Initial commit

README.md

@@ -1,3 +1,31 @@
----
-license: mit
----
+# LLM Caption
+
+This CLI script creates caption files for all images of a folder. Its saves captions with the same name of the image with txt extension.
+
+## Installation
+
+```bash
+mkdir venv
+python3 -m venv ./venv
+source venv/bin/activate
+pip install -r requirements.txt
+```
+
+## Dependencies
+
+Google SigLIP (3.5GB) will be downloaded automatically from <https://huggingface.co/google/siglip-so400m-patch14-384>
+Uncensored LEXI LAMA Llama-3.1-8b-Instruct (5.5GB) will be downloaded automatically from <https://huggingface.co/John6666/Llama-3.1-8B-Lexi-Uncensored-V2-nf4>
+The Joy Caption model is on the checkpoint folder
+
+## Usage
+
+```bash
+python3 ./caption.py ./test \
+--prompt "Describe this image in detail within 50 words."
+
+python3 ./caption.py ./test 
+```
+
+## Default prompt
+
+In one paragraph, write a very descriptive caption for this image, describe all objects, characters and their actions, describe in detail what is happening and their emotions. Include information about lighting, the style of this image and information about camera angle within 200 words. Don't create any title for the image.

caption.py

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+import os
+import torch
+from PIL import Image
+from transformers import AutoProcessor, AutoModel, AutoTokenizer, PreTrainedTokenizer, PreTrainedTokenizerFast, AutoModelForCausalLM
+from pathlib import Path
+from torch import nn
+import torchvision.transforms.functional as TVF
+
+CLIP_PATH = "google/siglip-so400m-patch14-384"
+CHECKPOINT_PATH = Path("./checkpoint")
+LLMA_CHECKPOINT = "John6666/Llama-3.1-8B-Lexi-Uncensored-V2-nf4"
+WORDS=200
+PROMPT = "In one paragraph, write a very descriptive caption for this image, describe all objects, characters and their actions, describe in detail what is happening and their emotions. Include information about lighting, the style of this image and information about camera angle within {word_count} words. Don't create any title for the image."
+
+HF_TOKEN = os.environ.get("HF_TOKEN", None)
+
+class ImageAdapter(nn.Module):
+	def __init__(self, input_features: int, output_features: int, ln1: bool, pos_emb: bool, num_image_tokens: int, deep_extract: bool):
+		super().__init__()
+		self.deep_extract = deep_extract
+
+		if self.deep_extract:
+			input_features = input_features * 5
+
+		self.linear1 = nn.Linear(input_features, output_features)
+		self.activation = nn.GELU()
+		self.linear2 = nn.Linear(output_features, output_features)
+		self.ln1 = nn.Identity() if not ln1 else nn.LayerNorm(input_features)
+		self.pos_emb = None if not pos_emb else nn.Parameter(torch.zeros(num_image_tokens, input_features))
+
+		# Other tokens (<|image_start|>, <|image_end|>, <|eot_id|>)
+		self.other_tokens = nn.Embedding(3, output_features)
+		self.other_tokens.weight.data.normal_(mean=0.0, std=0.02)	 # Matches HF's implementation of llama3
+
+	def forward(self, vision_outputs: torch.Tensor):
+		if self.deep_extract:
+			x = torch.concat((
+				vision_outputs[-2],
+				vision_outputs[3],
+				vision_outputs[7],
+				vision_outputs[13],
+				vision_outputs[20],
+			), dim=-1)
+			assert len(x.shape) == 3, f"Expected 3, got {len(x.shape)}"	# batch, tokens, features
+			assert x.shape[-1] == vision_outputs[-2].shape[-1] * 5, f"Expected {vision_outputs[-2].shape[-1] * 5}, got {x.shape[-1]}"
+		else:
+			x = vision_outputs[-2]
+
+		x = self.ln1(x)
+
+		if self.pos_emb is not None:
+			assert x.shape[-2:] == self.pos_emb.shape, f"Expected {self.pos_emb.shape}, got {x.shape[-2:]}"
+			x = x + self.pos_emb
+
+		x = self.linear1(x)
