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Model: chaoyinshe/llava-med-v1.5-mistral-7b-hf Source: Original Platform
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README.md
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README.md
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---
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license: apache-2.0
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base_model:
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- microsoft/llava-med-v1.5-mistral-7b
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pipeline_tag: visual-question-answering
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language:
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- en
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tags:
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- medical
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- biology
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---
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# llava-med-v1.5-mistral-7b-hf
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This repository contains a **drop-in, Hugging Face–compatible** checkpoint converted from
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[https://huggingface.co/microsoft/llava-med-v1.5-mistral-7b](https://huggingface.co/microsoft/llava-med-v1.5-mistral-7b).
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You can load it with the **exact same code** you use for the original model—no extra conversion steps required.
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---
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## ✅ Clarification on Vocab Size Expansion & Weight Integrity
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You can refer to the original conversion code we provided to access the relevant implementation details and code snippets.
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You may wonder if the vocab size mismatch (e.g., 32000 vs. 32064) breaks the original weights by causing dimension mismatches between embed_tokens/lm_head and the original weights.
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The answer is no. Below are key clarifications:
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### 1. Does expanding embed_tokens affect the attention mechanism?
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No. The embed_tokens layer is simply an embedding lookup table (nn.Embedding) that maps token IDs to vectors.
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The attention mechanism (e.g., nn.MultiheadAttention or LlamaAttention) does not operate on this lookup table directly. It only cares if the dimension of input hidden states is consistent.
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Thus, even if you add more tokens, as long as the hidden_size remains unchanged, the weight shape of the attention layer is not affected at all.
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### 2. Which layers actually change after expansion?
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| Module | Weight Shape Change | Affects Attention Calculation? |
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|----------------------|--------------------------------------|---------------------------------|
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| embed_tokens | [vocab_size+2, hidden_size] | ❌ No |
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| lm_head | [vocab_size+2, hidden_size] | ❌ No |
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| All attention layers | No shape change | ✅ Completely unchanged |
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#### Analogy for easier understanding
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Think of the model as a dictionary:
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- embed_tokens is the "new word list": You add two new words, but the length of each word’s explanation (hidden_size) stays the same.
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- Attention is the "reading rule": It only focuses on how the vectors of each word in a sentence interact with each other, not how many words are in the dictionary.
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#### The only "change" lies in input distribution
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- The original model never encountered embeddings for tokens like `<image>` or `<pad>`.
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- Now these tokens are initialized, and their new vectors appear during the first forward pass.
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- This is a data-level change, not damage to the model’s parameters.
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#### Summary
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Expanding the vocab only changes the "dictionary size", not the "reading rules". The weight shape and calculation logic of the attention mechanism remain completely unchanged. The output layer (lm_head) changes in shape but not in functional logic.
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### 3. What exactly changes in the output layer (lm_head)?
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The line below modifies the weight matrix of lm_head (the language modeling head):
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```python
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model.resize_token_embeddings(config.text_config.vocab_size + 2, pad_shape)
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```
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## Quick Start
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```python
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from transformers import LlavaForConditionalGeneration, AutoProcessor
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import torch
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model_path = "chaoyinshe/llava-med-v1.5-mistral-7b-hf"
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model = LlavaForConditionalGeneration.from_pretrained(
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model_path,
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torch_dtype=torch.bfloat16,
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attn_implementation="flash_attention_2", # requires FA2
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device_map="auto" # multi-GPU ready
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)
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processor = AutoProcessor.from_pretrained(model_path)
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# Example inference
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messages = [
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{
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"role": "user",
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"content": [
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{"type": "image"},
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{"type": "text", "text": "What is the main finding in this chest X-ray?"}
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]
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}
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]
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prompt = processor.tokenizer.apply_chat_template(
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messages, tokenize=False, add_generation_prompt=True
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)
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inputs = processor(
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images=[image], text=prompt, return_tensors="pt"
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).to(model.device, torch.bfloat16)
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with torch.inference_mode():
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out = model.generate(**inputs, max_new_tokens=256)
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print(processor.decode(out[0], skip_special_tokens=True))
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```
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## ✅ Training Screenshot
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(Note: The image below is for illustrative purposes only — actual training metrics may vary.)
