support mllm hf inference

data/README.md

@@ -18,7 +18,8 @@ If you are using a custom dataset, please provide your dataset definition in the
     "history": "the column name in the dataset containing the histories. (default: None)",
     "messages": "the column name in the dataset containing the messages. (default: conversations)",
     "system": "the column name in the dataset containing the system prompts. (default: None)",
-    "tools": "the column name in the dataset containing the tool description. (default: None)"
+    "tools": "the column name in the dataset containing the tool description. (default: None)",
+    "images": "the column name in the dataset containing the image inputs. (default: None)"
   },
   "tags (optional, used for the sharegpt format)": {
     "role_tag": "the key in the message represents the identity. (default: from)",

data/README_zh.md

@@ -18,7 +18,8 @@
     "history": "数据集代表历史对话的表头名称（默认：None）",
     "messages": "数据集代表消息列表的表头名称（默认：conversations）",
     "system": "数据集代表系统提示的表头名称（默认：None）",
-    "tools": "数据集代表工具描述的表头名称（默认：None）"
+    "tools": "数据集代表工具描述的表头名称（默认：None）",
+    "images": "数据集代表图像输入的表头名称（默认：None）"
   },
   "tags（可选，用于 sharegpt 格式）": {
     "role_tag": "消息中代表发送者身份的键名（默认：from）",

examples/README.md

@@ -9,6 +9,7 @@ examples/
 │   ├── ppo.sh: Do PPO training using LoRA
 │   ├── dpo.sh: Do DPO training using LoRA
 │   ├── orpo.sh: Do ORPO training using LoRA
+│   ├── sft_mllm.sh: Do supervised fine-tuning on multimodal data using LoRA
 │   ├── prepare.sh: Save tokenized dataset
 │   └── predict.sh: Do batch predict and compute BLEU and ROUGE scores after LoRA tuning
 ├── qlora_single_gpu/

examples/README_zh.md

@@ -9,6 +9,7 @@ examples/
 │   ├── ppo.sh: 基于 LoRA 进行 PPO 训练
 │   ├── dpo.sh: 基于 LoRA 进行 DPO 训练
 │   ├── orpo.sh: 基于 LoRA 进行 ORPO 训练
+│   ├── sft_mllm.sh: 基于 LoRA 进行多模态指令监督微调
 │   ├── prepare.sh: 保存预处理后的数据集
 │   └── predict.sh: 基于 LoRA 进行批量预测并计算 BLEU 和 ROUGE 分数
 ├── qlora_single_gpu/

examples/lora_single_gpu/sft_mllm.sh

@@ -1,32 +1,33 @@
 #!/bin/bash
 
-CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
+CUDA_VISIBLE_DEVICES=0 python ../../src/train_bash.py \
-    --stage sft_mm \
+    --stage sft \
     --do_train \
     --model_name_or_path llava-hf/llava-1.5-7b-hf \
-    --dataset mllm_instruct_example \
+    --visual_inputs \
-    --dataset_dir data \
+    --dataset mllm_demo \
-    --template default \
+    --dataset_dir ../../data \
+    --template vicuna \
     --finetuning_type lora \
-    --lora_target all \
+    --lora_target q_proj,v_proj \
-    --output_dir saves/llava-1.5-7b/lora/sft \
+    --output_dir ../../saves/LLaMA2-7B/lora/sft_mllm \
     --overwrite_cache \
     --overwrite_output_dir \
     --cutoff_len 1024 \
     --preprocessing_num_workers 16 \
-    --per_device_train_batch_size 3 \
+    --per_device_train_batch_size 1 \
     --per_device_eval_batch_size 1 \
-    --gradient_accumulation_steps 1 \
+    --gradient_accumulation_steps 8 \
     --lr_scheduler_type cosine \
-    --logging_steps 1 \
+    --logging_steps 10 \
     --warmup_steps 20 \
     --save_steps 100 \
     --eval_steps 100 \
     --evaluation_strategy steps \
     --load_best_model_at_end \
     --learning_rate 5e-5 \
-    --num_train_epochs 100 \
+    --num_train_epochs 100.0 \
     --max_samples 3000 \
     --val_size 0.1 \
     --plot_loss \
-    --bf16
+    --fp16