+		x = self.activation(x)
+		x = self.linear2(x)
+
+		# <|image_start|>, IMAGE, <|image_end|>
+		other_tokens = self.other_tokens(torch.tensor([0, 1], device=self.other_tokens.weight.device).expand(x.shape[0], -1))
+		assert other_tokens.shape == (x.shape[0], 2, x.shape[2]), f"Expected {(x.shape[0], 2, x.shape[2])}, got {other_tokens.shape}"
+		x = torch.cat((other_tokens[:, 0:1], x, other_tokens[:, 1:2]), dim=1)
+
+		return x
+
+	def get_eot_embedding(self):
+		return self.other_tokens(torch.tensor([2], device=self.other_tokens.weight.device)).squeeze(0)
+			
+
+def proc_img(input_image):
+	# Preprocess image
+	# NOTE: I found the default processor for so400M to have worse results than just using PIL directly
+	#image = clip_processor(images=input_image, return_tensors='pt').pixel_values
+	image = input_image.resize((384, 384), Image.LANCZOS)
+	pixel_values = TVF.pil_to_tensor(image).unsqueeze(0) / 255.0
+	pixel_values = TVF.normalize(pixel_values, [0.5], [0.5])
+	pixel_values = pixel_values.to(device)
+
+	# Embed image
+	# This results in Batch x Image Tokens x Features
+	with torch.amp.autocast_mode.autocast(device, enabled=True):
+		vision_outputs = model(pixel_values=pixel_values, output_hidden_states=True)
+		embedded_images = image_adapter(vision_outputs.hidden_states)
+		embedded_images = embedded_images.to(device)
+	
+	# Build the conversation
+	convo = [
+		{
+			"role": "system",
+			"content": "You are a helpful image captioner.",
+		},
+		{
+			"role": "user",
+			"content": prompt_str,
+		},
+	]
+
+	# Format the conversation
+	convo_string = tokenizer.apply_chat_template(convo, tokenize = False, add_generation_prompt = True)
+	assert isinstance(convo_string, str)
+
+	# Tokenize the conversation
+	# prompt_str is tokenized separately so we can do the calculations below
+	convo_tokens = tokenizer.encode(convo_string, return_tensors="pt", add_special_tokens=False, truncation=False)
+	prompt_tokens = tokenizer.encode(prompt_str, return_tensors="pt", add_special_tokens=False, truncation=False)
+	assert isinstance(convo_tokens, torch.Tensor) and isinstance(prompt_tokens, torch.Tensor)
+	convo_tokens = convo_tokens.squeeze(0)	 # Squeeze just to make the following easier
+	prompt_tokens = prompt_tokens.squeeze(0)
+
+	# Calculate where to inject the image
+	eot_id_indices = (convo_tokens == tokenizer.convert_tokens_to_ids("<|eot_id|>")).nonzero(as_tuple=True)[0].tolist()
+	assert len(eot_id_indices) == 2, f"Expected 2 <|eot_id|> tokens, got {len(eot_id_indices)}"
+
+	preamble_len = eot_id_indices[1] - prompt_tokens.shape[0]	 # Number of tokens before the prompt
+
+	# Embed the tokens
+	convo_embeds = text_model.model.embed_tokens(convo_tokens.unsqueeze(0).to(device))
+
+	# Construct the input
+	input_embeds = torch.cat([
+		convo_embeds[:, :preamble_len],	 # Part before the prompt
+		embedded_images.to(dtype=convo_embeds.dtype),	 # Image
+		convo_embeds[:, preamble_len:],	 # The prompt and anything after it
+	], dim=1).to(device)
+
+	input_ids = torch.cat([
+		convo_tokens[:preamble_len].unsqueeze(0),
+		torch.zeros((1, embedded_images.shape[1]), dtype=torch.long),	 # Dummy tokens for the image (TODO: Should probably use a special token here so as not to confuse any generation algorithms that might be inspecting the input)
+		convo_tokens[preamble_len:].unsqueeze(0),
+	], dim=1).to(device)
+	attention_mask = torch.ones_like(input_ids)
+
+	# Debugging
+	#print(f"Input to model: {repr(tokenizer.decode(input_ids[0]))}")
+
+	#generate_ids = text_model.generate(input_ids, inputs_embeds=inputs_embeds, attention_mask=attention_mask, max_new_tokens=300, do_sample=True, top_k=10, temperature=0.5, suppress_tokens=None)
