Update handler

.gitattributes

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README.md

@@ -1,71 +0,0 @@
----
-license: cc-by-sa-4.0
-language:
-- en
-tags:
-- Multimodal
-- StableLM
-datasets:
-- LDJnr/Capybara
-- LDJnr/LessWrong-Amplify-Instruct
-- LDJnr/Pure-Dove
-- LDJnr/Verified-Camel
----
-
-# Obsidian: Worlds smallest multi-modal LLM. First multi-modal model in size 3B
-
-## Model Name: Obsidian-3B-V0.5
-
-Obsidian is a brand new series of Multimodal Language Models. This first project is led by Quan N. and Luigi D.(LDJ).
-
-Obsidian-3B-V0.5 is a multi-modal AI model that has vision! it's smarts are built on [Capybara-3B-V1.9](https://huggingface.co/NousResearch/Capybara-3B-V1.9) based on [StableLM-3B-4e1t](https://huggingface.co/stabilityai/stablelm-3b-4e1t). Capybara-3B-V1.9 achieves state-of-the-art performance when compared to model with similar size, even beats some 7B models.
-
-Current finetuning and inference code is available on our GitHub repo: [Here](https://github.com/NousResearch/Obsidian)
-
-## Acknowledgement
-
-Obsidian-3B-V0.5 was developed and finetuned by [Nous Research](https://huggingface.co/NousResearch), in collaboration with [Virtual Interactive](https://huggingface.co/vilm).
-Special thank you to **LDJ** for the wonderful Capybara dataset, and **qnguyen3** for the model training procedure.
-## Model Training
-
-Obsidian-3B-V0.5 followed the same training procedure as LLaVA 1.5
-
-## Prompt Format
-
-The model followed ChatML format. However, with `###` as the seperator
-
-```
-<|im_start|>user
-What is this sign about?\n<image>
-###
-<|im_start|>assistant
-The sign is about bullying, and it is placed on a black background with a red background.
-###
-```
-
-## Benchmarks
-
-Coming Soon!
-
-
-Citation:
-
-```
-@article{nguyen2023Obsidian-3B,
-  title={Obsidian-3B: First Multi-modal below 7B Parameters.},
-  author={Nguyen, Quan and Daniele},
-  journal={HuggingFace:https://huggingface.co/NousResearch/Obsidian-3B-V0.5},
-  year={2023}
-}
-```
-
-Acknowledgements:
-
-```
-@article{daniele2023amplify-instruct,
-  title={Amplify-Instruct: Synthetically Generated Diverse Multi-turn Conversations for Effecient LLM Training.},
-  author={Daniele, Luigi and Suphavadeeprasit},
-  journal={arXiv preprint arXiv:(comming soon)},
-  year={2023}
-}
-```

config.json

@@ -1,39 +0,0 @@
-{
-  "_name_or_path": "NousResearch/Obsidian-3B-V0.5",
-  "architectures": [
-    "LlavaStableLMEpochForCausalLM"
-  ],
-  "bos_token_id": 0,
-  "eos_token_id": 0,
-  "freeze_mm_mlp_adapter": false,
-  "hidden_act": "silu",
-  "hidden_size": 2560,
-  "image_aspect_ratio": "pad",
-  "image_grid_pinpoints": null,
-  "initializer_range": 0.02,
-  "intermediate_size": 6912,
-  "max_position_embeddings": 4096,
-  "mm_hidden_size": 1024,
-  "mm_projector_type": "mlp2x_gelu",
-  "mm_use_im_patch_token": false,
-  "mm_use_im_start_end": false,
-  "mm_vision_select_feature": "patch",
-  "mm_vision_select_layer": -2,
-  "mm_vision_tower": "openai/clip-vit-large-patch14-336",
-  "model_type": "llava_stablelm_epoch",
-  "norm_eps": 1e-05,
-  "num_attention_heads": 32,
-  "num_heads": 32,
-  "num_hidden_layers": 32,
-  "num_key_value_heads": 32,
-  "rope_pct": 0.25,
-  "rope_theta": 10000,
-  "rotary_scaling_factor": 1.0,
-  "tie_word_embeddings": false,
-  "torch_dtype": "bfloat16",
-  "transformers_version": "4.34.1",
-  "tune_mm_mlp_adapter": false,
-  "use_cache": true,
-  "use_mm_proj": true,
-  "vocab_size": 50304
-}