src/llmtuner/chat/base_engine.py

@@ -4,6 +4,7 @@ from typing import TYPE_CHECKING, Any, AsyncGenerator, Dict, List, Literal, Opti
 
 
 if TYPE_CHECKING:
+    from numpy.typing import NDArray
     from transformers import PreTrainedModel, PreTrainedTokenizer
     from vllm import AsyncLLMEngine
 
@@ -46,6 +47,7 @@ class BaseEngine(ABC):
         messages: Sequence[Dict[str, str]],
         system: Optional[str] = None,
         tools: Optional[str] = None,
+        image: Optional["NDArray"] = None,
         **input_kwargs,
     ) -> List["Response"]: ...
 
@@ -55,6 +57,7 @@ class BaseEngine(ABC):
         messages: Sequence[Dict[str, str]],
         system: Optional[str] = None,
         tools: Optional[str] = None,
+        image: Optional["NDArray"] = None,
         **input_kwargs,
     ) -> AsyncGenerator[str, None]: ...
 

src/llmtuner/chat/chat_model.py

@@ -8,6 +8,8 @@ from .vllm_engine import VllmEngine
 
 
 if TYPE_CHECKING:
+    from numpy.typing import NDArray
+
     from .base_engine import BaseEngine, Response
 
 
@@ -36,9 +38,10 @@ class ChatModel:
         messages: Sequence[Dict[str, str]],
         system: Optional[str] = None,
         tools: Optional[str] = None,
+        image: Optional["NDArray"] = None,
         **input_kwargs,
     ) -> List["Response"]:
-        task = asyncio.run_coroutine_threadsafe(self.achat(messages, system, tools, **input_kwargs), self._loop)
+        task = asyncio.run_coroutine_threadsafe(self.achat(messages, system, tools, image, **input_kwargs), self._loop)
         return task.result()
 
     async def achat(
@@ -46,18 +49,20 @@ class ChatModel:
         messages: Sequence[Dict[str, str]],
         system: Optional[str] = None,
         tools: Optional[str] = None,
+        image: Optional["NDArray"] = None,
         **input_kwargs,
     ) -> List["Response"]:
-        return await self.engine.chat(messages, system, tools, **input_kwargs)
+        return await self.engine.chat(messages, system, tools, image, **input_kwargs)
 
     def stream_chat(
         self,
         messages: Sequence[Dict[str, str]],
         system: Optional[str] = None,
         tools: Optional[str] = None,
+        image: Optional["NDArray"] = None,
         **input_kwargs,
     ) -> Generator[str, None, None]:
-        generator = self.astream_chat(messages, system, tools, **input_kwargs)
+        generator = self.astream_chat(messages, system, tools, image, **input_kwargs)
         while True:
             try:
                 task = asyncio.run_coroutine_threadsafe(generator.__anext__(), self._loop)
@@ -70,9 +75,10 @@ class ChatModel:
         messages: Sequence[Dict[str, str]],
         system: Optional[str] = None,
         tools: Optional[str] = None,
+        image: Optional["NDArray"] = None,
         **input_kwargs,
     ) -> AsyncGenerator[str, None]:
-        async for new_token in self.engine.stream_chat(messages, system, tools, **input_kwargs):
+        async for new_token in self.engine.stream_chat(messages, system, tools, image, **input_kwargs):
             yield new_token
 
     def get_scores(

src/llmtuner/chat/hf_engine.py

@@ -14,7 +14,9 @@ from .base_engine import BaseEngine, Response
 
 
 if TYPE_CHECKING:
-    from transformers import PreTrainedModel, PreTrainedTokenizer
+    from numpy.typing import NDArray
+    from transformers import PreTrainedModel, PreTrainedTokenizer, ProcessorMixin
+    from transformers.image_processing_utils import BaseImageProcessor
     from trl import PreTrainedModelWrapper
 