+	generate_ids = text_model.generate(input_ids, inputs_embeds=input_embeds, attention_mask=attention_mask, max_new_tokens=300, do_sample=True, suppress_tokens=None)	 # Uses the default which is temp=0.6, top_p=0.9
+
+	# Trim off the prompt
+	generate_ids = generate_ids[:, input_ids.shape[1]:]
+	if generate_ids[0][-1] == tokenizer.eos_token_id or generate_ids[0][-1] == tokenizer.convert_tokens_to_ids("<|eot_id|>"):
+		generate_ids = generate_ids[:, :-1]
+
+	caption = tokenizer.batch_decode(generate_ids, skip_special_tokens=False, clean_up_tokenization_spaces=False)[0]
+
+	return caption.strip('\"')
+
+def describe_image(image_path):
+		if not os.path.exists(image_path):
+				print(f"File not found: {image_path}")
+				return
+
+		if not image_path.lower().endswith(".png"):
+				print("File must be PNG.")
+				return
+
+		image = Image.open(image_path).convert("RGB")
+
+		description = proc_img(image)
+
+		# Output filename
+		output_path = os.path.splitext(image_path)[0] + ".txt"
+
+		# Save caption file
+		with open(output_path, "w", encoding="utf-8") as f:
+				f.write(description)
+
+		print(f"Description save in: {output_path}")
+
+if __name__ == "__main__":
+		import argparse
+		
+		parser = argparse.ArgumentParser(description="Caption all PNG image files in a folder")
+		parser.add_argument("folder_path", type=str, help="Folder containing images.")
+		parser.add_argument("--prompt", type=str, help="Prompt to ask a caption.", default=None, required=False)
+		args = parser.parse_args()
+
+		# Process all PNG images in the folder
+		folder_path = Path(args.folder_path)
+		if not folder_path.is_dir():
+				print(f"Error: {folder_path} is not a valid directory.")
+				exit(1)
+
+		png_files = list(folder_path.glob("*.png"))
+		if not png_files:
+				print(f"No PNG files found in the directory: {folder_path}")
+				exit(1)
+
+		# Prompt
+		if args.prompt is None: 
+			prompt_str = PROMPT.format(word_count=WORDS)
+		else:
+			prompt_str = args.prompt
+
+		total = len(png_files)
+		print(f"Found {total} PNG files. Processing...")
+
+		device = "cuda" if torch.cuda.is_available() else "cpu"
+
+		# Load CLIP
+		print("Loading CLIP")
+		processor = AutoProcessor.from_pretrained(CLIP_PATH)
+		model = AutoModel.from_pretrained(CLIP_PATH).to(device)
+		model = model.vision_model
+
+		assert (CHECKPOINT_PATH / "clip_model.pt").exists()
+		print("Loading VLM's custom vision model")
+		checkpoint = torch.load(CHECKPOINT_PATH / "clip_model.pt", map_location='cpu',weights_only=True)
+		checkpoint = {k.replace("_orig_mod.module.", ""): v for k, v in checkpoint.items()}
+		model.load_state_dict(checkpoint)
+		del checkpoint
+
+		# Tokenizer
+		print("Loading tokenizer")
+		tokenizer = AutoTokenizer.from_pretrained(CHECKPOINT_PATH / "text_model", use_fast=True)
+		assert isinstance(tokenizer, PreTrainedTokenizer) or isinstance(tokenizer, PreTrainedTokenizerFast), f"Tokenizer is of type {type(tokenizer)}"
+
+		# LLM
+		print("Loading VLM's custom text model")
+		text_model = AutoModelForCausalLM.from_pretrained(LLMA_CHECKPOINT , device_map=0, trust_remote_code=True,torch_dtype=torch.bfloat16)
+		text_model.eval()
+
+		# Image Adapter
+		print("Loading image adapter")
+		image_adapter = ImageAdapter(model.config.hidden_size, text_model.config.hidden_size, False, False, 38, False)
+		image_adapter.load_state_dict(torch.load(CHECKPOINT_PATH / "image_adapter.pt", map_location="cpu",weights_only=True))
+		image_adapter.eval()
+		image_adapter.to(device)
+
+		curr = 1
+		for image_path in png_files:
+				print(f"Processing image {curr} of {total}: {image_path}")
+				curr += 1
+				describe_image(str(image_path))