generation_config.json

@@ -1,6 +0,0 @@
-{
-  "_from_model_config": true,
-  "bos_token_id": 0,
-  "eos_token_id": 0,
-  "transformers_version": "4.34.1"
-}

handler.py

@@ -1,11 +1,16 @@
 from typing import Dict, List, Any
-from transformers import pipeline
-from obsidian import Vision
+import torch
+from transformers import AutoProcessor, LlavaForConditionalGeneration
 
 class EndpointHandler():
     def __init__(self, path="", vision_model="obsidian3b"):
-        self.pipeline = pipeline("text-generation", model=path)
-        self.vision = Vision(vision_model)
+        self.model = LlavaForConditionalGeneration.from_pretrained(
+        "NousResearch/Obsidian-3B-V0.5",
+        torch_dtype=torch.float16,
+        low_cpu_mem_usage=True,
+        ).to("cuda" if torch.is_cuda_available() else "cpu")
+        self.processor = AutoProcessor.from_pretrained("NousResearch/Obsidian-3B-V0.5")
+            
 
     def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
         """
@@ -18,23 +23,28 @@ class EndpointHandler():
         # get inputs
         inputs = data.pop("inputs", "")
         image = data.pop("image", None)
+        
+        inputs = self.processor(inputs, image, return_tensors="pt")
+        res = self.model.generate(**inputs, do_sample=False, max_new_tokens=4096)
+        return self.processor.decode(res[0], skip_special_tokens=True)
 
-        if image:
+        #if image:
             # perform image classification using Obsidian 3b vision
-            image_features = self.vision.encode_image(image)
-            image_embedding = self.vision.extract_feature(image_features)
-            image_caption = self.vision.generate_caption(image_embedding)
+            #image_features = self.vision.encode_image(image)
+            #image_embedding = self.vision.extract_feature(image_features)
+            #image_caption = self.vision.generate_caption(image_embedding)
 
             # combine text and image captions
-            combined_captions = [inputs, image_caption]
+            #combined_captions = [inputs, image_caption]
 
             # run text classification on combined captions
-            prediction = self.pipeline(combined_captions, temperature=0.33, num_beams=5, stop=[], do_sample=True)
+            #prediction = self.pipeline(combined_captions, temperature=0.33, num_beams=5, stop=[], do_sample=True)
 
-            return prediction
+            #return prediction
+        
 
-        else:
+        #else:
             # run text classification on plain text input
-            prediction = self.pipeline(inputs, temperature=0.33, num_beams=5, stop=[], do_sample=True)
+        #    prediction = self.pipeline(inputs, temperature=0.33, num_beams=5, stop=[], do_sample=True)
 
-            return prediction
+        #    return prediction

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requirements.txt

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-obsidian

special_tokens_map.json

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-{
-  "bos_token": {
-    "content": "<|endoftext|>",
-    "lstrip": false,
-    "normalized": false,
-    "rstrip": false,
-    "single_word": false
-  },
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-    "lstrip": false,
-    "normalized": false,
-    "rstrip": false,
-    "single_word": false
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-  "pad_token": "<|endoftext|>",
-  "unk_token": {
-    "content": "<|endoftext|>",
-    "lstrip": false,
-    "normalized": false,
-    "rstrip": false,
-    "single_word": false
-  }
-}

tokenizer_config.json

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-{
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-  "added_tokens_decoder": {
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-      "lstrip": false,
-      "normalized": false,
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