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MiniMind2_tokenizer/special_tokens_map.json

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+{
+  "bos_token": {
+    "content": "<|im_start|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<|im_end|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

MiniMind2_tokenizer/tokenizer_config.json

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+{
+  "add_bos_token": false,
+  "add_eos_token": false,
+  "add_prefix_space": false,
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<|endoftext|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<|im_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "<|im_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "additional_special_tokens": [],
+  "bos_token": "<|im_start|>",
+  "chat_template": "{% if messages[0]['role'] == 'system' %}{% set system_message = messages[0]['content'] %}{{ '<|im_start|>system\\n' + system_message + '<|im_end|>\\n' }}{% else %}{{ '<|im_start|>system\\nYou are a helpful assistant<|im_end|>\\n' }}{% endif %}{% for message in messages %}{% set content = message['content'] %}{% if message['role'] == 'user' %}{{ '<|im_start|>user\\n' + content + '<|im_end|>\\n<|im_start|>assistant\\n' }}{% elif message['role'] == 'assistant' %}{{ content + '<|im_end|>' + '\\n' }}{% endif %}{% endfor %}",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "extra_special_tokens": {},
+  "legacy": true,
+  "model_max_length": 32768,
+  "pad_token": "<|endoftext|>",
+  "sp_model_kwargs": {},
+  "spaces_between_special_tokens": false,
+  "tokenizer_class": "PreTrainedTokenizer",
+  "unk_token": "<|endoftext|>"
+}