checkpoint/clip_model.pt

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+oid sha256:9277e041aab3e7f20a8e6ecf7248b663aac1c281daf4472c12a6e5013cf9f0cc
+size 1713067838

checkpoint/config.yaml

@@ -0,0 +1,39 @@
+wandb_project: joy-caption-1
+device_batch_size: 2
+batch_size: 256
+learning_rate: 0.0002
+warmup_samples: 18000
+max_samples: 600000
+save_every: 50000
+test_every: 50000
+use_amp: true
+grad_scaler: true
+lr_scheduler_type: cosine
+min_lr_ratio: 0.0
+allow_tf32: true
+seed: 69
+num_workers: 8
+optimizer_type: adamw
+adam_beta1: 0.9
+adam_beta2: 0.999
+adam_eps: 1.0e-08
+adam_weight_decay: 0.0
+clip_grad_norm: 1.0
+dataset: fancyfeast/joy-captioning-20240924a
+clip_model: google/siglip-so400m-patch14-384
+text_model: ../lora-train/lora_model_vwbzycxh
+resume: null
+gradient_checkpointing: false
+test_size: 2048
+grad_scaler_init: 65536.0
+max_caption_length: 257
+num_image_tokens: 32
+adapter_type: mlp
+text_model_dtype: bfloat16
+pre_test: false
+train_image_model: true
+image_model_lr: null
+train_lora: true
+lora_r: 64
+lora_alpha: 16
+lora_dropout: 0.1

checkpoint/image_adapter.pt

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+oid sha256:38db2fe263be2d494a50be4a7bbfd7b23b76f9d03e4008a1b7df97d6b27894ef
+size 86067714

checkpoint/text_model/README.md

@@ -0,0 +1,202 @@
+---
+base_model: unsloth/Meta-Llama-3.1-8B-Instruct
+library_name: peft
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+### Framework versions
+
+- PEFT 0.12.0

checkpoint/text_model/adapter_config.json

@@ -0,0 +1,34 @@
+{
+    "alpha_pattern": {},
+    "auto_mapping": null,
+    "base_model_name_or_path": "/media/alan/king/AI/ComfyUI/models/LLM/Llama-3.1-8B-Lexi-Uncensored-V2-nf4",
+    "bias": "none",
+    "fan_in_fan_out": false,
+    "inference_mode": true,
+    "init_lora_weights": true,
+    "layer_replication": null,
+    "layers_pattern": null,
+    "layers_to_transform": null,
+    "loftq_config": {},
+    "lora_alpha": 16,
+    "lora_dropout": 0,
+    "megatron_config": null,
+    "megatron_core": "megatron.core",
+    "modules_to_save": null,
+    "peft_type": "LORA",
+    "r": 64,
+    "rank_pattern": {},
+    "revision": null,
+    "target_modules": [
+        "q_proj",
+        "v_proj",
+        "gate_proj",
+        "down_proj",
+        "o_proj",
+        "k_proj",
+        "up_proj"
+    ],
+    "task_type": "CAUSAL_LM",
+    "use_dora": false,
+    "use_rslora": false
+}

checkpoint/text_model/adapter_model.safetensors

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+size 671149168

checkpoint/text_model/special_tokens_map.json

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+{
+  "bos_token": {
+    "content": "<|begin_of_text|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<|eot_id|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<|finetune_right_pad_id|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