     from ..data import Template
@@ -30,7 +32,9 @@ class HuggingfaceEngine(BaseEngine):
         generating_args: "GeneratingArguments",
     ) -> None:
         self.can_generate = finetuning_args.stage == "sft"
-        self.tokenizer = load_tokenizer(model_args)
+        tokenizer_module = load_tokenizer(model_args)
+        self.tokenizer = tokenizer_module["tokenizer"]
+        self.processor = tokenizer_module["processor"]
         self.tokenizer.padding_side = "left" if self.can_generate else "right"
         self.template = get_template_and_fix_tokenizer(self.tokenizer, data_args.template)
         self.model = load_model(
@@ -42,13 +46,18 @@ class HuggingfaceEngine(BaseEngine):
     def _process_args(
         model: "PreTrainedModel",
         tokenizer: "PreTrainedTokenizer",
+        processor: Optional["ProcessorMixin"],
         template: "Template",
         generating_args: Dict[str, Any],
         messages: Sequence[Dict[str, str]],
         system: Optional[str] = None,
         tools: Optional[str] = None,
+        image: Optional["NDArray"] = None,
         input_kwargs: Optional[Dict[str, Any]] = {},
     ) -> Tuple[Dict[str, Any], int]:
+        if processor is not None and image is not None and "<image>" not in messages[0]["content"]:
+            messages[0]["content"] = messages[0]["content"] + "<image>"
+
         paired_messages = messages + [{"role": "assistant", "content": ""}]
         prompt_ids, _ = template.encode_oneturn(
             tokenizer=tokenizer, messages=paired_messages, system=system, tools=tools
@@ -95,6 +104,11 @@ class HuggingfaceEngine(BaseEngine):
             logits_processor=get_logits_processor(),
         )
 
+        if processor is not None and image is not None:
+            image_processor: "BaseImageProcessor" = getattr(processor, "image_processor")
+            pixel_values: "torch.Tensor" = image_processor(image, return_tensors="pt")["pixel_values"]
+            gen_kwargs["pixel_values"] = pixel_values.to(model.device)
+
         return gen_kwargs, prompt_length
 
     @staticmethod
@@ -102,15 +116,17 @@ class HuggingfaceEngine(BaseEngine):
     def _chat(
         model: "PreTrainedModel",
         tokenizer: "PreTrainedTokenizer",
+        processor: Optional["ProcessorMixin"],
         template: "Template",
         generating_args: Dict[str, Any],
         messages: Sequence[Dict[str, str]],
         system: Optional[str] = None,
         tools: Optional[str] = None,
+        image: Optional["NDArray"] = None,
         input_kwargs: Optional[Dict[str, Any]] = {},
     ) -> List["Response"]:
         gen_kwargs, prompt_length = HuggingfaceEngine._process_args(
-            model, tokenizer, template, generating_args, messages, system, tools, input_kwargs
+            model, tokenizer, processor, template, generating_args, messages, system, tools, image, input_kwargs
         )
         generate_output = model.generate(**gen_kwargs)
         response_ids = generate_output[:, prompt_length:]
@@ -135,15 +151,17 @@ class HuggingfaceEngine(BaseEngine):
     def _stream_chat(
         model: "PreTrainedModel",
         tokenizer: "PreTrainedTokenizer",
+        processor: Optional["ProcessorMixin"],
         template: "Template",
         generating_args: Dict[str, Any],
         messages: Sequence[Dict[str, str]],
         system: Optional[str] = None,
         tools: Optional[str] = None,
+        image: Optional["NDArray"] = None,
         input_kwargs: Optional[Dict[str, Any]] = {},
     ) -> Callable[[], str]:
         gen_kwargs, _ = HuggingfaceEngine._process_args(
-            model, tokenizer, template, generating_args, messages, system, tools, input_kwargs
+            model, tokenizer, processor, template, generating_args, messages, system, tools, image, input_kwargs
         )
         streamer = TextIteratorStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
         gen_kwargs["streamer"] = streamer
@@ -199,6 +217,7 @@ class HuggingfaceEngine(BaseEngine):
         messages: Sequence[Dict[str, str]],
         system: Optional[str] = None,
         tools: Optional[str] = None,
+        image: Optional["NDArray"] = None,
         **input_kwargs,
     ) -> List["Response"]:
         if not self.can_generate:
@@ -208,11 +227,13 @@ class HuggingfaceEngine(BaseEngine):
         input_args = (
             self.model,
             self.tokenizer,
+            self.processor,
             self.template,
             self.generating_args,
             messages,
             system,
             tools,
+            image,
             input_kwargs,
         )
         async with self._semaphore:
@@ -224,6 +245,7 @@ class HuggingfaceEngine(BaseEngine):
         messages: Sequence[Dict[str, str]],
         system: Optional[str] = None,
         tools: Optional[str] = None,
+        image: Optional["NDArray"] = None,
         **input_kwargs,
     ) -> AsyncGenerator[str, None]:
         if not self.can_generate:
@@ -233,11 +255,13 @@ class HuggingfaceEngine(BaseEngine):
         input_args = (
             self.model,
             self.tokenizer,
+            self.processor,
             self.template,
             self.generating_args,
             messages,
             system,
             tools,
+            image,
             input_kwargs,
         )
         async with self._semaphore:

