Update app.py

app.py

@@ -1,59 +1,69 @@
+import os, copy
+os.environ["RWKV_JIT_ON"] = '1'
+os.environ["RWKV_CUDA_ON"] = '1' # if '1' then use CUDA kernel for seq mode (much faster)
+# make sure cuda dir is in the same level as modeling_rwkv.py
+from modeling_rwkv import RWKV
+
+import gc, re
 import gradio as gr
-import os, gc, copy, torch, re
+import base64
+from io import BytesIO
+import torch
+import torch.nn.functional as F
 from datetime import datetime
+from transformers import CLIPImageProcessor
 from huggingface_hub import hf_hub_download
 from pynvml import *
 nvmlInit()
 gpu_h = nvmlDeviceGetHandleByIndex(0)
-ctx_limit = 1024
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+ctx_limit = 2500
 gen_limit = 500
 gen_limit_long = 800
-title = "RWKV-x060-World-7B-v3-20241112-ctx4096"
+ENABLE_VISUAL = False
 
-os.environ["RWKV_JIT_ON"] = '1'
-os.environ["RWKV_CUDA_ON"] = '1' # if '1' then use CUDA kernel for seq mode (much faster)
+########################## text rwkv ################################################################
+from rwkv.utils import PIPELINE, PIPELINE_ARGS
 
-from rwkv.model import RWKV
+title_v6 = "RWKV-x060-World-3B-v2.1-20240417-ctx4096"
+model_path_v6 = hf_hub_download(repo_id="BlinkDL/rwkv-6-world", filename=f"{title_v6}.pth")
+# model_path_v6 = '/mnt/e/RWKV-Runner/models/rwkv-final-v6-2.1-3b' # conda activate torch2; cd /mnt/program/_RWKV_/_ref_/_gradio_/RWKV-Gradio-1; python app.py
+model_v6 = RWKV(model=model_path_v6, strategy='cuda fp16')
+pipeline_v6 = PIPELINE(model_v6, "rwkv_vocab_v20230424")
 
-model_path = hf_hub_download(repo_id="BlinkDL/rwkv-6-world", filename=f"{title}.pth")
-model = RWKV(model=model_path, strategy='cuda fp16i8 *8 -> cuda fp16')
-# model_path = '/mnt/e/RWKV-Runner/models/rwkv-final-v6-2.1-7b' # conda activate torch2; cd /mnt/program/_RWKV_/_ref_/_gradio_/RWKV-Gradio-2; python app_tab.py
-# model = RWKV(model=model_path, strategy='cuda fp16i8 *8 -> cuda fp16')
+args = model_v6.args
+eng_name = 'rwkv-x060-eng_single_round_qa-3B-20240516-ctx2048'
+chn_name = 'rwkv-x060-chn_single_round_qa-3B-20240516-ctx2048'
 
-from rwkv.utils import PIPELINE, PIPELINE_ARGS
-pipeline = PIPELINE(model, "rwkv_vocab_v20230424")
+# state_eng_raw = torch.load(f'/mnt/e/RWKV-Runner/models/{eng_name}.pth', map_location=torch.device('cpu'))
+# state_chn_raw = torch.load(f'/mnt/e/RWKV-Runner/models/{chn_name}.pth', map_location=torch.device('cpu'))
 
-args = model.args
-eng_name = 'rwkv-x060-eng_single_round_qa-7B-20240516-ctx2048'
 eng_file = hf_hub_download(repo_id="BlinkDL/temp-latest-training-models", filename=f"{eng_name}.pth")
-state_eng_raw = torch.load(eng_file)
-state_eng = [None] * args.n_layer * 3
-
-chn_name = 'rwkv-x060-chn_single_round_qa-7B-20240516-ctx2048'
 chn_file = hf_hub_download(repo_id="BlinkDL/temp-latest-training-models", filename=f"{chn_name}.pth")
-state_chn_raw = torch.load(chn_file)
-state_chn = [None] * args.n_layer * 3
-
-wyw_name = 'rwkv-x060-chn_文言文和古典名著_single_round_qa-7B-20240601-ctx2048'
-wyw_file = hf_hub_download(repo_id="BlinkDL/temp-latest-training-models", filename=f"{wyw_name}.pth")
-state_wyw_raw = torch.load(wyw_file)
-state_wyw = [None] * args.n_layer * 3
+state_eng_raw = torch.load(eng_file, map_location=torch.device('cpu'))
+state_chn_raw = torch.load(chn_file, map_location=torch.device('cpu'))
 