checkpoint/text_model/tokenizer_config.json

@@ -0,0 +1,2064 @@
+{
+  "added_tokens_decoder": {
+    "128000": {
+      "content": "<|begin_of_text|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128001": {
+      "content": "<|end_of_text|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128002": {
+      "content": "<|reserved_special_token_0|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128003": {
+      "content": "<|reserved_special_token_1|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128004": {
+      "content": "<|finetune_right_pad_id|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128005": {
+      "content": "<|reserved_special_token_2|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128006": {
+      "content": "<|start_header_id|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128007": {
+      "content": "<|end_header_id|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128008": {
+      "content": "<|eom_id|>",
+      "lstrip": false,
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+      "special": true
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+      "content": "<|reserved_special_token_227|>",
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+      "content": "<|reserved_special_token_228|>",
+      "lstrip": false,
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+      "special": true
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+    "128238": {
+      "content": "<|reserved_special_token_230|>",
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+      "special": true
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+      "content": "<|reserved_special_token_232|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128241": {
+      "content": "<|reserved_special_token_233|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+      "content": "<|reserved_special_token_234|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
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+      "content": "<|reserved_special_token_235|>",
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+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128244": {
+      "content": "<|reserved_special_token_236|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128245": {
+      "content": "<|reserved_special_token_237|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128246": {
+      "content": "<|reserved_special_token_238|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128247": {
+      "content": "<|reserved_special_token_239|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+    "128248": {
+      "content": "<|reserved_special_token_240|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128249": {
+      "content": "<|reserved_special_token_241|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
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+      "content": "<|reserved_special_token_242|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128251": {
+      "content": "<|reserved_special_token_243|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128252": {
+      "content": "<|reserved_special_token_244|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128253": {
+      "content": "<|reserved_special_token_245|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128254": {
+      "content": "<|reserved_special_token_246|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128255": {
+      "content": "<|reserved_special_token_247|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<|begin_of_text|>",
+  "chat_template": "{{- bos_token }}\n{%- if custom_tools is defined %}\n    {%- set tools = custom_tools %}\n{%- endif %}\n{%- if not tools_in_user_message is defined %}\n    {%- set tools_in_user_message = true %}\n{%- endif %}\n{%- if not date_string is defined %}\n    {%- set date_string = \"26 July 2024\" %}\n{%- endif %}\n{%- if not tools is defined %}\n    {%- set tools = none %}\n{%- endif %}\n\n{#- This block extracts the system message, so we can slot it into the right place. #}\n{%- if messages[0]['role'] == 'system' %}\n    {%- set system_message = messages[0]['content'] %}\n    {%- set messages = messages[1:] %}\n{%- else %}\n    {%- set system_message = \"\" %}\n{%- endif %}\n\n{#- System message + builtin tools #}\n{{- \"<|start_header_id|>system<|end_header_id|>\n\n\" }}\n{%- if builtin_tools is defined or tools is not none %}\n    {{- \"Environment: ipython\n\" }}\n{%- endif %}\n{%- if builtin_tools is defined %}\n    {{- \"Tools: \" + builtin_tools | reject('equalto', 'code_interpreter') | join(\", \") + \"\n\n\"}}\n{%- endif %}\n{{- \"Cutting Knowledge Date: December 2023\n\" }}\n{{- \"Today Date: \" + date_string + \"\n\n\" }}\n{%- if tools is not none and not tools_in_user_message %}\n    {{- \"You have access to the following functions. To call a function, please respond with JSON for a function call.\" }}\n    {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n    {{- \"Do not use variables.\n\n\" }}\n    {%- for t in tools %}\n        {{- t | tojson(indent=4) }}\n        {{- \"\n\n\" }}\n    {%- endfor %}\n{%- endif %}\n{{- system_message }}\n{{- \"<|eot_id|>\" }}\n\n{#- Custom tools are passed in a user message with some extra guidance #}\n{%- if tools_in_user_message and not tools is none %}\n    {#- Extract the first user message so we can plug it in here #}\n    {%- if messages | length != 0 %}\n        {%- set first_user_message = messages[0]['content'] %}\n        {%- set messages = messages[1:] %}\n    {%- else %}\n        {{- raise_exception(\"Cannot put tools in the first user message when there's no first user message!\") }}\n{%- endif %}\n    {{- '<|start_header_id|>user<|end_header_id|>\n\n' -}}\n    {{- \"Given the following functions, please respond with a JSON for a function call \" }}\n    {{- \"with its proper arguments that best answers the given prompt.\n\n\" }}\n    {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n    {{- \"Do not use variables.\n\n\" }}\n    {%- for t in tools %}\n        {{- t | tojson(indent=4) }}\n        {{- \"\n\n\" }}\n    {%- endfor %}\n    {{- first_user_message + \"<|eot_id|>\"}}\n{%- endif %}\n\n{%- for message in messages %}\n    {%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}\n        {{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] + '<|eot_id|>' }}\n    {%- elif 'tool_calls' in message %}\n        {%- if not message.tool_calls|length == 1 %}\n            {{- raise_exception(\"This model only supports single tool-calls at once!\") }}\n        {%- endif %}\n        {%- set tool_call = message.tool_calls[0].function %}\n        {%- if builtin_tools is defined and tool_call.name in builtin_tools %}\n            {{- '<|start_header_id|>assistant<|end_header_id|>\n\n' -}}\n            {{- \"<|python_tag|>\" + tool_call.name + \".call(\" }}\n            {%- for arg_name, arg_val in tool_call.arguments | items %}\n                {{- arg_name + '=\"' + arg_val + '\"' }}\n                {%- if not loop.last %}\n                    {{- \", \" }}\n                {%- endif %}\n                {%- endfor %}\n            {{- \")\" }}\n        {%- else  %}\n            {{- '<|start_header_id|>assistant<|end_header_id|>\n\n' -}}\n            {{- '{\"name\": \"' + tool_call.name + '\", ' }}\n            {{- '\"parameters\": ' }}\n            {{- tool_call.arguments | tojson }}\n            {{- \"}\" }}\n        {%- endif %}\n        {%- if builtin_tools is defined %}\n            {#- This means we're in ipython mode #}\n            {{- \"<|eom_id|>\" }}\n        {%- else %}\n            {{- \"<|eot_id|>\" }}\n        {%- endif %}\n    {%- elif message.role == \"tool\" or message.role == \"ipython\" %}\n        {{- \"<|start_header_id|>ipython<|end_header_id|>\n\n\" }}\n        {%- if message.content is mapping or message.content is iterable %}\n            {{- message.content | tojson }}\n        {%- else %}\n            {{- message.content }}\n        {%- endif %}\n        {{- \"<|eot_id|>\" }}\n    {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n    {{- '<|start_header_id|>assistant<|end_header_id|>\n\n' }}\n{%- endif %}\n",
+  "clean_up_tokenization_spaces": true,
+  "eos_token": "<|eot_id|>",
+  "model_input_names": [
+    "input_ids",
+    "attention_mask"
+  ],
+  "model_max_length": 131072,
+  "pad_token": "<|finetune_right_pad_id|>",
+  "padding_side": "right",
+  "tokenizer_class": "PreTrainedTokenizerFast"
+}