src/llmtuner/chat/vllm_engine.py

@@ -12,7 +12,10 @@ if is_vllm_available():
     from vllm import AsyncEngineArgs, AsyncLLMEngine, RequestOutput, SamplingParams
     from vllm.lora.request import LoRARequest
 
+
 if TYPE_CHECKING:
+    from numpy.typing import NDArray
+
     from ..hparams import DataArguments, FinetuningArguments, GeneratingArguments, ModelArguments
 
 
@@ -29,7 +32,9 @@ class VllmEngine(BaseEngine):
         infer_dtype = str(infer_dtype).split(".")[-1]
 
         self.can_generate = finetuning_args.stage == "sft"
-        self.tokenizer = load_tokenizer(model_args)
+        tokenizer_module = load_tokenizer(model_args)
+        self.tokenizer = tokenizer_module["tokenizer"]
+        self.processor = tokenizer_module["processor"]
         self.tokenizer.padding_side = "left"
         self.template = get_template_and_fix_tokenizer(self.tokenizer, data_args.template)
         self.generating_args = generating_args.to_dict()
@@ -58,6 +63,7 @@ class VllmEngine(BaseEngine):
         messages: Sequence[Dict[str, str]],
         system: Optional[str] = None,
         tools: Optional[str] = None,
+        image: Optional["NDArray"] = None,
         **input_kwargs,
     ) -> AsyncIterator["RequestOutput"]:
         request_id = "chatcmpl-{}".format(uuid.uuid4().hex)
@@ -121,10 +127,11 @@ class VllmEngine(BaseEngine):
         messages: Sequence[Dict[str, str]],
         system: Optional[str] = None,
         tools: Optional[str] = None,
+        image: Optional["NDArray"] = None,
         **input_kwargs,
     ) -> List["Response"]:
         final_output = None
-        generator = await self._generate(messages, system, tools, **input_kwargs)
+        generator = await self._generate(messages, system, tools, image, **input_kwargs)
         async for request_output in generator:
             final_output = request_output
 
