' }}\n        {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n            {{- '<|im_end|>\\n' }}\n        {%- endif %}\n    {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n    {{- '<|im_start|>assistant\\n' }}\n{%- endif %}\n",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "extra_special_tokens": {},
+  "model_max_length": 2048,
+  "pad_token": "<|endoftext|>",
+  "padding_side": "left",
+  "processor_class": "Qwen2_5_VLProcessor",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null
+}

4.prune-finetune.py

@@ -0,0 +1,212 @@
+from unsloth import FastVisionModel # FastLanguageModel for LLMs
+import torch
+from datasets import load_dataset
+from unsloth import is_bf16_supported
+from unsloth.trainer import UnslothVisionDataCollator
+from trl import SFTTrainer, SFTConfig
+from transformers import TextStreamer
+from PIL import Image
+import requests
+from io import BytesIO
+import datetime
+timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
+
+model, tokenizer = FastVisionModel.from_pretrained(
+    model_name = "/home/rzhong/project/FSTSPrune/model_pretrain_sft_20250303_125849-Pruned-hf",
+    load_in_4bit = False, # Use 4bit to reduce memory use. False for 16bit LoRA.
+                          # 模型已经选了4bit量化后的，这里还需不需要再以4bit加载？建议实验一下 | 应该是一个意思
+    use_gradient_checkpointing = "unsloth", # True or "unsloth" for long context
+    max_seq_length = 2048, # unsloth支持4x的上下文微调，如果原模型支持8192的上下文，这里只需要设置为2048
+    dtype = torch.bfloat16, # A100支持bfloat16，可以减少显存占用。默认为None，也可以选择torch.float16
+)
+
+model = FastVisionModel.get_peft_model(
+    model,
+    finetune_vision_layers     = True, # False if not finetuning vision layers
+    finetune_language_layers   = True, # False if not finetuning language layers
+    finetune_attention_modules = True, # False if not finetuning attention layers
+    finetune_mlp_modules       = True, # False if not finetuning MLP layers
+    
+    r = 16,           # The larger, the higher the accuracy, but might overfit
+    lora_alpha = 16,  # Recommended alpha == r at least
+    lora_dropout = 0,
+    bias = "none",
+    random_state = 3407,
+    use_rslora = False,  # We support rank stabilized LoRA
+    loftq_config = None, # And LoftQ
+    # target_modules = "all-linear", # Optional now! Can specify a list if needed
+    target_modules = ["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"],
+)
+
+# 数据集1处理
+def process_dataset1():
+    dataset = load_dataset("/home/share/rzhong/dataset/Qwen10k", split="train").select(range(200))
+    
+    def convert(sample):
+        return {
+            "messages": [
+                {
+                    "role": "user",
+                    "content": [
+                        {"type": "text", "text": sample["question"]},
+                        {"type": "image", "image": sample["image"]}
+                    ]
+                },
+                {
+                    "role": "assistant",
+                    "content": [{"type": "text", "text": sample["chosen"]}]
+                }
+            ]
+        }
+    
+    return [convert(sample) for sample in dataset]
+
+# 数据集2处理（带图片下载和压缩）
+def process_dataset2():
+    dataset = load_dataset(
+        "json",
+        data_files="/home/share/rzhong/dataset/ALLaVA-4V-Chinese/allava_laion/ALLaVA-Instruct-LAION-4V_Chinese.json",
+        split="train"
+    ).select(range(200))
+    
+    def convert(sample):
+        try:
+            # 下载并处理图片
+            url = sample['url'].split('?')[0]
+            response = requests.get(url, timeout=5)
+            response.raise_for_status()
+            image = Image.open(BytesIO(response.content))
+            
+            # 图片压缩处理
+            image = image.resize((320, 240), Image.LANCZOS).convert('RGB')
+            
+            return {
+                "messages": [
+                    {
+                        "role": "user",
+                        "content": [
+                            {"type": "text", "text": sample['conversations'][0]['value']},
+                            {"type": "image", "image": image}
+                        ]
+                    },
+                    {
+                        "role": "assistant",
