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@@ -33,3 +33,4 @@ 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
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,107 @@
+---
+library_name: transformers
+license: apache-2.0
+language:
+- en
+base_model:
+- mistralai/Pixtral-12B-2409
+pipeline_tag: image-to-text
+---
+
+# Pixtral-12B-Captioner-Relaxed
+
+## Introduction
+
+*Original Pixtral-12B-Captioner-Relaxed from https://huggingface.co/Ertugrul/Pixtral-12B-Captioner-Relaxed/tree/main with tokenizer fix*
+
+Pixtral-12B-Captioner-Relaxed is an instruction-tuned version of [Pixtral-12B-2409](https://huggingface.co/mistralai/Pixtral-12B-2409), an advanced multimodal large language model. This fine-tuned version is based on a hand-curated dataset for text-to-image models, providing significantly more detailed descriptions of given images.
+
+### Key Features:
+
+* **Enhanced Detail:** Generates more comprehensive and nuanced image descriptions.
+* **Relaxed Constraints:** Offers less restrictive image descriptions compared to the base model.
+* **Natural Language Output:** Describes different subjects in the image while specifying their locations using natural language.
+* **Optimized for Image Generation:** Produces captions in formats compatible with state-of-the-art text-to-image generation models.
+
+**Note:** This fine-tuned model is optimized for creating text-to-image datasets. As a result, performance on other complex tasks may be lower compared to the original model.
+
+## Requirements
+
+The 12B model needs 24GB of VRAM at half precision. Model can be loaded at 8 bit or 4 bit quantization but expect degraded performance.
+
+## Quickstart
+```python
+from PIL import Image
+from transformers import LlavaForConditionalGeneration, AutoProcessor
+from transformers import BitsAndBytesConfig
+import torch
+import matplotlib.pyplot as plt
+
+
+
+# example quantization config, add it to model load parameters to use 4bit quantization
+quantization_config = BitsAndBytesConfig(
+    # load_in_8bit=True,
+    load_in_4bit=True,
+    bnb_4bit_compute_dtype=torch.bfloat16,
+    bnb_4bit_quant_type="nf4"
+    )
+
+
+
+model_id = "Ertugrul/Pixtral-12B-Captioner-Relaxed"
+model = LlavaForConditionalGeneration.from_pretrained(model_id, device_map="auto", torch_dtype=torch.bfloat16)
+processor = AutoProcessor.from_pretrained(model_id)
+
+# for quantization just use this instead of previous load
+# model = LlavaForConditionalGeneration.from_pretrained(model_id, device_map="auto", torch_dtype=torch.bfloat16, quantization_config=quantization_config)
+
+conversation = [
+    {
+        "role": "user",
+        "content": [
+            
+            {"type": "text", "text": "Describe the image.\n"},
+            {
+                "type": "image",
+            }
+        ],
+    }
+]
+
+PROMPT = processor.apply_chat_template(conversation, add_generation_prompt=True)
+
+image = Image.open(r"PATH_TO_YOUR_IMAGE")
+
+def resize_image(image, target_size=768):
+    """Resize the image to have the target size on the shortest side."""
+    width, height = image.size
+    if width < height:
+        new_width = target_size
+        new_height = int(height * (new_width / width))
+    else:
+        new_height = target_size
+        new_width = int(width * (new_height / height))
+    return image.resize((new_width, new_height), Image.LANCZOS)
+
+
+# you can try different resolutions or disable it completely
+image = resize_image(image, 768)
+
+
+inputs = processor(text=PROMPT, images=image, return_tensors="pt").to("cuda")
+
+
+with torch.no_grad():
+    with torch.autocast(device_type="cuda", dtype=torch.bfloat16):
+        generate_ids = model.generate(**inputs, max_new_tokens=384, do_sample=True, temperature=0.3, use_cache=True, top_k=20)
+output_text = processor.batch_decode(generate_ids[:, inputs.input_ids.shape[1]:], skip_special_tokens=True, clean_up_tokenization_spaces=True)[0]
+
+print(output_text)
+```
+
+## Acknowledgements
+
+For more detailed options, refer to the [Pixtral-12B-2409](https://huggingface.co/mistralai/Pixtral-12B-2409) or [mistral-community/pixtral-12b](https://huggingface.co/mistral-community/pixtral-12b) documentation.
+
+You can also try the [Qwen2-VL-7B-Captioner-Relaxed](https://huggingface.co/Ertugrul/Qwen2-VL-7B-Captioner-Relaxed), for an alternative smaller model. It's trianed in a similar manner.