+state_eng = [None] * args.n_layer * 3
+state_chn = [None] * args.n_layer * 3
 for i in range(args.n_layer):
-    dd = model.strategy[i]
+    dd = model_v6.strategy[i]
     dev = dd.device
     atype = dd.atype    
     state_eng[i*3+0] = torch.zeros(args.n_embd, dtype=atype, requires_grad=False, device=dev).contiguous()
-    state_eng[i*3+1] = state_eng_raw[f'blocks.{i}.att.time_state'].transpose(1,2).to(dtype=torch.float, device=dev).requires_grad_(False).contiguous()
-    state_eng[i*3+2] = torch.zeros(args.n_embd, dtype=atype, requires_grad=False, device=dev).contiguous()
-
     state_chn[i*3+0] = torch.zeros(args.n_embd, dtype=atype, requires_grad=False, device=dev).contiguous()
+    state_eng[i*3+1] = state_eng_raw[f'blocks.{i}.att.time_state'].transpose(1,2).to(dtype=torch.float, device=dev).requires_grad_(False).contiguous()
     state_chn[i*3+1] = state_chn_raw[f'blocks.{i}.att.time_state'].transpose(1,2).to(dtype=torch.float, device=dev).requires_grad_(False).contiguous()
+    state_eng[i*3+2] = torch.zeros(args.n_embd, dtype=atype, requires_grad=False, device=dev).contiguous()
     state_chn[i*3+2] = torch.zeros(args.n_embd, dtype=atype, requires_grad=False, device=dev).contiguous()
 
-    state_wyw[i*3+0] = torch.zeros(args.n_embd, dtype=atype, requires_grad=False, device=dev).contiguous()
-    state_wyw[i*3+1] = state_wyw_raw[f'blocks.{i}.att.time_state'].transpose(1,2).to(dtype=torch.float, device=dev).requires_grad_(False).contiguous()
-    state_wyw[i*3+2] = torch.zeros(args.n_embd, dtype=atype, requires_grad=False, device=dev).contiguous()
+penalty_decay = 0.996
+
+if ENABLE_VISUAL:
+    title = "RWKV-5-World-1B5-v2-20231025-ctx4096"
+    model_path = hf_hub_download(repo_id="BlinkDL/rwkv-5-world", filename=f"{title}.pth")
+    model = RWKV(model=model_path, strategy='cuda fp16')
+    pipeline = PIPELINE(model, "rwkv_vocab_v20230424")
 
 def generate_prompt(instruction, input=""):
     instruction = instruction.strip().replace('\r\n','\n').replace('\n\n','\n')
@@ -68,15 +78,13 @@ def qa_prompt(instruction):
     instruction = re.sub(r'\n+', '\n', instruction)
     return f"User: {instruction}\n\nAssistant:"""
 
-penalty_decay = 0.996
-
 def evaluate(
     ctx,
-    token_count=gen_limit,
+    token_count=200,
     temperature=1.0,
-    top_p=0.3,
-    presencePenalty = 0.3,
-    countPenalty = 0.3,
+    top_p=0.7,
+    presencePenalty = 0.1,
+    countPenalty = 0.1,
 ):
     args = PIPELINE_ARGS(temperature = max(0.2, float(temperature)), top_p = float(top_p),
                      alpha_frequency = countPenalty,
@@ -90,22 +98,30 @@ def evaluate(
     occurrence = {}
     state = None
     for i in range(int(token_count)):
-        out, state = model.forward(pipeline.encode(ctx)[-ctx_limit:] if i == 0 else [token], state)
+        input_ids = pipeline_v6.encode(ctx)[-ctx_limit:] if i == 0 else [token]
+        out, state = model_v6.forward(tokens=input_ids, state=state)
         for n in occurrence:
             out[n] -= (args.alpha_presence + occurrence[n] * args.alpha_frequency)
 