+                        "content": [{"type": "text", "text": sample['conversations'][1]['value']}]
+                    }
+                ]
+            }
+        except Exception as e:
+            print(f"跳过样本: {url}，错误: {str(e)}")
+            return None
+    
+    # 过滤无效样本
+    return [conv for sample in dataset if (conv := convert(sample)) is not None]
+
+# 数据集3处理
+def process_dataset3():
+    dataset = load_dataset("/home/share/rzhong/dataset/google-landmark/dataset_4/dataset_file", split="train")
+    instruction = "描述这张图片。"
+    
+    def convert(sample):
+        return {
+            "messages": [
+                {
+                    "role": "user",
+                    "content": [
+                        {"type": "text", "text": instruction},
+                        {"type": "image", "image": sample["image"]}
+                    ]
+                },
+                {
+                    "role": "assistant",
+                    "content": [{"type": "text", "text": sample["text"]}]
+                }
+            ]
+        }
+    
+    return [convert(sample) for sample in dataset]
+
+# 数据集4���理
+def process_dataset4():
+    dataset = load_dataset("/home/share/rzhong/dataset/R1-Vision-PixMo-Cap-QA-zh", split="train").select(range(200))
+
+    def convert(sample):
+        try:
+            image = Image.open(BytesIO(requests.get(sample['image_url'], timeout=5).content))
+            image = image.resize((320, 240), Image.LANCZOS).convert('RGB')
+            return {
+                "messages": [
+                    {
+                        "role": "user",
+                        "content": [
+                            {"type": "text", "text": sample['question']},
+                            {"type": "image", "image": image}
+                        ]
+                    },
+                    {
+                        "role": "assistant",
+                        "content": [{"type": "text", "text": sample['r1_solution']}]
+                    }
+                ]
+            }
+        except Exception as e:
+            print(f"跳过样本: {sample['image_url']}，错误: {str(e)}")
+            return None
+    
+    return [conv for sample in dataset if (conv := convert(sample)) is not None]
+
+# 合并所有数据集
+def merge_datasets():
+    dataset1 = process_dataset1()
+    dataset2 = process_dataset2()
+    dataset3 = process_dataset3()
+    dataset4 = process_dataset4()
+    
+    merged_dataset = dataset1 + dataset2 + dataset3 + dataset4
+    print(f"总样本数: {len(merged_dataset)}")
+    print("前3个样本预览:")
+    for i in range(min(3, len(merged_dataset))):
+        print(merged_dataset[i])
+    
+    return merged_dataset
+
+# 执行合并
+merged_data = merge_datasets()
+
+FastVisionModel.for_training(model) # Enable for training!
+
+trainer = SFTTrainer(
+    model = model,
+    tokenizer = tokenizer,
+    data_collator = UnslothVisionDataCollator(model, tokenizer), # Must use!
+    train_dataset = merged_data,
+    args = SFTConfig(
+        per_device_train_batch_size = 2,
+        gradient_accumulation_steps = 4, # 原来是4。可以增加，相当于提高batch size，但不会影响内存消耗。增加会使loss曲线更平滑
+        warmup_steps = 500,
+        # max_steps = None,
+        num_train_epochs = 10, # Set this instead of max_steps for full training runs
+        learning_rate = 5e-5, # 2e-4 1e-4 5e-5 2e-5
+        fp16 = not is_bf16_supported(),
+        bf16 = is_bf16_supported(),
+        logging_steps = 1,
+        optim = "adamw_8bit",
+        weight_decay = 0.01,
+        lr_scheduler_type = "linear",
+        seed = 3407,
+        output_dir = "model_pretrain_sft_20250303_125849-Pruned-hf-lora-output6",
+        report_to = "none",     # For Weights and Biases
+
+        # You MUST put the below items for vision finetuning:
+        remove_unused_columns = False,
+        dataset_text_field = "",
+        dataset_kwargs = {"skip_prepare_dataset": True},
+        dataset_num_proc = 16,
+        max_seq_length = 2048,
+    ),
+)
+
+trainer_stats = trainer.train()
+
+model.save_pretrained("model_pretrain_sft_20250303_125849-Pruned-hf-lora6")  # Local saving
+tokenizer.save_pretrained("model_pretrain_sft_20250303_125849-Pruned-hf-lora6")