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🤗
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4
added_tokens.json
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added_tokens.json
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{
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"<image>": 32000,
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"<pad>": 32001
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}
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3
chat_template.json
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chat_template.json
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{
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"chat_template": "{% for message in messages %}{% if message['role'] == 'system' %}{{ '<<SYS>>\n' + message['content'][0]['text'] + '\n<</SYS>>\n\n' }}{% elif message['role'] == 'user' %}{{ '[INST] ' }}{# Render all images first #}{% for content in message['content'] | selectattr('type', 'equalto', 'image') %}{{ '<image>\n' }}{% endfor %}{# Render all text next #}{% for content in message['content'] | selectattr('type', 'equalto', 'text') %}{{ content['text'] }}{% endfor %}{{' [/INST]' }}{% elif message['role'] == 'assistant' %}{{message['content'][0]['text'] + '</s>'}}{% else %}{{ raise_exception('Only user and assistant roles are supported!') }}{% endif %}{% endfor %}"
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}
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config.json
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config.json
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{
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"architectures": [
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"LlavaForConditionalGeneration"
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],
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"image_seq_length": 576,
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"image_token_index": 32000,
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"model_type": "llava",
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"multimodal_projector_bias": true,
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"pad_token_id": 32001,
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"projector_hidden_act": "gelu",
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"text_config": {
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"_name_or_path": "mistralai/Mistral-7B-Instruct-v0.2",
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"architectures": [
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"MistralForCausalLM"
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],
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"attention_dropout": 0.0,
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"head_dim": null,
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"hidden_act": "silu",
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"hidden_size": 4096,
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"initializer_range": 0.02,
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"intermediate_size": 14336,
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"max_position_embeddings": 32768,
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"model_type": "mistral",
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"num_attention_heads": 32,
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"num_hidden_layers": 32,
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"num_key_value_heads": 8,
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"rms_norm_eps": 1e-05,
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"rope_theta": 1000000.0,
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"sliding_window": null,
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"torch_dtype": "bfloat16",
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"use_cache": true,
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"vocab_size": 32064
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},
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"torch_dtype": "bfloat16",
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"transformers_version": "4.53.0",
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"vision_config": {
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"attention_dropout": 0.0,
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"hidden_act": "quick_gelu",
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"hidden_size": 1024,
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"image_size": 336,
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"initializer_factor": 1.0,
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"initializer_range": 0.02,
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"intermediate_size": 4096,
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"layer_norm_eps": 1e-05,
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"model_type": "clip_vision_model",
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"num_attention_heads": 16,
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"num_channels": 3,
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"num_hidden_layers": 24,
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"patch_size": 14,
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"projection_dim": 768,
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"vocab_size": 32000
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},
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"vision_feature_layer": -2,
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"vision_feature_select_strategy": "default"
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}
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248
convert_llava_weights_to_hf.py
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convert_llava_weights_to_hf.py
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# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import argparse
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import glob
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import torch
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from huggingface_hub import file_exists, hf_hub_download, snapshot_download
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from safetensors import safe_open
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from transformers import (
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AddedToken,
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AutoConfig,
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AutoImageProcessor,
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AutoTokenizer,
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LlavaConfig,
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LlavaProcessor,
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SiglipVisionConfig,
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)
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from modeling_llavamed import LlavaForConditionalGeneration
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EPILOG_TXT = """Example:
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python transformers/src/transformers/models/llava/convert_llava_weights_to_hf.py --text_model_id lmsys/vicuna-7b-v1.5 --vision_model_id openai/clip-vit-large-patch14-336 --output_hub_path org/llava-v1.5-7b-conv --old_state_dict_id liuhaotian/llava-v1.5-7b
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Example for creating the old state dict file with Python:
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import torch
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from llava.model.language_model.llava_llama import LlavaLlamaForCausalLM
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# load model
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kwargs = {"device_map": "auto", "dtype": torch.float16}
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model = LlavaLlamaForCausalLM.from_pretrained("liuhaotian/llava-v1.5-7b", **kwargs)
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# load vision tower
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model.get_vision_tower().load_model()
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# Save state dict
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torch.save(model.state_dict(), "tmp/hf_models/llava-v1.5-7b/model_state_dict.bin")
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"""
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KEYS_TO_MODIFY_MAPPING = {
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"model.vision_tower.": "",
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".vision_resampler": "", # all lmms-lab models do avg pooling, so no vision_resampler
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"model.mm_projector": "multi_modal_projector",
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"model": "model.model",
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"vision_model.model": "vision_model",
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"lm_head": "language_model.lm_head",
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"model.model": "language_model.model",
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"multi_modal_projector.0": "multi_modal_projector.linear_1",
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"multi_modal_projector.2": "multi_modal_projector.linear_2",
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}
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def load_original_state_dict(directory_path):