@@ -146,10 +153,11 @@ class VllmEngine(BaseEngine):
         messages: Sequence[Dict[str, str]],
         system: Optional[str] = None,
         tools: Optional[str] = None,
+        image: Optional["NDArray"] = None,
         **input_kwargs,
     ) -> AsyncGenerator[str, None]:
         generated_text = ""
-        generator = await self._generate(messages, system, tools, **input_kwargs)
+        generator = await self._generate(messages, system, tools, image, **input_kwargs)
         async for result in generator:
             delta_text = result.outputs[0].text[len(generated_text) :]
             generated_text = result.outputs[0].text

src/llmtuner/data/preprocess.py

@@ -8,7 +8,7 @@ from .utils import Role
 
 
 if TYPE_CHECKING:
-    from PIL import Image
+    from PIL.Image import Image
     from transformers import ProcessorMixin, Seq2SeqTrainingArguments
     from transformers.image_processing_utils import BaseImageProcessor
     from transformers.tokenization_utils import PreTrainedTokenizer
@@ -271,7 +271,11 @@ def get_preprocess_and_print_func(
     processor: Optional["ProcessorMixin"],
 ) -> Tuple[Callable, Callable]:
     if stage == "pt":
-        preprocess_func = partial(preprocess_pretrain_dataset, tokenizer=tokenizer, data_args=data_args)
+        preprocess_func = partial(
+            preprocess_pretrain_dataset,
+            tokenizer=tokenizer,
+            data_args=data_args,
+        )
         print_function = partial(print_unsupervised_dataset_example, tokenizer=tokenizer)
     elif stage == "sft" and not training_args.predict_with_generate:
         if data_args.packing:

src/llmtuner/eval/evaluator.py

@@ -21,7 +21,7 @@ from .template import get_eval_template
 class Evaluator:
     def __init__(self, args: Optional[Dict[str, Any]] = None) -> None:
         self.model_args, self.data_args, self.eval_args, finetuning_args = get_eval_args(args)
-        self.tokenizer = load_tokenizer(self.model_args)
+        self.tokenizer = load_tokenizer(self.model_args)["tokenizer"]
         self.tokenizer.padding_side = "right"  # avoid overflow issue in batched inference for llama2
         self.template = get_template_and_fix_tokenizer(self.tokenizer, self.data_args.template)
         self.model = load_model(self.tokenizer, self.model_args, finetuning_args)

src/llmtuner/hparams/parser.py

@@ -196,6 +196,9 @@ def get_train_args(args: Optional[Dict[str, Any]] = None) -> _TRAIN_CLS:
     if model_args.infer_backend == "vllm":
         raise ValueError("vLLM backend is only available for API, CLI and Web.")
 
+    if model_args.visual_inputs and data_args.packing:
+        raise ValueError("Cannot use packing in MLLM fine-tuning.")
+
     _verify_model_args(model_args, finetuning_args)
     _check_extra_dependencies(model_args, finetuning_args, training_args)
 

src/llmtuner/train/dpo/workflow.py

@@ -24,8 +24,9 @@ def run_dpo(
     finetuning_args: "FinetuningArguments",
     callbacks: Optional[List["TrainerCallback"]] = None,
 ):
-    tokenizer = load_tokenizer(model_args)
+    tokenizer_module = load_tokenizer(model_args)
-    dataset = get_dataset(tokenizer, model_args, data_args, training_args, stage="rm")
+    tokenizer = tokenizer_module["tokenizer"]
+    dataset = get_dataset(model_args, data_args, training_args, stage="rm", **tokenizer_module)
     model = load_model(tokenizer, model_args, finetuning_args, training_args.do_train)
 
     data_collator = PairwiseDataCollatorWithPadding(

src/llmtuner/train/orpo/workflow.py

@@ -24,8 +24,9 @@ def run_orpo(
     finetuning_args: "FinetuningArguments",
     callbacks: Optional[List["TrainerCallback"]] = None,
 ):
-    tokenizer = load_tokenizer(model_args)
+    tokenizer_module = load_tokenizer(model_args)
-    dataset = get_dataset(tokenizer, model_args, data_args, training_args, stage="rm")
+    tokenizer = tokenizer_module["tokenizer"]
+    dataset = get_dataset(model_args, data_args, training_args, stage="rm", **tokenizer_module)
     model = load_model(tokenizer, model_args, finetuning_args, training_args.do_train)
 
