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Model: DAMO-NLP-MT/polylm-multialpaca-13b
Source: Original Platform

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README.md

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+---
+license: apache-2.0
+---
+
+# Model Card for PolyLM-Multialpaca
+
+This model is finetuned on [polyLM-13b](https://huggingface.co/DAMO-NLP-MT/polylm-13b) using [multialpaca](https://huggingface.co/datasets/DAMO-NLP-MT/multialpaca) (a self-instruction dataset)
+
+# Demo
+[Open](https://modelscope.cn/studios/damo/demo-polylm-multialpaca-13b/summary)
+
+# Bias, Risks, and Limitations
+
+The information below in this section are copied from the model's [official model card](https://arxiv.org/pdf/2307.06018.pdf):
+
+> Our contributions are fully methodological: adding the support of multilingualism to LLM during training and SFT phases. It is unavoidable that PolyLM might exhibit several common deficiencies of language models, e.g. hallucination and toxicity. PolyLM should not be used directly in any application, without a prior assessment of safety and fairness concerns specific to the application.
+
+> This version activates the instruction-following capability of PolyLM through self-instruction, but currently, the training instructions are relatively simple and the support for abilities such as multi-turn dialogue, context understanding, CoT, Plugin, etc. is not very friendly. We are making efforts to develop a new version.
+
+# Citation
+
+**BibTeX:**
+
+```bibtex
+@misc{wei2023polylm,
+    title={PolyLM: An Open Source Polyglot Large Language Model},
+    author={Xiangpeng Wei and Haoran Wei and Huan Lin and Tianhao Li and Pei Zhang and Xingzhang Ren and Mei Li and Yu Wan and Zhiwei Cao and Binbin Xie and Tianxiang Hu and Shangjie Li and Binyuan Hui and Bowen Yu and Dayiheng Liu and Baosong Yang and Fei Huang and Jun Xie},
+    year={2023},
+    eprint={2307.06018},
+    archivePrefix={arXiv},
+    primaryClass={cs.CL}
+}
+```

config.json

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+{
+  "activation_function": "gelu_fast",
+  "architectures": [
+    "GPT2LMHeadModel"
+  ],
+  "attn_pdrop": 0.0,
+  "bos_token_id": 255999,
+  "embd_pdrop": 0.0,
+  "eos_token_id": 255999,
+  "initializer_range": 0.02,
+  "layer_norm_epsilon": 1e-05,
+  "model_type": "gpt2",
+  "n_embd": 5120,
+  "n_head": 40,
+  "n_inner": 20480,
+  "n_layer": 40,
+  "n_positions": 2048,
+  "reorder_and_upcast_attn": false,
+  "resid_pdrop": 0.0,
+  "scale_attn_by_inverse_layer_idx": false,
+  "scale_attn_weights": true,
+  "summary_activation": null,
+  "summary_first_dropout": 0.0,
+  "summary_proj_to_labels": true,
+  "summary_type": "cls_index",
+  "summary_use_proj": true,
+  "tokenizer_class": "AutoTokenizer",
+  "transformers_version": "4.29.2",
+  "use_cache": true,
+  "vocab_size": 256000
+}

