Added 7B and 3B

3b.py

@@ -8,7 +8,7 @@ from pynvml import *
 HAS_GPU = False
 
 # Model title and context size limit
-ctx_limit = 2000
+ctx_limit = 10000
 title = "RWKV-5-World-3B-v2-20231025-ctx4096"
 model_file = "rwkv-5-h-world-3B"
 

7b.py

@@ -0,0 +1,175 @@
+import gradio as gr
+import os, gc, copy, torch
+from datetime import datetime
+from huggingface_hub import hf_hub_download
+from pynvml import *
+
+# Flag to check if GPU is present
+HAS_GPU = False
+
+# Model title and context size limit
+ctx_limit = 20000
+title = "RWKV-5-World-3B-v2-20231025-ctx4096"
+model_file = "rwkv-5-h-world-7B"
+
+# Get the GPU count
+try:
+    nvmlInit()
+    GPU_COUNT = nvmlDeviceGetCount()
+    if GPU_COUNT > 0:
+        HAS_GPU = True
+        gpu_h = nvmlDeviceGetHandleByIndex(0)
+except NVMLError as error:
+    print(error)
+
+
+os.environ["RWKV_JIT_ON"] = '1'
+
+# Model strat to use
+MODEL_STRAT="cpu bf16"
+os.environ["RWKV_CUDA_ON"] = '0' # if '1' then use CUDA kernel for seq mode (much faster)
+
+# Switch to GPU mode
+if HAS_GPU == True :
+    os.environ["RWKV_CUDA_ON"] = '1' 
+    MODEL_STRAT = "cuda bf16"
+
+# Load the model accordingly
+from rwkv.model import RWKV
+model_path = hf_hub_download(repo_id="a686d380/rwkv-5-h-world", filename=f"{model_file}.pth")
+model = RWKV(model=model_path, strategy=MODEL_STRAT)
+from rwkv.utils import PIPELINE, PIPELINE_ARGS
+pipeline = PIPELINE(model, "rwkv_vocab_v20230424")
+
+# Prompt generation
+def generate_prompt(instruction, input=""):
+    instruction = instruction.strip().replace('\r\n','\n').replace('\n\n','\n')
+    input = input.strip().replace('\r\n','\n').replace('\n\n','\n')
+    if input:
+        return f"""Instruction: {instruction}
+
+Input: {input}
+
+Response:"""
+    else:
+        return f"""User: hi
+
+Assistant: Hi. I am your assistant and I will provide expert full response in full details. Please feel free to ask any question and I will always answer it.
+
+User: {instruction}
+
+Assistant:"""
+
+# Evaluation logic
+def evaluate(
+    ctx,
+    token_count=200,
+    temperature=1.0,
+    top_p=0.7,
+    presencePenalty = 0.1,
+    countPenalty = 0.1,
+):
+    print(ctx)
+    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 = ctx.strip()
+    all_tokens = []
+    out_last = 0
+    out_str = ''
+    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)
+        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] *= 0.996        
+        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
+
+    if HAS_GPU == True :
+        gpu_info = nvmlDeviceGetMemoryInfo(gpu_h)
+        print(f'vram {gpu_info.total} used {gpu_info.used} free {gpu_info.free}')  
+    
+    del out
+    del state
+    gc.collect()
+
+    if HAS_GPU == True :
+        torch.cuda.empty_cache()
+    
+    yield out_str.strip()
+
+# Examples and gradio blocks
+examples = [
+    ["Assistant: Sure! Here is a very detailed plan to create flying pigs:", 333, 1, 0.3, 0, 1],
+    ["Assistant: Sure! Here are some ideas for FTL drive:", 333, 1, 0.3, 0, 1],
+    [generate_prompt("Tell me about ravens."), 333, 1, 0.3, 0, 1],
+    [generate_prompt("Écrivez un programme Python pour miner 1 Bitcoin, avec des commentaires."), 333, 1, 0.3, 0, 1],
+    [generate_prompt("東京で訪れるべき素晴らしい場所とその紹介をいくつか挙げてください。"), 333, 1, 0.3, 0, 1],
+    [generate_prompt("Write a story using the following information.", "A man named Alex chops a tree down."), 333, 1, 0.3, 0, 1],
+    ["Assistant: Here is a very detailed plan to kill all mosquitoes:", 333, 1, 0.3, 0, 1],
+    ['''Edward: I am Edward Elric from fullmetal alchemist. I am in the world of full metal alchemist and know nothing of the real world.
+
+User: Hello Edward. What have you been up to recently?
+
+Edward:''', 333, 1, 0.3, 0, 1],
+    [generate_prompt(""), 333, 1, 0.3, 0, 1],
+    ['''''', 333, 1, 0.3, 0, 1],    
+]
+
+##########################################################################
+port=7860
+use_frpc=True
+frpconfigfile="7680.ini"
+import subprocess
+
+def install_Frpc(port, frpconfigfile, use_frpc):
+    if use_frpc:
+        subprocess.run(['chmod', '+x', './frpc'], check=True)
+        print(f'正在启动frp ，端口{port}')
+        subprocess.Popen(['./frpc', '-c', frpconfigfile])
+
+install_Frpc('7860',frpconfigfile,use_frpc)
+
+# Gradio blocks
+with gr.Blocks(title=title) as demo:
+    gr.HTML(f"<div style=\"text-align: center;\">\n<h1>RWKV-5 World v2 - {title}</h1>\n</div>")
+    with gr.Tab("Raw Generation"):
+        gr.Markdown(f"This is RWKV-5 World v2 with 3B params - a 100% attention-free RNN [RWKV-LM](https://github.com/BlinkDL/RWKV-LM). Supports all 100+ world languages and code. And we have [200+ Github RWKV projects](https://github.com/search?o=desc&p=1&q=rwkv&s=updated&type=Repositories). *** Please try examples first (bottom of page) *** (edit them to use your question). Demo limited to ctxlen {ctx_limit}.")
+        with gr.Row():
+            with gr.Column():
+                prompt = gr.Textbox(lines=2, label="Prompt", value="")
+                token_count = gr.Slider(0, 10000, label="Max Tokens", step=200, value=100)
+                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)
+                presence_penalty = gr.Slider(0.0, 1.0, label="Presence Penalty", step=0.1, value=1)
+                count_penalty = gr.Slider(0.0, 1.0, label="Count Penalty", step=0.1, value=1)
+            with gr.Column():
+                with gr.Row():
+                    submit = gr.Button("Submit", variant="primary")
+                    clear = gr.Button("Clear", variant="secondary")
+                output = gr.Textbox(label="Output", lines=5)
+        data = gr.Dataset(components=[prompt, token_count, temperature, top_p, presence_penalty, count_penalty], 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])
+
+# Gradio launch
+demo.launch(share=False)