-        token = pipeline.sample_logits(out, temperature=args.temperature, top_p=args.top_p)
+        token = pipeline_v6.sample_logits(out, temperature=args.temperature, top_p=args.top_p)
         if token in args.token_stop:
             break
         all_tokens += [token]
         for xxx in occurrence:
             occurrence[xxx] *= penalty_decay
+            
+        ttt = pipeline_v6.decode([token])
+        www = 1
+        if ttt in ' \t0123456789':
+            www = 0
+        #elif ttt in '\r\n,.;?!"\':+-*/=#@$%^&_`~|<>\\()[]{}，。；“”：？！（）【】':
+        #    www = 0.5
         if token not in occurrence:
-            occurrence[token] = 1
+            occurrence[token] = www
         else:
-            occurrence[token] += 1
-        
-        tmp = pipeline.decode(all_tokens[out_last:])
+            occurrence[token] += www
+            
+        tmp = pipeline_v6.decode(all_tokens[out_last:])
         if '\ufffd' not in tmp:
             out_str += tmp
             yield out_str.strip()
@@ -113,7 +129,7 @@ def evaluate(
 
     gpu_info = nvmlDeviceGetMemoryInfo(gpu_h)
     timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
-    print(f'{timestamp} - vram {gpu_info.total} used {gpu_info.used} free {gpu_info.free}')  
+    print(f'{timestamp} - vram {gpu_info.total} used {gpu_info.used} free {gpu_info.free}')
     del out
     del state
     gc.collect()
@@ -122,11 +138,11 @@ def evaluate(
 
 def evaluate_eng(
     ctx,
-    token_count=gen_limit,
+    token_count=200,
     temperature=1.0,
-    top_p=0.3,
-    presencePenalty=0.3,
-    countPenalty=0.3,
+    top_p=0.7,
+    presencePenalty = 0.1,
+    countPenalty = 0.1,
 ):
     args = PIPELINE_ARGS(temperature = max(0.2, float(temperature)), top_p = float(top_p),
                      alpha_frequency = countPenalty,
@@ -140,22 +156,30 @@ def evaluate_eng(
     occurrence = {}
     state = copy.deepcopy(state_eng)
     for i in range(int(token_count)):
-        out, state = model.forward(pipeline.encode(ctx)[-ctx_limit:] if i == 0 else [token], state)
+        input_ids = pipeline_v6.encode(ctx)[-ctx_limit:] if i == 0 else [token]
+        out, state = model_v6.forward(tokens=input_ids, state=state)
         for n in occurrence:
             out[n] -= (args.alpha_presence + occurrence[n] * args.alpha_frequency)
 
-        token = pipeline.sample_logits(out, temperature=args.temperature, top_p=args.top_p)
+        token = pipeline_v6.sample_logits(out, temperature=args.temperature, top_p=args.top_p)
         if token in args.token_stop:
             break
         all_tokens += [token]
         for xxx in occurrence:
             occurrence[xxx] *= penalty_decay
+            
+        ttt = pipeline_v6.decode([token])
+        www = 1
+        if ttt in ' \t0123456789':
+            www = 0
+        #elif ttt in '\r\n,.;?!"\':+-*/=#@$%^&_`~|<>\\()[]{}，。；“”：？！（）【】':
+        #    www = 0.5
         if token not in occurrence:
-            occurrence[token] = 1
+            occurrence[token] = www
         else:
-            occurrence[token] += 1
-        
-        tmp = pipeline.decode(all_tokens[out_last:])
+            occurrence[token] += www
+            
+        tmp = pipeline_v6.decode(all_tokens[out_last:])
         if '\ufffd' not in tmp:
             out_str += tmp
             yield out_str.strip()
@@ -163,7 +187,7 @@ def evaluate_eng(
 
     gpu_info = nvmlDeviceGetMemoryInfo(gpu_h)
     timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
-    print(f'{timestamp} - vram {gpu_info.total} used {gpu_info.used} free {gpu_info.free}')  
+    print(f'{timestamp} - vram {gpu_info.total} used {gpu_info.used} free {gpu_info.free}')
     del out
     del state
     gc.collect()
@@ -172,11 +196,11 @@ def evaluate_eng(
 