     data_collator = PairwiseDataCollatorWithPadding(

src/llmtuner/train/ppo/workflow.py

@@ -27,8 +27,9 @@ def run_ppo(
     generating_args: "GeneratingArguments",
     callbacks: Optional[List["TrainerCallback"]] = None,
 ):
-    tokenizer = load_tokenizer(model_args)
+    tokenizer_module = load_tokenizer(model_args)
-    dataset = get_dataset(tokenizer, model_args, data_args, training_args, stage="ppo")
+    tokenizer = tokenizer_module["tokenizer"]
+    dataset = get_dataset(model_args, data_args, training_args, stage="ppo", **tokenizer_module)
     model = load_model(tokenizer, model_args, finetuning_args, training_args.do_train, add_valuehead=True)
 
     tokenizer.padding_side = "left"  # use left-padding in generation while using right-padding in training

src/llmtuner/train/pt/workflow.py

@@ -25,8 +25,9 @@ def run_pt(
     finetuning_args: "FinetuningArguments",
     callbacks: Optional[List["TrainerCallback"]] = None,
 ):
-    tokenizer = load_tokenizer(model_args)
+    tokenizer_module = load_tokenizer(model_args)
-    dataset = get_dataset(tokenizer, model_args, data_args, training_args, stage="pt")
+    tokenizer = tokenizer_module["tokenizer"]
+    dataset = get_dataset(model_args, data_args, training_args, stage="pt", **tokenizer_module)
     model = load_model(tokenizer, model_args, finetuning_args, training_args.do_train)
     data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False)
 

src/llmtuner/train/rm/workflow.py

@@ -25,8 +25,9 @@ def run_rm(
     finetuning_args: "FinetuningArguments",
     callbacks: Optional[List["TrainerCallback"]] = None,
 ):
-    tokenizer = load_tokenizer(model_args)
+    tokenizer_module = load_tokenizer(model_args)
-    dataset = get_dataset(tokenizer, model_args, data_args, training_args, stage="rm")
+    tokenizer = tokenizer_module["tokenizer"]
+    dataset = get_dataset(model_args, data_args, training_args, stage="rm", **tokenizer_module)
     model = load_model(tokenizer, model_args, finetuning_args, training_args.do_train, add_valuehead=True)
     data_collator = PairwiseDataCollatorWithPadding(tokenizer, pad_to_multiple_of=8)
 

src/llmtuner/train/sft/workflow.py

@@ -29,9 +29,9 @@ def run_sft(
     callbacks: Optional[List["TrainerCallback"]] = None,
 ):
     tokenizer_module = load_tokenizer(model_args)
-    dataset = get_dataset(model_args, data_args, training_args, stage="sft", **tokenizer_module)
     tokenizer = tokenizer_module["tokenizer"]
-    model = load_model(tokenizer, model_args, finetuning_args, is_trainable=training_args.do_train)
+    dataset = get_dataset(model_args, data_args, training_args, stage="sft", **tokenizer_module)
+    model = load_model(tokenizer, model_args, finetuning_args, training_args.do_train)
 
     if training_args.predict_with_generate:
         tokenizer.padding_side = "left"  # use left-padding in generation

src/llmtuner/train/tuner.py

@@ -52,7 +52,7 @@ def export_model(args: Optional[Dict[str, Any]] = None):
     if model_args.adapter_name_or_path is not None and model_args.export_quantization_bit is not None:
         raise ValueError("Please merge adapters before quantizing the model.")
 
-    tokenizer = load_tokenizer(model_args)
+    tokenizer = load_tokenizer(model_args)["tokenizer"]
     get_template_and_fix_tokenizer(tokenizer, data_args.template)
     model = load_model(tokenizer, model_args, finetuning_args)  # must after fixing tokenizer to resize vocab
 