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# directory_path = snapshot_download(repo_id=model_id, allow_patterns=["*.safetensors"])
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original_state_dict = {}
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for path in glob.glob(f"{directory_path}/*"):
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if path.endswith(".safetensors"):
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with safe_open(path, framework="pt", device="cpu") as f:
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for key in f.keys():
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original_state_dict[key] = f.get_tensor(key)
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# tied weights so lm.head is not saved. Let's clone to load state dict
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if "lm_head.weight" not in original_state_dict:
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original_state_dict["lm_head.weight"] = original_state_dict["model.embed_tokens.weight"].clone()
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if "model.image_newline" in original_state_dict:
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# not used in the original implementation because "merge_type=flat"
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del original_state_dict["model.image_newline"]
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return original_state_dict
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# used only for llava-interlave
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# for ex: Qwen/Qwen1.5-0.5B-Chat google/siglip-so400m-patch14-384 lmms-lab/llava-next-interleave-qwen-0.5b
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# def convert_state_dict_to_hf(state_dict):
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# new_state_dict = {}
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# for key, value in state_dict.items():
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# if key.endswith(".inv_freq"):
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# continue
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# for key_to_modify, new_key in KEYS_TO_MODIFY_MAPPING.items():
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# if key_to_modify in key:
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# key = key.replace(key_to_modify, new_key)
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#
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# new_state_dict[key] = value
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# return new_state_dict
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def convert_state_dict_to_hf(state_dict):
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new_state_dict = {}
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for key, value in state_dict.items():
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if key.endswith(".inv_freq"):
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continue
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# ✅ 按顺序匹配，只替换一次
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if key.startswith("model.vision_tower."):
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new_key = key.replace("model.vision_tower.vision_tower", "model.vision_tower")#'model.vision_tower.vision_tower.vision_model.embeddings.class_embedding'
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elif key.startswith("model.mm_projector.0."):
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new_key = key.replace("model.mm_projector.0.", "model.multi_modal_projector.linear_1.")
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elif key.startswith("model.mm_projector.2."):
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new_key = key.replace("model.mm_projector.2.", "model.multi_modal_projector.linear_2.")
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elif key.startswith("model.embed_tokens."):
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new_key = key.replace("model.embed_tokens.", "model.language_model.embed_tokens.")
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elif key.startswith("model.layers."):
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new_key = key.replace("model.layers.", "model.language_model.layers.")
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elif key.startswith("model.norm."):
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new_key = key.replace("model.norm.", "model.language_model.norm.")
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# elif key.startswith("lm_head."):
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# new_key = key.replace("lm_head.", "language_model.lm_head.")
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else:
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new_key = key # 保留未匹配的 key（如 vision_tower 内部）
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new_state_dict[new_key] = value
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return new_state_dict
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def convert_llava_llama_to_hf(text_model_id, vision_model_id, output_path, old_state_dict_id):
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torch.set_default_dtype(torch.float16)
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text_config = AutoConfig.from_pretrained(text_model_id)
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tokenizer = AutoTokenizer.from_pretrained(text_model_id)
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tokenizer.add_tokens(AddedToken("<image>", special=True, normalized=False), special_tokens=True)
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if "Qwen" not in text_model_id: # qwen already has a pad token
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tokenizer.add_special_tokens({"pad_token": "<pad>"})
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image_processor = AutoImageProcessor.from_pretrained(vision_model_id)
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processor = LlavaProcessor(tokenizer=tokenizer, image_processor=image_processor)
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if "siglip" in vision_model_id:
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vision_config = SiglipVisionConfig(
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hidden_size=1152,
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image_size=384,
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intermediate_size=4304,
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num_attention_heads=16,
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num_hidden_layers=26,
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patch_size=14,
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vision_use_head=False,
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).to_dict()
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else:
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vision_config = None
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config = LlavaConfig(
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text_config=text_config,
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vision_config=vision_config,
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)
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# llms-lab interleave models do not use any selection strategy except for last hidden state
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if "Qwen" in text_model_id:
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config.image_token_id = 151646
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if "siglip" in vision_model_id:
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config.vision_feature_select_strategy = "full"
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config.vision_feature_layer = -1
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else:
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config.pad_token_id = 32001
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config.image_token_id = 32000
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with torch.device("meta"):
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model = LlavaForConditionalGeneration(config)
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# Some llava variants like microsoft/llava-med-v1.5-mistral-7b use safetensors to store weights
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# if file_exists(old_state_dict_id, "model_state_dict.bin"):
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# state_dict_path = hf_hub_download(old_state_dict_id, "model_state_dict.bin")
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# state_dict = torch.load(state_dict_path, map_location="cpu", weights_only=True)
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# else:
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state_dict = load_original_state_dict(old_state_dict_id)
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expected_keys = set(model.state_dict().keys())
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provided_keys = set(state_dict.keys())
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missing_keys = expected_keys - provided_keys
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unexpected_keys = provided_keys - expected_keys
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print(f"✅ Total expected keys: {len(expected_keys)}")
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||||
print(f"✅ Provided keys: {len(provided_keys)}")
|
||||
if missing_keys:
|
||||
print("⚠️ Missing keys (not in original weights):")
|
||||
for k in sorted(missing_keys):
|
||||
print(f" - {k}")
|
||||
else:
|
||||
print("✅ No missing keys.")