 def evaluate_chn(
     ctx,
-    token_count=gen_limit,
+    token_count=200,
     temperature=1.0,
-    top_p=0.3,
-    presencePenalty=0.3,
-    countPenalty=0.3,
+    top_p=0.7,
+    presencePenalty = 0.1,
+    countPenalty = 0.1,
 ):
     args = PIPELINE_ARGS(temperature = max(0.2, float(temperature)), top_p = float(top_p),
                      alpha_frequency = countPenalty,
@@ -190,22 +214,30 @@ def evaluate_chn(
     occurrence = {}
     state = copy.deepcopy(state_chn)
     for i in range(int(token_count)):
-        out, state = model.forward(pipeline.encode(ctx)[-ctx_limit:] if i == 0 else [token], state)
+        input_ids = pipeline_v6.encode(ctx)[-ctx_limit:] if i == 0 else [token]
+        out, state = model_v6.forward(tokens=input_ids, state=state)
         for n in occurrence:
             out[n] -= (args.alpha_presence + occurrence[n] * args.alpha_frequency)
 
-        token = pipeline.sample_logits(out, temperature=args.temperature, top_p=args.top_p)
+        token = pipeline_v6.sample_logits(out, temperature=args.temperature, top_p=args.top_p)
         if token in args.token_stop:
             break
         all_tokens += [token]
         for xxx in occurrence:
             occurrence[xxx] *= penalty_decay
+            
+        ttt = pipeline_v6.decode([token])
+        www = 1
+        if ttt in ' \t0123456789':
+            www = 0
+        #elif ttt in '\r\n,.;?!"\':+-*/=#@$%^&_`~|<>\\()[]{}，。；“”：？！（）【】':
+        #    www = 0.5
         if token not in occurrence:
-            occurrence[token] = 1
+            occurrence[token] = www
         else:
-            occurrence[token] += 1
-        
-        tmp = pipeline.decode(all_tokens[out_last:])
+            occurrence[token] += www
+            
+        tmp = pipeline_v6.decode(all_tokens[out_last:])
         if '\ufffd' not in tmp:
             out_str += tmp
             yield out_str.strip()
@@ -213,57 +245,7 @@ def evaluate_chn(
 
     gpu_info = nvmlDeviceGetMemoryInfo(gpu_h)
     timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
-    print(f'{timestamp} - vram {gpu_info.total} used {gpu_info.used} free {gpu_info.free}')  
-    del out
-    del state
-    gc.collect()
-    torch.cuda.empty_cache()
-    yield out_str.strip()
-
-def evaluate_wyw(
-    ctx,
-    token_count=gen_limit,
-    temperature=1.0,
-    top_p=0.3,
-    presencePenalty=0.3,
-    countPenalty=0.3,
-):
-    args = PIPELINE_ARGS(temperature = max(0.2, float(temperature)), top_p = float(top_p),
-                     alpha_frequency = countPenalty,
-                     alpha_presence = presencePenalty,
-                     token_ban = [], # ban the generation of some tokens
-                     token_stop = [0]) # stop generation whenever you see any token here
-    ctx = qa_prompt(ctx)
-    all_tokens = []
-    out_last = 0
-    out_str = ''
-    occurrence = {}
-    state = copy.deepcopy(state_wyw)
-    for i in range(int(token_count)):
-        out, state = model.forward(pipeline.encode(ctx)[-ctx_limit:] if i == 0 else [token], state)
-        for n in occurrence:
-            out[n] -= (args.alpha_presence + occurrence[n] * args.alpha_frequency)
-
-        token = pipeline.sample_logits(out, temperature=args.temperature, top_p=args.top_p)
-        if token in args.token_stop:
-            break
-        all_tokens += [token]
-        for xxx in occurrence:
-            occurrence[xxx] *= penalty_decay
-        if token not in occurrence:
-            occurrence[token] = 1
-        else:
-            occurrence[token] += 1
-        
-        tmp = pipeline.decode(all_tokens[out_last:])
-        if '\ufffd' not in tmp:
-            out_str += tmp
-            yield out_str.strip()
-            out_last = i + 1
-
-    gpu_info = nvmlDeviceGetMemoryInfo(gpu_h)
-    timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
-    print(f'{timestamp} - vram {gpu_info.total} used {gpu_info.used} free {gpu_info.free}')  
+    print(f'{timestamp} - vram {gpu_info.total} used {gpu_info.used} free {gpu_info.free}')
     del out
     del state
     gc.collect()
@@ -292,36 +274,163 @@ examples_eng = [
     ["Write an outline for a fantasy novel where dreams can alter reality.", gen_limit_long, 1, 0.2, 0.3, 0.3],
     ["Can fish get thirsty?", gen_limit_long, 1, 0.2, 0.3, 0.3],
     ["Write a Bash script to check disk usage and send alerts if it's too high.", gen_limit_long, 1, 0.2, 0.3, 0.3],
-    ["Write a simple website in HTML. When a user clicks the button, it shows a random joke from a list of 4 jokes.", gen_limit_long, 1, 0.2, 0.3, 0.3],
+    ["Write a simple webpage. When a user clicks the button, it shows a random joke from a list of 4 jokes.", gen_limit_long, 1, 0.2, 0.3, 0.3],
 ]
 