polylm_cli_demo.py

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+import argparse
+import os
+import platform
+import warnings
+
+import re
+pattern = re.compile("[\n]+")
+
+import torch
+from accelerate import init_empty_weights, load_checkpoint_and_dispatch
+from huggingface_hub import snapshot_download
+
+from transformers.generation.utils import logger
+from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer
+
+parser = argparse.ArgumentParser()
+parser.add_argument("--model_name", default="DAMO-NLP-MT/polylm-multialpaca-13b", 
+                    choices=["DAMO-NLP-MT/polylm-multialpaca-13b"], type=str)
+parser.add_argument("--multi_round", action="store_true",
+                    help="Turn multiple rounds interaction on.")
+parser.add_argument("--gpu", default="0", type=str)
+args = parser.parse_args()
+
+os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu
+num_gpus = len(args.gpu.split(","))
+
+if args.model_name in ["DAMO-NLP-MT/polylm-multialpaca-13b-int8", "DAMO-NLP-MT/polylm-multialpaca-13b-int4"] and num_gpus > 1:
+    raise ValueError("Quantized models do not support model parallel. Please run on a single GPU (e.g., --gpu 0).")
+
+logger.setLevel("ERROR")
+warnings.filterwarnings("ignore")
+
+model_path = args.model_name
+if not os.path.exists(args.model_name):
+    model_path = snapshot_download(args.model_name)
+
+config = AutoConfig.from_pretrained(model_path)
+tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False)
+
+if num_gpus > 1:  
+    print("Waiting for all devices to be ready, it may take a few minutes...")
+    with init_empty_weights():
+        raw_model = AutoModelForCausalLM.from_config(config)
+    raw_model.tie_weights()
+    model = load_checkpoint_and_dispatch(
+        raw_model, model_path, device_map="auto", no_split_module_classes=["GPT2Block"]
+    )
+else:
+    print("Loading model files, it may take a few minutes...")
+    model = AutoModelForCausalLM.from_pretrained(model_path, device_map="auto").cuda()
+
+def clear():
+    os.system('cls' if platform.system() == 'Windows' else 'clear')
+    
+def main():
+    print("欢迎使用 PolyLM 多语言人工智能助手！输入内容即可进行对话。输入 clear 以清空对话历史，输入 stop 以终止对话。")
+    prompt = ""
+    while True:
+        query = input()
+        if query.strip() == "stop":
+            break
+        if query.strip() == "clear":
+            if args.multi_round:
+                prompt = ""
+            clear()
+            continue
+        
+        text = query.strip()
+        text = re.sub(pattern, "\n", text)
+        if args.multi_round:
+            prompt += f"{text}\n\n"
+        else:
+            prompt = f"{text}\n\n"
+        
+        inputs = tokenizer(prompt, return_tensors="pt")
+        with torch.no_grad():
+            outputs = model.generate(
+                inputs.input_ids.cuda(), 
+                attention_mask=inputs.attention_mask.cuda(), 
+                max_length=1024, 
+                do_sample=True, 
+                top_p=0.8, 
+                temperature=0.7,
+                repetition_penalty=1.02,
+                num_return_sequences=1, 
+                eos_token_id=2,
+                early_stopping=True)
+            response = tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True)
+            if args.multi_round:
+                prompt += f"{response}\n"
+            print(f">>> {response}")
+    
+if __name__ == "__main__":
+    main()