|
||||
|
||||
if unexpected_keys:
|
||||
print("❓ Unexpected keys (in original but not in HF model):")
|
||||
for k in sorted(unexpected_keys):
|
||||
print(f" - {k}")
|
||||
|
||||
state_dict = convert_state_dict_to_hf(state_dict)
|
||||
model.load_state_dict(state_dict, strict=True, assign=True)
|
||||
|
||||
pre_expansion_embeddings = model.language_model.embed_tokens.weight.data
|
||||
mu = torch.mean(pre_expansion_embeddings, dim=0).float()
|
||||
n = pre_expansion_embeddings.size()[0]
|
||||
sigma = ((pre_expansion_embeddings - mu).T @ (pre_expansion_embeddings - mu)) / n
|
||||
dist = torch.distributions.multivariate_normal.MultivariateNormal(mu, covariance_matrix=1e-5 * sigma)
|
||||
|
||||
# We add an image token so we resize the model and pad to 64 for performance reasons
|
||||
pad_shape = 64
|
||||
vocab_size = config.text_config.vocab_size
|
||||
model.resize_token_embeddings(config.text_config.vocab_size + 2, pad_shape)
|
||||
model.language_model.embed_tokens.weight.data[vocab_size:] = torch.stack(
|
||||
tuple(dist.sample() for _ in range(model.language_model.embed_tokens.weight.data[vocab_size:].shape[0])),
|
||||
dim=0,
|
||||
)
|
||||
model.lm_head.weight.data[vocab_size:] = torch.stack(
|
||||
tuple(dist.sample() for _ in range(model.lm_head.weight.data[vocab_size:].shape[0])),
|
||||
dim=0,
|
||||
)
|
||||
model.save_pretrained(output_path, safe_serialization=True)
|
||||
|
||||
# 2. 保存 processor（tokenizer + image_processor）
|
||||
processor.save_pretrained(output_path)
|
||||
|
||||
def main():
|
||||
parser = argparse.ArgumentParser(
|
||||
epilog=EPILOG_TXT,
|
||||
formatter_class=argparse.RawDescriptionHelpFormatter,
|
||||
)
|
||||
parser.add_argument(
|
||||
"--text_model_id",default='/data/scy/SCY/Model_weights/Mistral-7B-Instruct-v0.2',
|
||||
help="Hub location of the text model",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--vision_model_id",default='/data/scy/SCY/Model_weights/clip-vit-large-patch14-336',
|
||||
help="Hub location of the vision model",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--output_path",default='/data/scy/SCY/SonoVLM_V2/llavamed/revised',
|
||||
help="Location on the hub of the converted model",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--old_state_dict_id",default='/data/scy/SCY/Model_weights/llava-med-v1.5-mistral-7b-original',
|
||||
help="Location on the hub of the raw state dict of the original model. The filename needs to be `model_state_dict.bin`",
|
||||
)
|
||||
args = parser.parse_args()
|
||||
convert_llava_llama_to_hf(args.text_model_id, args.vision_model_id, args.output_path, args.old_state_dict_id)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
7
generation_config.json
Normal file
7
generation_config.json
Normal file
@@ -0,0 +1,7 @@
|
||||
{
|
||||
"_from_model_config": true,
|
||||
"bos_token_id": 1,
|
||||
"eos_token_id": 2,
|
||||
"pad_token_id": 32001,
|
||||
"transformers_version": "4.53.0"
|
||||
}
|
||||
3
model-00001-of-00004.safetensors
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3
model-00001-of-00004.safetensors
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@@ -0,0 +1,3 @@
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version https://git-lfs.github.com/spec/v1
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3
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@@ -0,0 +1,3 @@
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version https://git-lfs.github.com/spec/v1
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version https://git-lfs.github.com/spec/v1
