svjack/DPO_Bactrian_X_ZH_RJ_EN_ORPO_Mistral7B_v2_inst_lora_small

Model Card for Model ID

Model Details

Model Description

Install

pip install peft transformers bitsandbytes

Run by transformers

from transformers import TextStreamer, AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel
tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.2",)
mis_model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct-v0.2", load_in_4bit = True)
mis_model = PeftModel.from_pretrained(mis_model, "svjack/DPO_Bactrian_X_ZH_RJ_EN_ORPO_Mistral7B_v2_inst_lora_small")
mis_model = mis_model.eval()

streamer = TextStreamer(tokenizer)

def mistral_hf_predict(messages, mis_model = mis_model,
    tokenizer = tokenizer, streamer = streamer,
    do_sample = True,
    top_p = 0.95,
    top_k = 40,
    max_new_tokens = 512,
    max_input_length = 3500,
    temperature = 0.9,
    repetition_penalty = 1.0,
    device = "cuda"):

    encodeds = tokenizer.apply_chat_template(messages, return_tensors="pt")
    model_inputs = encodeds.to(device)

    generated_ids = mis_model.generate(model_inputs, max_new_tokens=max_new_tokens,
                                do_sample=do_sample,
                                  streamer = streamer,
                                  top_p = top_p,
                                  top_k = top_k,
                                  temperature = temperature,
                                  repetition_penalty = repetition_penalty,
                                  )
    out = tokenizer.batch_decode(generated_ids)[0].split("[/INST]")[-1].replace("</s>", "").strip()
    return out

out = mistral_hf_predict([
            {
                "role": "user",
                "content": "不使用sorted，写一份简单的Python排序程序，使用markdown进行输出。"
            }
        ],
        repetition_penalty = 1.0,
        temperature = 0.01,
        max_new_tokens=1024
)
print(out)

Output

def sort_list(lst):
    for i in range(len(lst)):
        for j in range(i+1, len(lst)):
            if lst[i] > lst[j]:
                lst[i], lst[j] = lst[j], lst[i]

# 输入一个列表
lst = [5, 3, 8, 1, 6, 2, 7, 4]

# 调用sort_list函数进行排序
sort_list(lst)

# 输出排序后的列表
print("排序后的列表：")
print(lst)

输出：

排序后的列表：
[1, 2, 3, 4, 5, 6, 7, 8]

out = mistral_hf_predict([
            {
                "role": "user",
                "content": "不使用sorted，写一份简单的Python排序程序，使用markdown进行输出。"
            },
            {   "role": "assistant",
                "content": '''
                ```python
                def sort_list(lst):
                    for i in range(len(lst)):
                        for j in range(i+1, len(lst)):
                            if lst[i] > lst[j]:
                                lst[i], lst[j] = lst[j], lst[i]

                # 输入一个列表
                lst = [5, 3, 8, 1, 6, 2, 7, 4]

                # 调用sort_list函数进行排序
                sort_list(lst)

                # 输出排序后的列表
                print("排序后的列表：")
                print(lst)
                ```

                输出：

                ```
                排序后的列表：
                [1, 2, 3, 4, 5, 6, 7, 8]
                ```
                '''
            },
            {
                "role": "user",
                "content": "将上面的语言翻译成C++。"
            }
        ],
        repetition_penalty = 1.0,
        temperature = 0.01,
        max_new_tokens=1024
)
print(out)

Output

#include <iostream>
#include <algorithm>

using namespace std;

void sortList(int arr[], int n) {
    for (int i = 0; i < n; i++) {
        for (int j = i + 1; j < n; j++) {
            if (arr[i] > arr[j]) {
                swap(arr[i], arr[j]);
            }
        }
    }
}

int main() {
    int arr[] = {5, 3, 8, 1, 6, 2, 7, 4};
    int n = sizeof(arr) / sizeof(arr[0]);

    sortList(arr, n);

    cout << "Sorted array: ";
    for (int i = 0; i < n; i++) {
        cout << arr[i] << " ";
    }
    cout << endl;

    return 0;
}

输出：

Sorted array: 1 2 3 4 5 6 7 8

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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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Framework versions

• PEFT 0.11.0