chat_template.json

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+{
+  "chat_template": "{%- if messages[0][\"role\"] == \"system\" %}\n    {%- set system_message = messages[0][\"content\"] %}\n    {%- set loop_messages = messages[1:] %}\n{%- else %}\n    {%- set loop_messages = messages %}\n{%- endif %}\n\n{{- bos_token }}\n{%- for message in loop_messages %}\n    {%- if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}\n        {{- raise_exception('After the optional system message, conversation roles must alternate user/assistant/user/assistant/...') }}\n    {%- endif %}\n    {%- if message[\"role\"] == \"user\" %}\n        {%- if loop.last and system_message is defined %}\n            {{- \"[INST]\" + system_message + \"\n\n\" }}\n        {%- else %}\n            {{- \"[INST]\" }}\n        {%- endif %}\n        {%- if message[\"content\"] is not string %}\n            {%- for chunk in message[\"content\"] %}\n                {%- if chunk[\"type\"] == \"text\" %}\n                    {{- chunk[\"content\"] }}\n                {%- elif chunk[\"type\"] == \"image\" %}\n                    {{- \"[IMG]\" }}\n                {%- else %}\n                    {{- raise_exception(\"Unrecognized content type!\") }}\n                {%- endif %}\n            {%- endfor %}\n        {%- else %}\n            {{- message[\"content\"] }}\n        {%- endif %}\n        {{- \"[/INST]\" }}\n    {%- elif message[\"role\"] == \"assistant\" %}\n        {{- message[\"content\"] + eos_token}}\n    {%- else %}\n        {{- raise_exception(\"Only user and assistant roles are supported, with the exception of an initial optional system message!\") }}\n    {%- endif %}\n{%- endfor %}"
+}

config.json

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+{
+  "_name_or_path": "mistral-community/pixtral-12b",
+  "architectures": [
+    "LlavaForConditionalGeneration"
+  ],
+  "ignore_index": -100,
+  "image_seq_length": 1,
+  "image_token_index": 10,
+  "model_type": "llava",
+  "projector_hidden_act": "gelu",
+  "text_config": {
+    "_name_or_path": "",
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+    "head_dim": 128,
+    "hidden_act": "silu",
+    "hidden_size": 5120,
+    "id2label": {
+      "0": "LABEL_0",
+      "1": "LABEL_1"
+    },
+    "initializer_range": 0.02,
+    "intermediate_size": 14336,
+    "is_composition": true,
+    "is_decoder": false,
+    "is_encoder_decoder": false,
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+      "LABEL_0": 0,
+      "LABEL_1": 1
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+    "length_penalty": 1.0,
+    "max_length": 20,
+    "max_position_embeddings": 1024000,
+    "min_length": 0,
+    "model_type": "mistral",
+    "no_repeat_ngram_size": 0,
+    "num_attention_heads": 32,
+    "num_beam_groups": 1,
+    "num_beams": 1,
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+    "num_key_value_heads": 8,
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+    "pruned_heads": {},
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+    "return_dict": true,
+    "return_dict_in_generate": false,
+    "rms_norm_eps": 1e-05,
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+    "use_bfloat16": false,
+    "use_cache": true,
+    "vocab_size": 131072
+  },
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.45.1",
+  "vision_config": {
+    "_name_or_path": "",
+    "add_cross_attention": false,
+    "architectures": null,
+    "attention_dropout": 0.0,
+    "bad_words_ids": null,
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+    "hidden_act": "gelu",
+    "hidden_size": 1024,
+    "id2label": {
+      "0": "LABEL_0",
+      "1": "LABEL_1"
+    },
+    "image_size": 1024,
+    "intermediate_size": 4096,
+    "is_composition": true,
+    "is_decoder": false,
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+    "label2id": {
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+    "model_type": "pixtral",
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+    "typical_p": 1.0,
+    "use_bfloat16": false
+  },
+  "vision_feature_layer": -1,
+  "vision_feature_select_strategy": "full"
+}

generation_config.json

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+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "transformers_version": "4.45.1"
+}

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preprocessor_config.json

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processor_config.json

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special_tokens_map.json

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tokenizer.json

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