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version https://git-lfs.github.com/spec/v1
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oid sha256:fb4a00008716c0774241b63a3d402fd7064e793781334988071a6ff1b5854d06
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size 380134008
|
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694
model.safetensors.index.json
Normal file
694
model.safetensors.index.json
Normal file
@@ -0,0 +1,694 @@
|
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{
|
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|
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|
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|
||||
}
|
||||
}
|
||||
28
preprocessor_config.json
Normal file
28
preprocessor_config.json
Normal file
@@ -0,0 +1,28 @@
|
||||
{
|
||||
"crop_size": {
|
||||
"height": 336,
|
||||
"width": 336
|
||||
},
|
||||
"do_center_crop": true,
|
||||
"do_convert_rgb": true,
|
||||
"do_normalize": true,
|
||||
"do_rescale": true,
|
||||
"do_resize": true,
|
||||
"image_mean": [
|
||||
0.48145466,
|
||||
0.4578275,
|
||||
0.40821073
|
||||
],
|
||||
"image_processor_type": "CLIPImageProcessor",
|
||||
"image_std": [
|
||||
0.26862954,
|
||||
0.26130258,
|
||||
0.27577711
|
||||
],
|
||||
"processor_class": "LlavaProcessor",
|
||||
"resample": 3,
|
||||
"rescale_factor": 0.00392156862745098,
|
||||
"size": {
|
||||
"shortest_edge": 336
|
||||
}
|
||||
}
|
||||
7
processor_config.json
Normal file
7
processor_config.json
Normal file
@@ -0,0 +1,7 @@
|
||||
{
|
||||
"image_token": "<image>",
|
||||
"num_additional_image_tokens": 0,
|
||||
"patch_size": 14,
|
||||
"processor_class": "LlavaProcessor",
|
||||
"vision_feature_select_strategy": null
|
||||
}
|
||||
30
special_tokens_map.json
Normal file
30
special_tokens_map.json
Normal file
@@ -0,0 +1,30 @@
|
||||
{
|
||||
"bos_token": {
|
||||
"content": "<s>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"eos_token": {
|
||||
"content": "</s>",
|
||||
"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
|
||||
}
|
||||
}
|
||||
268071
tokenizer.json
Normal file
268071
tokenizer.json
Normal file
File diff suppressed because it is too large
Load Diff
BIN
tokenizer.model
(Stored with Git LFS)
Normal file
BIN
tokenizer.model
(Stored with Git LFS)
Normal file
Binary file not shown.
61
tokenizer_config.json
Normal file
61
tokenizer_config.json
Normal file
@@ -0,0 +1,61 @@
|
||||
{
|
||||
"add_bos_token": true,
|
||||
"add_eos_token": false,
|
||||
"add_prefix_space": null,
|
||||
"added_tokens_decoder": {
|
||||
"0": {
|
||||
"content": "<unk>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"1": {
|
||||
"content": "<s>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"2": {
|
||||
"content": "</s>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32000": {
|
||||
"content": "<image>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32001": {
|
||||
"content": "<pad>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
}
|
||||
},
|
||||
"additional_special_tokens": [],
|
||||
"bos_token": "<s>",
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "</s>",
|
||||
"extra_special_tokens": {},
|
||||
"legacy": false,
|
||||
"model_max_length": 1000000000000000019884624838656,
|
||||
"pad_token": "<pad>",
|
||||
"processor_class": "LlavaProcessor",
|
||||
"sp_model_kwargs": {},
|
||||
"spaces_between_special_tokens": false,
|
||||
"tokenizer_class": "LlamaTokenizer",
|
||||
"unk_token": "<unk>",
|
||||
"use_default_system_prompt": false
|
||||
}
|
||||
Reference in New Issue
Block a user