requirements.txt

@@ -0,0 +1,43 @@
+accelerate==1.1.1
+bitsandbytes==0.45.0
+certifi==2024.8.30
+charset-normalizer==3.4.0
+filelock==3.16.1
+fsspec==2024.10.0
+huggingface-hub==0.26.3
+idna==3.10
+Jinja2==3.1.4
+MarkupSafe==3.0.2
+mpmath==1.3.0
+networkx==3.4.2
+numpy==2.1.3
+nvidia-cublas-cu12==12.4.5.8
+nvidia-cuda-cupti-cu12==12.4.127
+nvidia-cuda-nvrtc-cu12==12.4.127
+nvidia-cuda-runtime-cu12==12.4.127
+nvidia-cudnn-cu12==9.1.0.70
+nvidia-cufft-cu12==11.2.1.3
+nvidia-curand-cu12==10.3.5.147
+nvidia-cusolver-cu12==11.6.1.9
+nvidia-cusparse-cu12==12.3.1.170
+nvidia-nccl-cu12==2.21.5
+nvidia-nvjitlink-cu12==12.4.127
+nvidia-nvtx-cu12==12.4.127
+packaging==24.2
+pillow==11.0.0
+protobuf==5.29.1
+psutil==6.1.0
+PyYAML==6.0.2
+regex==2024.11.6
+requests==2.32.3
+safetensors==0.4.5
+sentencepiece==0.2.0
+sympy==1.13.1
+tokenizers==0.21.0
+torch==2.5.1
+torchvision==0.20.1
+tqdm==4.67.1
+transformers==4.47.0
+triton==3.1.0
+typing_extensions==4.12.2
+urllib3==2.2.3

test/c.txt

@@ -0,0 +1 @@
+In this stylized illustration, a profile view of a bearded man is presented against a plain white background, rendered in a minimalist, line-art style reminiscent of 19th-century engravings. The subject's face is turned to the right, with a slight smirk suggesting a hint of confidence and self-assurance. His blonde hair is cut in a buzzed, asymmetrical style, with a prominent fade on the right side, accentuating his angular features. A thick, golden beard, flecked with darker strands, frames his jawline, its wispy tendrils curling around his chin. A black cloak or hoodie wraps around his shoulders, its edges subtly textured to suggest a rough, woolen fabric. At the bottom of the image, in bold, black letters, the word "Yes" is emblazoned, serving as a stark, declarative statement. The entire scene is bathed in a soft, diffused light, as if illuminated by a warm, overhead glow.

test/d.txt

@@ -0,0 +1 @@
+This image is a close-up of a man's face, taken against a black background, with a white outline. The man, who appears to be of East Asian descent, has a round face with a prominent nose and a small, pointed chin. His eyes are closed and his face is contorted in a humorous expression, with his mouth open wide in a laugh. His teeth are visible, and they are straight and white. His eyebrows are thick and bushy, and they are furrowed in a way that suggests he is trying to hold back his laughter. The man's hair is short and dark, and it is styled in a way that suggests he has recently shaved his head. He is wearing a light-colored shirt, which is not visible in its entirety due to the tight framing of the image. The lighting in the image is soft and even, suggesting that it was taken in a studio setting. The style of the image is playful and humorous, and it appears to be a cartoon or a meme. The camera angle is very close, with the lens almost touching the man's face.