 examples_chn = [
     ["怎样写一个在火星上的吸血鬼的有趣故事？", gen_limit_long, 1, 0.2, 0.3, 0.3],
     ["比较苹果和谷歌的商业模式。", gen_limit_long, 1, 0.2, 0.3, 0.3],
     ["鱼会口渴吗？", gen_limit_long, 1, 0.2, 0.3, 0.3],
-    ["以 JSON 格式解释冰箱是如何工作的。", gen_limit_long, 1, 0.2, 0.3, 0.3],
+    ["以 JSON 格式列举���京的美食。", gen_limit_long, 1, 0.2, 0.3, 0.3],
     ["编写一个Bash脚本来检查磁盘使用情况，如果使用量过高则发送警报。", gen_limit_long, 1, 0.2, 0.3, 0.3],
     ["用HTML编写一个简单的网站。当用户点击按钮时，从4个笑话的列表中随机显示一个笑话。", gen_limit_long, 1, 0.2, 0.3, 0.3],
 ]
 
-examples_wyw = [
-    ["我和前男友分手了", gen_limit_long, 1, 0.2, 0.3, 0.3],
-    ["量子计算机的原理", gen_limit_long, 1, 0.2, 0.3, 0.3],
-    ["李白和杜甫的结拜故事", gen_limit_long, 1, 0.2, 0.3, 0.3],
-    ["林黛玉和伏地魔的关系是什么？", gen_limit_long, 1, 0.2, 0.3, 0.3],
-    ["我被同事陷害了，帮我写一篇文言文骂他", gen_limit_long, 1, 0.2, 0.3, 0.3],
-]
-
-##########################################################################
-
-with gr.Blocks(title=title) as demo:
-    gr.HTML(f"<div style=\"text-align: center;\">\n<h1>{title}</h1>\n</div>")
+if ENABLE_VISUAL:
+    ########################## visual rwkv ################################################################
+    visual_title = 'ViusualRWKV-v5'
+    rwkv_remote_path = "rwkv1b5-vitl336p14-577token_mix665k_rwkv.pth"
+    vision_remote_path = "rwkv1b5-vitl336p14-577token_mix665k_visual.pth"
+    vision_tower_name = 'openai/clip-vit-large-patch14-336'
+
+    model_path = hf_hub_download(repo_id="howard-hou/visualrwkv-5", filename=rwkv_remote_path)
+    visual_rwkv = RWKV(model=model_path, strategy='cuda fp16')
+
+    ##########################################################################
+    from modeling_vision import VisionEncoder, VisionEncoderConfig
+    config = VisionEncoderConfig(n_embd=model.args.n_embd, 
+                                vision_tower_name=vision_tower_name, 
+                                grid_size=-1)
+    visual_encoder = VisionEncoder(config)
+    vision_local_path = hf_hub_download(repo_id="howard-hou/visualrwkv-5", filename=vision_remote_path)
+    vision_state_dict = torch.load(vision_local_path, map_location='cpu')
+    visual_encoder.load_state_dict(vision_state_dict)
+    image_processor = CLIPImageProcessor.from_pretrained(vision_tower_name)
+    visual_encoder = visual_encoder.to(device)
+    ##########################################################################
+    def visual_generate_prompt(instruction):
+        instruction = instruction.strip().replace('\r\n','\n').replace('\n\n','\n')
+        return f"\n{instruction}\n\nAssistant:"
+
+    def generate(
+        ctx,
+        image_state,
+        token_count=200,
+        temperature=1.0,
+        top_p=0.1,
+        presencePenalty = 0.0,
+        countPenalty = 1.0,
+    ):
+        args = PIPELINE_ARGS(temperature = 1.0, top_p = 0.1,
+                        alpha_frequency = 1.0,
+                        alpha_presence = 0.0,
+                        token_ban = [], # ban the generation of some tokens
+                        token_stop = [0, 261]) # stop generation whenever you see any token here
+        ctx = ctx.strip()
+        all_tokens = []
+        out_last = 0
+        out_str = ''
+        occurrence = {}
+        for i in range(int(token_count)):
+            if i == 0:
+                input_ids = pipeline.encode(ctx)[-ctx_limit:]
+                out, state = visual_rwkv.forward(tokens=input_ids, state=image_state)
+            else:
+                input_ids = [token]
+                out, state = visual_rwkv.forward(tokens=input_ids, state=state)
+            for n in occurrence:
