Update app.py

app.py

@@ -1,7 +1,100 @@
 import gradio as gr
+import os
+from datetime import datetime
+from huggingface_hub import hf_hub_download
 
-def greet(name):
-    return 'Please use https://huggingface.co/spaces/yahma/rwkv-14b first :)'
+title = "RWKV-4 14B fp16 ctx4096"
+desc = '''Links:
+<a href='https://github.com/BlinkDL/ChatRWKV' target="_blank" style="margin:0 1em">ChatRWKV</a>
+<a href='https://github.com/BlinkDL/RWKV-LM' target="_blank" style="margin:0 1em">RWKV-LM</a>
+<a href="https://pypi.org/project/rwkv/" target="_blank" style="margin:0 1em">RWKV pip package</a>
+'''
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()
+os.environ["RWKV_JIT_ON"] = '1'
+os.environ["RWKV_CUDA_ON"] = '1' # if '1' then use CUDA kernel for seq mode (much faster)
+
+from rwkv.model import RWKV
+model_path = hf_hub_download(repo_id="BlinkDL/rwkv-4-pile-169m", filename="RWKV-4-Pile-169M-20220807-8023.pth")
+model = RWKV(model=model_path, strategy='cuda fp16')
+from rwkv.utils import PIPELINE, PIPELINE_ARGS
+pipeline = PIPELINE(model, "20B_tokenizer.json")
+
+def infer(
+        ctx,
+        token_count=10,
+        temperature=1.0,
+        top_p=0.85,
+        presencePenalty = 0.1,
+        countPenalty = 0.1,
+):
+    args = PIPELINE_ARGS(temperature = max(0.2, float(temperature)), top_p = float(top_p),
+                     alpha_frequency = countPenalty,
+                     alpha_presence = presencePenalty,
+                     token_ban = [0], # ban the generation of some tokens
+                     token_stop = []) # stop generation whenever you see any token here
+
+    ctx = ctx.strip(' ')
+    if ctx.endswith('\n'):
+        ctx = f'\n{ctx.strip()}\n'
+    else:
+        ctx = f'\n{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) if i == 0 else [token], state)
+        for n in args.token_ban:
+            out[n] = -float('inf')
+        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]
+        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
+    yield out_str.strip()
+
+examples = [
+    ["Ask Expert\n\nQuestion:\nWhat are some good plans for world peace?\n\nExpert Full Answer:\n", 100, 1.0, 0.85, 0.1, 0.1],
+    ["Q & A\n\nQuestion:\nWhy is the sky blue?\n\nDetailed Expert Answer:\n", 100, 1.0, 0.85, 0.1, 0.1],
+    ["Expert Questions & Helpful Answers\nAsk Research Experts\nQuestion:\nCan you write a short story about an elf maiden named Julia that meets a warrior named Rallio and they go on an adventure together?\n\nFull Answer:\n", 100, 1.0, 0.85, 0.1, 0.1],
+]
+
+
+iface = gr.Interface(
+    fn=infer,
+    description=f'''{desc}''',
+    allow_flagging="never",
+    inputs=[
+        gr.Textbox(lines=20, label="Prompt"),  # prompt
+        gr.Slider(10, 200, step=10, value=100),  # token_count
+        gr.Slider(0.2, 2.0, step=0.1, value=1.0),  # temperature
+        gr.Slider(0.0, 1.0, step=0.05, value=0.85),  # top_p
+        gr.Slider(0.0, 1.0, step=0.1, value=0.1),  # presencePenalty
+        gr.Slider(0.0, 1.0, step=0.1, value=0.1),  # countPenalty
+    ],
+    outputs=gr.Textbox(label="Generated Output", lines=35),
+    examples=examples,
+    cache_examples=False,
+).queue()
+
+demo = gr.TabbedInterface(
+    [iface], ["Generative"],
+    title=title,
+)
+
+demo.queue()
+demo.launch(share=False)