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import gradio as gr |
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import os, gc, copy, torch |
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from datetime import datetime |
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from huggingface_hub import hf_hub_download |
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from pynvml import * |
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nvmlInit() |
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gpu_h = nvmlDeviceGetHandleByIndex(0) |
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ctx_limit = 1536 |
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title = "RWKV-4-Raven-14B-v12-Eng98%-Other2%-20230523-ctx8192" |
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os.environ["RWKV_JIT_ON"] = '1' |
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os.environ["RWKV_CUDA_ON"] = '1' |
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from rwkv.model import RWKV |
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model_path = hf_hub_download(repo_id="BlinkDL/rwkv-4-raven", filename=f"{title}.pth") |
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model = RWKV(model=model_path, strategy='cuda fp16i8 *24 -> cuda fp16') |
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from rwkv.utils import PIPELINE, PIPELINE_ARGS |
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pipeline = PIPELINE(model, "20B_tokenizer.json") |
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def generate_prompt(instruction, input=None): |
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instruction = instruction.strip().replace('\r\n','\n').replace('\n\n','\n') |
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input = input.strip().replace('\r\n','\n').replace('\n\n','\n') |
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if input: |
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return f"""Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request. |
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# Instruction: |
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{instruction} |
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# Input: |
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{input} |
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# Response: |
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""" |
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else: |
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return f"""Below is an instruction that describes a task. Write a response that appropriately completes the request. |
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# Instruction: |
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{instruction} |
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# Response: |
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""" |
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def evaluate( |
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instruction, |
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input=None, |
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token_count=200, |
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temperature=1.0, |
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top_p=0.7, |
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presencePenalty = 0.1, |
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countPenalty = 0.1, |
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): |
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args = PIPELINE_ARGS(temperature = max(0.2, float(temperature)), top_p = float(top_p), |
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alpha_frequency = countPenalty, |
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alpha_presence = presencePenalty, |
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token_ban = [], |
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token_stop = [0]) |
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instruction = instruction.strip().replace('\r\n','\n').replace('\n\n','\n') |
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input = input.strip().replace('\r\n','\n').replace('\n\n','\n') |
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ctx = generate_prompt(instruction, input) |
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all_tokens = [] |
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out_last = 0 |
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out_str = '' |
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occurrence = {} |
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state = None |
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for i in range(int(token_count)): |
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out, state = model.forward(pipeline.encode(ctx)[-ctx_limit:] if i == 0 else [token], state) |
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for n in occurrence: |
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out[n] -= (args.alpha_presence + occurrence[n] * args.alpha_frequency) |
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token = pipeline.sample_logits(out, temperature=args.temperature, top_p=args.top_p) |
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if token in args.token_stop: |
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break |
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all_tokens += [token] |
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if token not in occurrence: |
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occurrence[token] = 1 |
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else: |
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occurrence[token] += 1 |
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tmp = pipeline.decode(all_tokens[out_last:]) |
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if '\ufffd' not in tmp: |
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out_str += tmp |
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yield out_str.strip() |
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out_last = i + 1 |
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gpu_info = nvmlDeviceGetMemoryInfo(gpu_h) |
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print(f'vram {gpu_info.total} used {gpu_info.used} free {gpu_info.free}') |
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del out |
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del state |
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gc.collect() |
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torch.cuda.empty_cache() |
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yield out_str.strip() |
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examples = [ |
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["Tell me about ravens.", "", 300, 1.2, 0.5, 0.4, 0.4], |
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["Write a python function to mine 1 BTC, with details and comments.", "", 300, 1.2, 0.5, 0.4, 0.4], |
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["Write a song about ravens.", "", 300, 1.2, 0.5, 0.4, 0.4], |
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["Explain the following metaphor: Life is like cats.", "", 300, 1.2, 0.5, 0.4, 0.4], |
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["Write a story using the following information", "A man named Alex chops a tree down", 300, 1.2, 0.5, 0.4, 0.4], |
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["Generate a list of adjectives that describe a person as brave.", "", 300, 1.2, 0.5, 0.4, 0.4], |
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["You have $100, and your goal is to turn that into as much money as possible with AI and Machine Learning. Please respond with detailed plan.", "", 300, 1.2, 0.5, 0.4, 0.4], |
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] |
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chat_intro = '''The following is a coherent verbose detailed conversation between <|user|> and an AI girl named <|bot|>. |
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<|user|>: Hi <|bot|>, Would you like to chat with me for a while? |
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<|bot|>: Hi <|user|>. Sure. What would you like to talk about? I'm listening. |
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''' |
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def user(message, chatbot): |
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chatbot = chatbot or [] |
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return "", chatbot + [[message, None]] |
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def alternative(chatbot, history): |
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if not chatbot or not history: |
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return chatbot, history |
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chatbot[-1][1] = None |
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history[0] = copy.deepcopy(history[1]) |
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return chatbot, history |
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def chat( |
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prompt, |
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user, |
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bot, |
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chatbot, |
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history, |
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temperature=1.0, |
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top_p=0.8, |
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presence_penalty=0.1, |
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count_penalty=0.1, |
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): |