+                out[n] -= (args.alpha_presence + occurrence[n] * args.alpha_frequency)
+
+            token = pipeline.sample_logits(out, temperature=args.temperature, top_p=args.top_p)
+            if token in args.token_stop:
+                break
+            all_tokens += [token]
+            for xxx in occurrence:
+                occurrence[xxx] *= 0.994        
+            if token not in occurrence:
+                occurrence[token] = 1
+            else:
+                occurrence[token] += 1
+            
+            tmp = pipeline.decode(all_tokens[out_last:])
+            if '\ufffd' not in tmp:
+                out_str += tmp
+                yield out_str.strip()
+                out_last = i + 1
+
+        gpu_info = nvmlDeviceGetMemoryInfo(gpu_h)
+        timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+        print(f'{timestamp} - vram {gpu_info.total} used {gpu_info.used} free {gpu_info.free}')
+        del out
+        del state
+        gc.collect()
+        torch.cuda.empty_cache()
+        yield out_str.strip()
+
+
+    ##########################################################################
+    cur_dir = os.path.dirname(os.path.abspath(__file__))
+    visual_examples = [
+        [
+            f"{cur_dir}/examples_pizza.jpg",
+            "What are steps to cook it?"
+        ],
+        [
+            f"{cur_dir}/examples_bluejay.jpg",
+            "what is the name of this bird?",
+        ],
+        [
+            f"{cur_dir}/examples_woman_and_dog.png",
+            "describe this image",
+        ],
+    ]
+
+
+    def pil_image_to_base64(pil_image):
+        buffered = BytesIO()
+        pil_image.save(buffered, format="JPEG")  # You can change the format as needed (JPEG, PNG, etc.)
+        # Encodes the image data into base64 format as a bytes object
+        base64_image = base64.b64encode(buffered.getvalue()).decode('utf-8')
+        return base64_image
+
+    image_cache = {}
+    ln0_weight = model.w['blocks.0.ln0.weight'].to(torch.float32).to(device)
+    ln0_bias = model.w['blocks.0.ln0.bias'].to(torch.float32).to(device)
+    def compute_image_state(image):
+        base64_image = pil_image_to_base64(image)
+        if base64_image in image_cache:
+            image_state = image_cache[base64_image]
+        else:
+            image = image_processor(images=image.convert('RGB'), return_tensors='pt')['pixel_values'].to(device)
+            image_features = visual_encoder.encode_images(image.unsqueeze(0)).squeeze(0) # [L, D]
+            # apply layer norm to image feature, very important
+            image_features = F.layer_norm(image_features, 
+                                        (image_features.shape[-1],), 
+                                        weight=ln0_weight, 
+                                        bias=ln0_bias)
+            _, image_state = model.forward(embs=image_features, state=None)
+            image_cache[base64_image] = image_state
+        return image_state
+
+    def chatbot(image, question):
+        if image is None:
+            yield "Please upload an image."
+            return
+        image_state = compute_image_state(image)
+        input_text = visual_generate_prompt(question)
+        for output in generate(input_text, image_state):
+            yield output
+
+
+##################################################################################################################
+with gr.Blocks(title=title_v6) as demo:
+    gr.HTML(f"<div style=\"text-align: center;\">\n<h1>{title_v6}</h1>\n</div>")
 
