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  ---
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  license: mit
 
 
 
 
 
 
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  ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
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  license: mit
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+ datasets:
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+ - liuhaotian/LLaVA-Instruct-150K
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+ - liuhaotian/LLaVA-Pretrain
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+ language:
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+ - en
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+ pipeline_tag: visual-question-answering
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  ---
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+
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+ # Model Card for Model ID
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+
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+ This is a multimodal implementation of [Phi2](https://huggingface.co/microsoft/phi-2) model inspired by [LlaVA-Phi](https://github.com/zhuyiche/llava-phi).
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+
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+ ## Model Details
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+ 1. LLM Backbone: [Phi2](https://huggingface.co/microsoft/phi-2)
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+ 2. Vision Tower: [clip-vit-large-patch14-336](https://huggingface.co/openai/clip-vit-large-patch14-336)
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+ 4. Pretraining Dataset: [LAION-CC-SBU dataset with BLIP captions(200k samples)](https://huggingface.co/datasets/liuhaotian/LLaVA-Pretrain)
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+ 5. Finetuning Dataset: [Instruct 150k dataset based on COCO](https://huggingface.co/datasets/liuhaotian/LLaVA-Instruct-150K)
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+ 6. Finetuned Model: [RaviNaik/Llava-Phi2](https://huggingface.co/RaviNaik/Llava-Phi2)
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+
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+
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+ ### Model Sources
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+
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+ <!-- Provide the basic links for the model. -->
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+
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+ - **Original Repository:** [Llava-Phi](https://github.com/zhuyiche/llava-phi)
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+ - **Paper [optional]:** [LLaVA-Phi: Efficient Multi-Modal Assistant with Small Language Model](https://arxiv.org/pdf/2401.02330)
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+ - **Demo [optional]:** [Demo Link](https://huggingface.co/spaces/RaviNaik/MultiModal-Phi2)
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+
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+
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+ ## How to Get Started with the Model
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+
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+ Use the code below to get started with the model.
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+ 1. Clone this repository and navigate to llava-phi folder
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+ ```bash
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+ git clone https://github.com/zhuyiche/llava-phi.git
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+ cd llava-phi
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+ ```
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+ 2. Install Package
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+ ```bash
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+ conda create -n llava_phi python=3.10 -y
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+ conda activate llava_phi
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+ pip install --upgrade pip # enable PEP 660 support
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+ pip install -e .
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+ ```
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+ 3. Run the Model
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+ ```bash
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+ python llava_phi/eval/run_llava_phi.py --model-path="RaviNaik/Llava-Phi2" \
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+ --image-file="https://huggingface.co/RaviNaik/Llava-Phi2/resolve/main/people.jpg?download=true" \
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+ --query="How many people are there in the image?"
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+ ```
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+
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+ ### Acknowledgement
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+ This implementation is based on wonderful work done by: \
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+ [LlaVA-Phi](https://github.com/zhuyiche/llava-phi) \
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+ [Llava](https://github.com/haotian-liu/LLaVA) \
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+ [Phi2](https://huggingface.co/microsoft/phi-2)
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+ "50281": {
206
+ "content": " ",
207
+ "lstrip": false,
208
+ "normalized": true,
209
+ "rstrip": false,
210
+ "single_word": false,
211
+ "special": false
212
+ },
213
+ "50282": {
214
+ "content": " ",
215
+ "lstrip": false,
216
+ "normalized": true,
217
+ "rstrip": false,
218
+ "single_word": false,
219
+ "special": false
220
+ },
221
+ "50283": {
222
+ "content": " ",
223
+ "lstrip": false,
224
+ "normalized": true,
225
+ "rstrip": false,
226
+ "single_word": false,
227
+ "special": false
228
+ },
229
+ "50284": {
230
+ "content": " ",
231
+ "lstrip": false,
232
+ "normalized": true,
233
+ "rstrip": false,
234
+ "single_word": false,
235
+ "special": false
236
+ },
237
+ "50285": {
238
+ "content": " ",
239
+ "lstrip": false,
240
+ "normalized": true,
241
+ "rstrip": false,
242
+ "single_word": false,
243
+ "special": false
244
+ },
245
+ "50286": {
246
+ "content": " ",
247
+ "lstrip": false,
248
+ "normalized": true,
249
+ "rstrip": false,
250
+ "single_word": false,
251
+ "special": false
252
+ },
253
+ "50287": {
254
+ "content": "\t\t\t\t\t\t\t\t\t",
255
+ "lstrip": false,
256
+ "normalized": true,
257
+ "rstrip": false,
258
+ "single_word": false,
259
+ "special": false
260
+ },
261
+ "50288": {
262
+ "content": "\t\t\t\t\t\t\t\t",
263
+ "lstrip": false,
264
+ "normalized": true,
265
+ "rstrip": false,
266
+ "single_word": false,
267
+ "special": false
268
+ },
269
+ "50289": {
270
+ "content": "\t\t\t\t\t\t\t",
271
+ "lstrip": false,
272
+ "normalized": true,
273
+ "rstrip": false,
274
+ "single_word": false,
275
+ "special": false
276
+ },
277
+ "50290": {
278
+ "content": "\t\t\t\t\t\t",
279
+ "lstrip": false,
280
+ "normalized": true,
281
+ "rstrip": false,
282
+ "single_word": false,
283
+ "special": false
284
+ },
285
+ "50291": {
286
+ "content": "\t\t\t\t\t",
287
+ "lstrip": false,
288
+ "normalized": true,
289
+ "rstrip": false,
290
+ "single_word": false,
291
+ "special": false
292
+ },
293
+ "50292": {
294
+ "content": "\t\t\t\t",
295
+ "lstrip": false,
296
+ "normalized": true,
297
+ "rstrip": false,
298
+ "single_word": false,
299
+ "special": false
300
+ },
301
+ "50293": {
302
+ "content": "\t\t\t",
303
+ "lstrip": false,
304
+ "normalized": true,
305
+ "rstrip": false,
306
+ "single_word": false,
307
+ "special": false
308
+ },
309
+ "50294": {
310
+ "content": "\t\t",
311
+ "lstrip": false,
312
+ "normalized": true,
313
+ "rstrip": false,
314
+ "single_word": false,
315
+ "special": false
316
+ }
317
+ },
318
+ "bos_token": "<|endoftext|>",
319
+ "clean_up_tokenization_spaces": true,
320
+ "eos_token": "<|endoftext|>",
321
+ "errors": "replace",
322
+ "model_max_length": 2048,
323
+ "pad_token": "<|endoftext|>",
324
+ "padding_side": "right",
325
+ "tokenizer_class": "CodeGenTokenizer",
326
+ "unk_token": "<|endoftext|>"
327
+ }
vocab.json ADDED
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