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args = PIPELINE_ARGS(temperature=max(0.2, float(temperature)), top_p=float(top_p), |
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alpha_frequency=float(count_penalty), |
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alpha_presence=float(presence_penalty), |
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token_ban=[], |
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token_stop=[]) |
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if not chatbot: |
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return chatbot, history |
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message = chatbot[-1][0] |
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message = message.strip().replace('\r\n','\n').replace('\n\n','\n') |
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ctx = f"{user}: {message}\n\n{bot}:" |
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if not history: |
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prompt = prompt.replace("<|user|>", user.strip()) |
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prompt = prompt.replace("<|bot|>", bot.strip()) |
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prompt = prompt.strip() |
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prompt = f"\n{prompt}\n\n" |
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out, state = model.forward(pipeline.encode(prompt), None) |
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history = [state, None, []] |
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[state, _, all_tokens] = history |
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state_pre_0 = copy.deepcopy(state) |
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out, state = model.forward(pipeline.encode(ctx)[-ctx_limit:], state) |
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state_pre_1 = copy.deepcopy(state) |
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begin = len(all_tokens) |
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out_last = begin |
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out_str: str = '' |
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occurrence = {} |
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for i in range(300): |
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if i <= 0: |
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nl_bias = -float('inf') |
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elif i <= 30: |
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nl_bias = (i - 30) * 0.1 |
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elif i <= 130: |
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nl_bias = 0 |
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else: |
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nl_bias = (i - 130) * 0.25 |
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out[187] += nl_bias |
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for n in occurrence: |
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out[n] -= (args.alpha_presence + occurrence[n] * args.alpha_frequency) |
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token = pipeline.sample_logits(out, temperature=args.temperature, top_p=args.top_p) |
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next_tokens = [token] |
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if token == 0: |
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next_tokens = pipeline.encode('\n\n') |
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all_tokens += next_tokens |
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if token not in occurrence: |
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occurrence[token] = 1 |
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else: |
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occurrence[token] += 1 |
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out, state = model.forward(next_tokens, state) |
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tmp = pipeline.decode(all_tokens[out_last:]) |
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if '\ufffd' not in tmp: |
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out_last = begin + i + 1 |
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out_str += tmp |
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chatbot[-1][1] = out_str.strip() |
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history = [state, all_tokens] |
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yield chatbot, history |
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out_str = pipeline.decode(all_tokens[begin:]) |
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out_str = out_str.replace("\r\n", '\n').replace('\\n', '\n') |
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if '\n\n' in out_str: |
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break |
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if f'{user}:' in out_str or f'{bot}:' in out_str: |
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idx_user = out_str.find(f'{user}:') |
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idx_user = len(out_str) if idx_user == -1 else idx_user |
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idx_bot = out_str.find(f'{bot}:') |
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idx_bot = len(out_str) if idx_bot == -1 else idx_bot |
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idx = min(idx_user, idx_bot) |
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if idx < len(out_str): |
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out_str = f" {out_str[:idx].strip()}\n\n" |
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tokens = pipeline.encode(out_str) |
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all_tokens = all_tokens[:begin] + tokens |
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out, state = model.forward(tokens, state_pre_1) |
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break |
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gpu_info = nvmlDeviceGetMemoryInfo(gpu_h) |
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print(f'vram {gpu_info.total} used {gpu_info.used} free {gpu_info.free}') |
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gc.collect() |
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torch.cuda.empty_cache() |
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chatbot[-1][1] = out_str.strip() |
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history = [state, state_pre_0, all_tokens] |
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yield chatbot, history |
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with gr.Blocks(title=title) as demo: |
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gr.HTML(f"<div style=\"text-align: center;\">\n<h1>🐦Raven - {title}</h1>\n</div>") |
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with gr.Tab("Instruct mode"): |
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gr.Markdown(f"Raven is [RWKV 14B](https://github.com/BlinkDL/ChatRWKV) 100% RNN [RWKV-LM](https://github.com/BlinkDL/RWKV-LM) finetuned to follow instructions. *** Please try examples first (bottom of page) *** (edit them to use your question). Demo limited to ctxlen {ctx_limit}. Finetuned on alpaca, gpt4all, codealpaca and more. For best results, *** keep you prompt short and clear ***. <b>UPDATE: now with Chat (see above, as a tab) ==> turn off as of now due to VRAM leak caused by buggy code.</b>.") |
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with gr.Row(): |
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with gr.Column(): |
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instruction = gr.Textbox(lines=2, label="Instruction", value="Tell me about ravens.") |
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input = gr.Textbox(lines=2, label="Input", placeholder="none") |
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token_count = gr.Slider(10, 300, label="Max Tokens", step=10, value=300) |
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temperature = gr.Slider(0.2, 2.0, label="Temperature", step=0.1, value=1.2) |
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top_p = gr.Slider(0.0, 1.0, label="Top P", step=0.05, value=0.5) |
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presence_penalty = gr.Slider(0.0, 1.0, label="Presence Penalty", step=0.1, value=0.4) |
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count_penalty = gr.Slider(0.0, 1.0, label="Count Penalty", step=0.1, value=0.4) |
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with gr.Column(): |
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with gr.Row(): |
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submit = gr.Button("Submit", variant="primary") |
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clear = gr.Button("Clear", variant="secondary") |
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output = gr.Textbox(label="Output", lines=5) |
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data = gr.Dataset(components=[instruction, input, token_count, temperature, top_p, presence_penalty, count_penalty], samples=examples, label="Example Instructions", headers=["Instruction", "Input", "Max Tokens", "Temperature", "Top P", "Presence Penalty", "Count Penalty"]) |
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submit.click(evaluate, [instruction, input, token_count, temperature, top_p, presence_penalty, count_penalty], [output]) |
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clear.click(lambda: None, [], [output]) |
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data.click(lambda x: x, [data], [instruction, input, token_count, temperature, top_p, presence_penalty, count_penalty]) |
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demo.queue(concurrency_count=1, max_size=10) |
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demo.launch(share=False) |
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