     with gr.Tab("=== Base Model (Raw Generation) ==="):
-        gr.Markdown(f"This is [RWKV-6](https://huggingface.co/BlinkDL/rwkv-6-world) base model. Supports 100+ world languages and code. RWKV is a 100% attention-free RNN [RWKV-LM](https://github.com/BlinkDL/RWKV-LM), and we have [400+ Github RWKV projects](https://github.com/search?o=desc&p=1&q=rwkv&s=updated&type=Repositories). Demo limited to ctxlen {ctx_limit}.")
+        gr.Markdown(f"This is [RWKV-6 World v2](https://huggingface.co/BlinkDL/rwkv-6-world) - a 100% attention-free RNN [RWKV-LM](https://github.com/BlinkDL/RWKV-LM). Supports 100+ world languages and code. Check [300+ Github RWKV projects](https://github.com/search?o=desc&p=1&q=rwkv&s=updated&type=Repositories). *** Can try examples (bottom of page) *** (can edit them). Demo limited to ctxlen {ctx_limit}.")
         with gr.Row():
             with gr.Column():
-                prompt = gr.Textbox(lines=2, label="Raw Input", value="Assistant: How can we craft an engaging story featuring vampires on Mars? Let's think step by step and provide an expert response.")
+                prompt = gr.Textbox(lines=2, label="Prompt", value="Assistant: How can we craft an engaging story featuring vampires on Mars? Let's think step by step and provide an expert response.")
                 token_count = gr.Slider(10, gen_limit, label="Max Tokens", step=10, value=gen_limit)
                 temperature = gr.Slider(0.2, 2.0, label="Temperature", step=0.1, value=1.0)
                 top_p = gr.Slider(0.0, 1.0, label="Top P", step=0.05, value=0.3)
@@ -332,10 +441,68 @@ with gr.Blocks(title=title) as demo:
                     submit = gr.Button("Submit", variant="primary")
                     clear = gr.Button("Clear", variant="secondary")
                 output = gr.Textbox(label="Output", lines=30)
-        data = gr.Dataset(components=[prompt, token_count, temperature, top_p, presence_penalty, count_penalty], samples=examples, samples_per_page=50, label="Examples", headers=["Prompt", "Max Tokens", "Temperature", "Top P", "Presence Penalty", "Count Penalty"])
+        data = gr.Dataset(components=[prompt, token_count, temperature, top_p, presence_penalty, count_penalty], samples=examples, samples_per_page=50, label="Example Instructions", headers=["Prompt", "Max Tokens", "Temperature", "Top P", "Presence Penalty", "Count Penalty"])
         submit.click(evaluate, [prompt, token_count, temperature, top_p, presence_penalty, count_penalty], [output])
         clear.click(lambda: None, [], [output])
         data.click(lambda x: x, [data], [prompt, token_count, temperature, top_p, presence_penalty, count_penalty])
 