src/llmtuner/train/utils.py

@@ -91,7 +91,7 @@ def create_ref_model(
         )
         ref_model_args = ModelArguments(**ref_model_args_dict)
         ref_finetuning_args = FinetuningArguments(finetuning_type="lora")
-        tokenizer = load_tokenizer(ref_model_args)
+        tokenizer = load_tokenizer(ref_model_args)["tokenizer"]
         ref_model = load_model(
             tokenizer, ref_model_args, ref_finetuning_args, is_trainable=False, add_valuehead=add_valuehead
         )
@@ -100,7 +100,7 @@ def create_ref_model(
         if finetuning_args.finetuning_type == "lora":
             ref_model = None
         else:
-            tokenizer = load_tokenizer(model_args)
+            tokenizer = load_tokenizer(model_args)["tokenizer"]
             ref_model = load_model(
                 tokenizer, model_args, finetuning_args, is_trainable=False, add_valuehead=add_valuehead
             )
@@ -147,7 +147,7 @@ def create_reward_model(
         )
         reward_model_args = ModelArguments(**reward_model_args_dict)
         reward_finetuning_args = FinetuningArguments(finetuning_type="lora")
-        tokenizer = load_tokenizer(reward_model_args)
+        tokenizer = load_tokenizer(reward_model_args)["tokenizer"]
         reward_model = load_model(
             tokenizer, reward_model_args, reward_finetuning_args, is_trainable=False, add_valuehead=True
         )

src/llmtuner/webui/chatter.py

@@ -2,6 +2,8 @@ import json
 import os
 from typing import TYPE_CHECKING, Dict, Generator, List, Optional, Sequence, Tuple
 
+from numpy.typing import NDArray
+
 from ..chat import ChatModel
 from ..data import Role
 from ..extras.misc import torch_gc
@@ -112,6 +114,7 @@ class WebChatModel(ChatModel):
         messages: Sequence[Dict[str, str]],
         system: str,
         tools: str,
+        image: Optional[NDArray],
         max_new_tokens: int,
         top_p: float,
         temperature: float,
@@ -119,7 +122,7 @@ class WebChatModel(ChatModel):
         chatbot[-1][1] = ""
         response = ""
         for new_text in self.stream_chat(
-            messages, system, tools, max_new_tokens=max_new_tokens, top_p=top_p, temperature=temperature
+            messages, system, tools, image, max_new_tokens=max_new_tokens, top_p=top_p, temperature=temperature
         ):
             response += new_text
             if tools:

src/llmtuner/webui/components/chatbot.py

@@ -23,9 +23,15 @@ def create_chat_box(
         messages = gr.State([])
         with gr.Row():
             with gr.Column(scale=4):
+                with gr.Row():
+                    with gr.Column():
                         role = gr.Dropdown(choices=[Role.USER.value, Role.OBSERVATION.value], value=Role.USER.value)
                         system = gr.Textbox(show_label=False)
-                tools = gr.Textbox(show_label=False, lines=2)
+                        tools = gr.Textbox(show_label=False, lines=4)
+
+                    with gr.Column():
+                        image = gr.Image(type="numpy")
+
                 query = gr.Textbox(show_label=False, lines=8)
                 submit_btn = gr.Button(variant="primary")
 
@@ -43,7 +49,7 @@ def create_chat_box(
         [chatbot, messages, query],
     ).then(
         engine.chatter.stream,
-        [chatbot, messages, system, tools, max_new_tokens, top_p, temperature],
+        [chatbot, messages, system, tools, image, max_new_tokens, top_p, temperature],
         [chatbot, messages],
     )
     clear_btn.click(lambda: ([], []), outputs=[chatbot, messages])
@@ -56,6 +62,7 @@ def create_chat_box(
             role=role,
             system=system,
             tools=tools,
+            image=image,
             query=query,
             submit_btn=submit_btn,
             max_new_tokens=max_new_tokens,

src/llmtuner/webui/locales.py

@@ -1073,6 +1073,17 @@ LOCALES = {
             "placeholder": "工具列表（非必填）",
         },
     },
+    "image": {
+        "en": {
+            "label": "Image (optional)",
+        },
+        "ru": {
+            "label": "Изображение (по желанию)",
+        },
+        "zh": {
+            "label": "图像（非必填）",
+        },
+    },
     "query": {
         "en": {
             "placeholder": "Input...",