Update app.py

app.py

@@ -44,19 +44,60 @@ def evaluate(
     token_count=200,
     temperature=1.0,
     top_p=0.7,
-    **kwargs,
+    presencePenalty = 0.1,
+    countPenalty = 0.1,
 ):
-    prompt = generate_prompt(instruction, input)
-    return prompt
+    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
+
+    instruction = instruction.strip()
+    input = input.strip()
+    ctx = generate_prompt(instruction, input)
+    
+    gpu_info = nvmlDeviceGetMemoryInfo(gpu_h)
+    print(f'vram {gpu_info.total} used {gpu_info.used} free {gpu_info.free}')
+    
+    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]
+        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
+    gc.collect()
+    torch.cuda.empty_cache()
+    yield out_str.strip()
 
 g = gr.Interface(
     fn=evaluate,
     inputs=[
         gr.components.Textbox(lines=2, label="Instruction", value="Tell me about alpacas."),
         gr.components.Textbox(lines=2, label="Input", placeholder="none"),
-        gr.components.Slider(minimum=10, maximum=250, step=10, value=200),
-        gr.components.Slider(minimum=0.2, maximum=2.0, step=0.1, value=1.0),
-        gr.components.Slider(minimum=0, maximum=1, step=0.05, value=0.7),
+        gr.components.Slider(minimum=10, maximum=250, step=10, value=200), # token_count
+        gr.components.Slider(minimum=0.2, maximum=2.0, step=0.1, value=1.0), # temperature
+        gr.components.Slider(minimum=0, maximum=1, step=0.05, value=0.7), # top_p
+        gr.components.Slider(0.0, 1.0, step=0.1, value=0.2),  # presencePenalty
+        gr.components.Slider(0.0, 1.0, step=0.1, value=0.2),  # countPenalty        
     ],
     outputs=[
         gr.inputs.Textbox(
@@ -64,8 +105,8 @@ g = gr.Interface(
             label="Output",
         )
     ],
-    title="🐦Raven-RWKV 7B",
-    description="Raven-RWKV 7B is [RWKV 7B](https://github.com/BlinkDL/ChatRWKV) finetuned to follow instructions. It is trained on the [Stanford Alpaca](https://github.com/tatsu-lab/stanford_alpaca) dataset and more.",
+    title=f"🐦Raven {title}",
+    description="Raven is [RWKV 7B](https://github.com/BlinkDL/ChatRWKV) finetuned to follow instructions. It is trained on the [Stanford Alpaca](https://github.com/tatsu-lab/stanford_alpaca) dataset and more.",
 )
 g.queue(concurrency_count=1, max_size=10)
 g.launch(share=False)