+    with gr.Tab("=== English Q/A ==="):
+        gr.Markdown(f"This is [RWKV-6](https://huggingface.co/BlinkDL/rwkv-6-world) state-tuned to [English Q/A](https://huggingface.co/BlinkDL/temp-latest-training-models/blob/main/{eng_name}.pth). RWKV is a 100% attention-free RNN [RWKV-LM](https://github.com/BlinkDL/RWKV-LM), and we have [300+ Github RWKV projects](https://github.com/search?o=desc&p=1&q=rwkv&s=updated&type=Repositories). Demo limited to ctxlen {ctx_limit}.")
+        with gr.Row():
+            with gr.Column():
+                prompt = gr.Textbox(lines=2, label="Prompt", value="How can I craft an engaging story featuring vampires on Mars?")
+                token_count = gr.Slider(10, gen_limit_long, label="Max Tokens", step=10, value=gen_limit_long)
+                temperature = gr.Slider(0.2, 2.0, label="Temperature", step=0.1, value=1.0)
+                top_p = gr.Slider(0.0, 1.0, label="Top P", step=0.05, value=0.2)
+                presence_penalty = gr.Slider(0.0, 1.0, label="Presence Penalty", step=0.1, value=0.3)
+                count_penalty = gr.Slider(0.0, 1.0, label="Count Penalty", step=0.1, value=0.3)
+            with gr.Column():
+                with gr.Row():
+                    submit = gr.Button("Submit", variant="primary")
+                    clear = gr.Button("Clear", variant="secondary")
+                output = gr.Textbox(label="Output", lines=30)
+        data = gr.Dataset(components=[prompt, token_count, temperature, top_p, presence_penalty, count_penalty], samples=examples_eng, samples_per_page=50, label="Examples", headers=["Prompt", "Max Tokens", "Temperature", "Top P", "Presence Penalty", "Count Penalty"])
+        submit.click(evaluate_eng, [prompt, token_count, temperature, top_p, presence_penalty, count_penalty], [output])
+        clear.click(lambda: None, [], [output])
+        data.click(lambda x: x, [data], [prompt, token_count, temperature, top_p, presence_penalty, count_penalty])
+
+    with gr.Tab("=== Chinese Q/A ==="):
+        gr.Markdown(f"This is [RWKV-6](https://huggingface.co/BlinkDL/rwkv-6-world) state-tuned to [Chinese Q/A](https://huggingface.co/BlinkDL/temp-latest-training-models/blob/main/{chn_name}.pth). RWKV is a 100% attention-free RNN [RWKV-LM](https://github.com/BlinkDL/RWKV-LM), and we have [300+ Github RWKV projects](https://github.com/search?o=desc&p=1&q=rwkv&s=updated&type=Repositories). Demo limited to ctxlen {ctx_limit}.")
+        with gr.Row():
+            with gr.Column():
+                prompt = gr.Textbox(lines=2, label="Prompt", value="怎样写一个在火星上的吸血鬼的有趣故事？")
+                token_count = gr.Slider(10, gen_limit_long, label="Max Tokens", step=10, value=gen_limit_long)
+                temperature = gr.Slider(0.2, 2.0, label="Temperature", step=0.1, value=1.0)
+                top_p = gr.Slider(0.0, 1.0, label="Top P", step=0.05, value=0.2)
+                presence_penalty = gr.Slider(0.0, 1.0, label="Presence Penalty", step=0.1, value=0.3)
+                count_penalty = gr.Slider(0.0, 1.0, label="Count Penalty", step=0.1, value=0.3)
+            with gr.Column():
+                with gr.Row():
+                    submit = gr.Button("Submit", variant="primary")
+                    clear = gr.Button("Clear", variant="secondary")
+                output = gr.Textbox(label="Output", lines=30)
+        data = gr.Dataset(components=[prompt, token_count, temperature, top_p, presence_penalty, count_penalty], samples=examples_chn, samples_per_page=50, label="Examples", headers=["Prompt", "Max Tokens", "Temperature", "Top P", "Presence Penalty", "Count Penalty"])
+        submit.click(evaluate_chn, [prompt, token_count, temperature, top_p, presence_penalty, count_penalty], [output])
+        clear.click(lambda: None, [], [output])
+        data.click(lambda x: x, [data], [prompt, token_count, temperature, top_p, presence_penalty, count_penalty])        
+
+    if ENABLE_VISUAL:
+        with gr.Tab("Visual RWKV-5 1.5B"):
+            with gr.Row():
+                with gr.Column():
+                    image = gr.Image(type='pil', label="Image")
+                with gr.Column():
+                    prompt = gr.Textbox(lines=8, label="Prompt", 
+                        value="Render a clear and concise summary of the photo.")
+                    with gr.Row():
+                        submit = gr.Button("Submit", variant="primary")
+                        clear = gr.Button("Clear", variant="secondary") 
+                with gr.Column():
+                    output = gr.Textbox(label="Output", lines=10)
+            data = gr.Dataset(components=[image, prompt], samples=visual_examples, label="Examples", headers=["Image", "Prompt"])
+            submit.click(chatbot, [image, prompt], [output])
+            clear.click(lambda: None, [], [output])
+            data.click(lambda x: x, [data], [image, prompt])
+
 demo.queue(concurrency_count=1, max_size=10)
-demo.launch(share=False)
+demo.launch(share=False)