4.prune-finetune/Modelfile

@@ -0,0 +1,16 @@
+# ollama modelfile auto-generated by llamafactory
+
+FROM .
+
+TEMPLATE """{{ if .System }}<|im_start|>system
+{{ .System }}<|im_end|>
+{{ end }}{{ range .Messages }}{{ if eq .Role "user" }}<|im_start|>user
+{{ .Content }}<|im_end|>
+<|im_start|>assistant
+{{ else if eq .Role "assistant" }}{{ .Content }}<|im_end|>
+{{ end }}{{ end }}"""
+
+SYSTEM """You are a helpful assistant."""
+
+PARAMETER stop "<|im_end|>"
+PARAMETER num_ctx 4096

4.prune-finetune/added_tokens.json

@@ -0,0 +1,24 @@
+{
+  "</tool_call>": 151658,
+  "<tool_call>": 151657,
+  "<|box_end|>": 151649,
+  "<|box_start|>": 151648,
+  "<|endoftext|>": 151643,
+  "<|file_sep|>": 151664,
+  "<|fim_middle|>": 151660,
+  "<|fim_pad|>": 151662,
+  "<|fim_prefix|>": 151659,
+  "<|fim_suffix|>": 151661,
+  "<|im_end|>": 151645,
+  "<|im_start|>": 151644,
+  "<|image_pad|>": 151655,
+  "<|object_ref_end|>": 151647,
+  "<|object_ref_start|>": 151646,
+  "<|quad_end|>": 151651,
+  "<|quad_start|>": 151650,
+  "<|repo_name|>": 151663,
+  "<|video_pad|>": 151656,
+  "<|vision_end|>": 151653,
+  "<|vision_pad|>": 151654,
+  "<|vision_start|>": 151652
+}

4.prune-finetune/chat_template.json

@@ -0,0 +1,3 @@
+{
+  "chat_template": "{% set image_count = namespace(value=0) %}{% set video_count = namespace(value=0) %}{% for message in messages %}{% if loop.first and message['role'] != 'system' %}<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n{% endif %}<|im_start|>{{ message['role'] }}\n{% if message['content'] is string %}{{ message['content'] }}<|im_end|>\n{% else %}{% for content in message['content'] %}{% if content['type'] == 'image' or 'image' in content or 'image_url' in content %}{% set image_count.value = image_count.value + 1 %}{% if add_vision_id %}Picture {{ image_count.value }}: {% endif %}<|vision_start|><|image_pad|><|vision_end|>{% elif content['type'] == 'video' or 'video' in content %}{% set video_count.value = video_count.value + 1 %}{% if add_vision_id %}Video {{ video_count.value }}: {% endif %}<|vision_start|><|video_pad|><|vision_end|>{% elif 'text' in content %}{{ content['text'] }}{% endif %}{% endfor %}<|im_end|>\n{% endif %}{% endfor %}{% if add_generation_prompt %}<|im_start|>assistant\n{% endif %}"
+}

4.prune-finetune/config.json

@@ -0,0 +1,51 @@
+{
+  "_name_or_path": "/home/rzhong/project/FSTSPrune/model_pretrain_sft_20250303_125849-Pruned-hf",
+  "architectures": [
+    "Qwen2_5_VLForConditionalGeneration"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "eos_token_id": 151645,
+  "hidden_act": "silu",
+  "hidden_size": 2048,
+  "image_token_id": 151655,
+  "initializer_range": 0.02,
+  "intermediate_size": 5504,
+  "max_position_embeddings": 128000,
+  "max_window_layers": 70,
+  "model_type": "qwen2_5_vl",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 36,
+  "num_key_value_heads": 2,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": {
+    "mrope_section": [
+      16,
+      24,
+      24
+    ],
+    "rope_type": "default",
+    "type": "default"
+  },
+  "rope_theta": 1000000.0,
+  "sliding_window": 32768,
+  "tie_word_embeddings": true,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.49.0",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "video_token_id": 151656,
+  "vision_config": {
+    "hidden_size": 1280,
+    "in_chans": 3,
+    "model_type": "qwen2_5_vl",
+    "out_hidden_size": 2048,
+    "spatial_patch_size": 14,
+    "tokens_per_second": 2,
+    "torch_dtype": "bfloat16"
+  },
+  "vision_end_token_id": 151653,
+  "vision_start_token_id": 151652,
+  "vision_token_id": 151654,
+  "vocab_size": 151936
+}