polylm_web_demo_gradio.py

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+import argparse
+import os
+import warnings
+import mdtex2html
+import gradio as gr
+
+import re
+pattern = re.compile("[\n]+")
+
+import torch
+from accelerate import init_empty_weights, load_checkpoint_and_dispatch
+from huggingface_hub import snapshot_download
+
+from transformers.generation.utils import logger
+from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer
+
+parser = argparse.ArgumentParser()
+parser.add_argument("--model_name", default="DAMO-NLP-MT/polylm-multialpaca-13b", 
+                    choices=["DAMO-NLP-MT/polylm-multialpaca-13b"], type=str)
+parser.add_argument("--gpu", default="0", type=str)
+args = parser.parse_args()
+
+os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu
+num_gpus = len(args.gpu.split(","))
+
+if ('int8' in args.model_name or 'int4' in args.model_name) and num_gpus > 1:
+    raise ValueError("Quantized models do not support model parallel. Please run on a single GPU (e.g., --gpu 0).")
+
+logger.setLevel("ERROR")
+warnings.filterwarnings("ignore")
+
+model_path = args.model_name
+if not os.path.exists(args.model_name):
+    model_path = snapshot_download(args.model_name)
+
+config = AutoConfig.from_pretrained(model_path)
+tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False)
+
+if num_gpus > 1:
+    print("Waiting for all devices to be ready, it may take a few minutes...")
+    with init_empty_weights():
+        raw_model = AutoModelForCausalLM.from_config(config)
+    raw_model.tie_weights()
+    model = load_checkpoint_and_dispatch(
+        raw_model, model_path, device_map="auto", no_split_module_classes=["GPT2Block"]
+    )
+else:
+    print("Loading model files, it may take a few minutes...")
+    model = AutoModelForCausalLM.from_pretrained(model_path, trust_remote_code=True).cuda()
+
+
+def postprocess(self, y):
+    if y is None:
+        return []
+    for i, (message, response) in enumerate(y):
+        y[i] = (
+            None if message is None else mdtex2html.convert((message)),
+            None if response is None else mdtex2html.convert(response),
+        )
+    return y
+
+
+gr.Chatbot.postprocess = postprocess
+
+
+def parse_text(text):
+    """copy from https://github.com/GaiZhenbiao/ChuanhuChatGPT/"""
+    lines = text.split("\n")
+    lines = [line for line in lines if line != ""]
+    count = 0
+    for i, line in enumerate(lines):
+        if "```" in line:
+            count += 1
+            items = line.split('`')
+            if count % 2 == 1:
+                lines[i] = f'<pre><code class="language-{items[-1]}">'
+            else:
+                lines[i] = f'<br></code></pre>'
+        else:
+            if i > 0:
+                if count % 2 == 1:
+                    line = line.replace("`", "\`")
+                    line = line.replace("<", "&lt;")
+                    line = line.replace(">", "&gt;")
+                    line = line.replace(" ", "&nbsp;")
+                    line = line.replace("*", "&ast;")
+                    line = line.replace("_", "&lowbar;")
+                    line = line.replace("-", "&#45;")
+                    line = line.replace(".", "&#46;")
+                    line = line.replace("!", "&#33;")
+                    line = line.replace("(", "&#40;")
+                    line = line.replace(")", "&#41;")
+                    line = line.replace("$", "&#36;")
+                lines[i] = "<br>"+line
+    text = "".join(lines)
+    return text
+
+
+def predict(input, chatbot, max_length, top_p, temperature, history):
+    query = input
+    query = query.strip()
+    query = re.sub(pattern, "\n", query)
+
+    chatbot.append((query, ""))
+    prompt = ""
+    for i, (old_query, response) in enumerate(history):
+        prompt += f"{old_query}\n\n" + f"{response}\n"
+    prompt += f"{query}\n\n"
+    inputs = tokenizer(prompt, return_tensors="pt")
+    with torch.no_grad():
+        outputs = model.generate(
+            inputs.input_ids.cuda(),
+            attention_mask=inputs.attention_mask.cuda(),
+            max_length=max_length,
+            do_sample=True,
+            top_p=top_p,
+            temperature=temperature,
+            repetition_penalty=1.02,
+            num_return_sequences=1,
+            eos_token_id=2,
+            early_stopping=True)
+        response = tokenizer.decode(
+            outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True)
+
+    chatbot[-1] = (query, parse_text(response))
+    history = history + [(query, response)]
+    print("==========================================================================")
+    print(f"chatbot is {chatbot}")
+    print(f"history is {history}")
+    print("==========================================================================")
+    return chatbot, history
+
+
+def reset_user_input():
+    return gr.update(value='')
+
+
+def reset_state():
+    return [], []
+
+
+with gr.Blocks() as demo:
+    gr.HTML("""<h1 align="center">欢迎使用 PolyLM 多语言人工智能助手！</h1>""")
+
+    chatbot = gr.Chatbot()
+    with gr.Row():
+        with gr.Column(scale=4):
+            with gr.Column(scale=12):
+                user_input = gr.Textbox(show_label=False, placeholder="Input...", lines=10).style(
+                    container=False)
+            with gr.Column(min_width=32, scale=1):
+                submitBtn = gr.Button("Submit", variant="primary")
+        with gr.Column(scale=1):
+            emptyBtn = gr.Button("Clear History")
+            max_length = gr.Slider(
+                0, 4096, value=2048, step=1.0, label="Maximum length", interactive=True)
+            top_p = gr.Slider(0, 1, value=0.8, step=0.01,
+                              label="Top P", interactive=True)
+            temperature = gr.Slider(
+                0, 1, value=0.7, step=0.01, label="Temperature", interactive=True)
+
+    history = gr.State([])  # (message, bot_message)
+
+    submitBtn.click(predict, [user_input, chatbot, max_length, top_p, temperature, history], [chatbot, history],
+                    show_progress=True)
+    submitBtn.click(reset_user_input, [], [user_input])
+
+    emptyBtn.click(reset_state, outputs=[chatbot, history], show_progress=True)
+
+demo.queue().launch(share=False, inbrowser=True)

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