app.py

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+import os
+import copy
+import types
+import torch
+from transformers import AutoTokenizer
+import gradio as gr
+
+os.environ["RWKV_V7_ON"] = "1"
+os.environ["RWKV_JIT_ON"] = "1"
+os.environ["RWKV_CUDA_ON"] = "0"
+
+from rwkv.model import RWKV
+from rwkv.utils import PIPELINE
+
+args = types.SimpleNamespace()
+args.strategy = "cuda fp16"
+args.MODEL_NAME = "./rwkv-final-sft-2048"
+
+STATE_NAME = None
+GEN_TEMP = 1.0
+GEN_TOP_P = 0.3
+GEN_alpha_presence = 0.5
+GEN_alpha_frequency = 0.5
+GEN_penalty_decay = 0.996
+CHUNK_LEN = 16
+
+print(f"Loading model - {args.MODEL_NAME}")
+model = RWKV(model=args.MODEL_NAME, strategy=args.strategy)
+pipeline = PIPELINE(model, "rwkv_vocab_v20230424")
+tokenizer = AutoTokenizer.from_pretrained("./MiniMind2_tokenizer")
+
+model_tokens = []
+model_state = None
+
+if STATE_NAME is not None:
+    GEN_TOP_P = 0.2
+    GEN_alpha_presence = 0.3
+    GEN_alpha_frequency = 0.3
+    
+    args = model.args
+    state_raw = torch.load(STATE_NAME + '.pth')
+    state_init = [None for i in range(args.n_layer * 3)]
+    for i in range(args.n_layer):
+        dd = model.strategy[i]
+        dev = dd.device
+        atype = dd.atype    
+        state_init[i*3+0] = torch.zeros(args.n_embd, dtype=atype, requires_grad=False, device=dev).contiguous()
+        state_init[i*3+1] = state_raw[f'blocks.{i}.att.time_state'].transpose(1,2).to(dtype=torch.float, device=dev).requires_grad_(False).contiguous()
+        state_init[i*3+2] = torch.zeros(args.n_embd, dtype=atype, requires_grad=False, device=dev).contiguous()
+    model_state = copy.deepcopy(state_init)
+
+def run_rnn(ctx, state):
+    ctx = ctx.replace("\r\n", "\n")
+    tokens = tokenizer.encode(ctx)
+    tokens = [int(x) for x in tokens]
+    
+    current_state = copy.deepcopy(state) if state is not None else None
+    
+    while len(tokens) > 0:
+        out, current_state = model.forward(tokens[:CHUNK_LEN], current_state)
+        tokens = tokens[CHUNK_LEN:]
+    
+    return out, current_state
+
+def generate_response(message, history, temperature=1.0, top_p=0.3):
+    global model_tokens, model_state
+    
+    ctx = ""
+    for human, assistant in history:
+        ctx += f"<|im_start|>user\n{human}<|im_end|>\n<|im_start|>assistant\n{assistant}<!--eos--><|im_end|>\n"
+    
+    ctx += f"<|im_start|>user\n{message}<|im_end|>\n<|im_start|>assistant\n"
+    
+    out, model_state = run_rnn(ctx, model_state)
+    
+    occurrence = {}
+    out_tokens = []
+    out_last = 0
+    response = ""
+    
+    eos_token_id = tokenizer.eos_token_id
+    im_end_id = tokenizer.encode("<|im_end|>")[0]  
+    for i in range(99999):
+        logits = out.clone()
+        for n in occurrence:
+            logits[n] -= GEN_alpha_presence + occurrence[n] * GEN_alpha_frequency
+        
+        logits[0] -= 1e10  
+        
+        token = pipeline.sample_logits(logits, temperature=temperature, top_p=top_p)
+        
+        if token == im_end_id:
+            break
+            
+        out, model_state = model.forward([token], model_state)
+        
+        out_tokens += [token]
+        for xxx in occurrence:
+            occurrence[xxx] *= GEN_penalty_decay
+        occurrence[token] = 1 + (occurrence[token] if token in occurrence else 0)
+        
+        tmp = tokenizer.decode(out_tokens[out_last:])
+        if "\ufffd" not in tmp:  
+            response += tmp
+            cleaned_response = response.replace("<|im_end|>", "")
+            yield cleaned_response
+            out_last = i + 1
+            
+            if token == eos_token_id:
+                break
+
+def chat_with_bot(message, history, temperature, top_p):
+    response = ""
+    for partial_response in generate_response(message, history, temperature, top_p):
+        response = partial_response
+        yield response
+
+with gr.Blocks(title="MiniRWKV_7 34.2M 🪿 2vGPU Space") as demo:
+    gr.Markdown("# MiniRWKV_7 34.2M 🪿 ")
+    gr.Markdown("### Only 34.2M Params!!! Use 2V CPU Backend to run this model. ")
+    
+    with gr.Row():
+        with gr.Column(scale=3):
+            chatbot = gr.Chatbot(
+                label="对话记录",
+                height=500,
+            )
+        
+        with gr.Column(scale=1):
+            msg = gr.Textbox(
+                label="输入消息",
+                placeholder="请输入您的问题...",
+                lines=3
+            )
+            
+            with gr.Row():
+                send_btn = gr.Button("发送", variant="primary")
+                clear_btn = gr.Button("清除历史")
+            
+            gr.Markdown("### 参数调节")
+            temperature_slider = gr.Slider(
+                minimum=0.1,
+                maximum=2.0,
+                value=GEN_TEMP,
+                step=0.1,
+                label="Temperature"
+            )
+            top_p_slider = gr.Slider(
+                minimum=0.0,
+                maximum=2.0,
+                value=GEN_TOP_P,
+                step=0.05,
+                label="Top-P"
+            )
+    
+    
+    def respond(message, chat_history, temperature, top_p):
+        if not message:
+            return "", chat_history
+        
+        chat_history.append((message, ""))
+        
+        response = ""
+        for partial_response in chat_with_bot(message, chat_history[:-1], temperature, top_p):
+            response = partial_response
+            cleaned_response = response.replace("<|im_end|>", "")
+            chat_history[-1] = (message, cleaned_response)
+            yield "", chat_history
+    
+    def clear_history():
+        global model_tokens, model_state
+        model_tokens = []
+        model_state = None
+        return []
+    
+    msg.submit(respond, [msg, chatbot, temperature_slider, top_p_slider], [msg, chatbot])
+    send_btn.click(respond, [msg, chatbot, temperature_slider, top_p_slider], [msg, chatbot])
+    clear_btn.click(clear_history, None, chatbot)
+
+if __name__ == "__main__":
+    demo.launch(server_name="127.0.0.1", server_port=7860)

requirements.txt

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+torch --index-url https://download.pytorch.org/whl/cpu
+rwkv
+transformers

rwkv-final-sft-2048.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:09459cc9b8cf413e71ab867d7be5673f4d5b554d8fb87cf8669e4aa34599152f
+size 68354364