HuggingFace镜像/Faro-Yi-9B
模型介绍文件和版本分析
下载使用量0

Faro 聊天模型注重实用性和长上下文建模能力。它能以更高质量处理各类下游任务,即便输入包含冗长文档或复杂指令,也能输出稳定可靠的结果。Faro 可流畅支持中英文双语。

Faro-Yi-9B

Faro-Yi-9B 是在 Yi-9B-200K 基础上进行改进的模型,在 Fusang-V1 上进行了广泛的指令微调。与 Yi-9B-200K 相比,借助 Fusang-V1 中的大规模合成数据,Faro-Yi-9B 在各类下游任务和长上下文建模方面的能力得到了显著提升。

与 Yi-9B-200K 一样,Faro-Yi-9B 支持最长 200K 的上下文长度。

在 openMind 中使用

Faro-Yi-9B 采用 chatml 模板,在短上下文和长上下文场景下均表现出色。

环境变量

# source environment variable
source /usr/local/Ascend/ascend-toolkit/set_env.sh
export OPENMIND_FRAMEWORK=pt

推理

from openmind import AutoModelForCausalLM, AutoTokenizer
from openmind_hub import snapshot_download
import argparse

def parse_args():
    parser = argparse.ArgumentParser()
    parser.add_argument(
        "--model_name_or_path",
        type=str,
        help="Jinan_AICC/Faro-Yi-9B",
        default=None,
    )
    args = parser.parse_args()
    return args

args = parse_args()
model_path = args.model_name_or_path
model = AutoModelForCausalLM.from_pretrained(model_path, device_map="auto")
tokenizer = AutoTokenizer.from_pretrained(model_path)
messages = [
    {"role": "system", "content": "You are a helpful assistant. Always answer with a short response."},
    {"role": "user", "content": "Tell me what is Pythagorean theorem like you are a pirate."}
]

input_ids = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt").to(model.device)
generated_ids = model.generate(input_ids, max_new_tokens=512, temperature=0.5)
response = tokenizer.decode(generated_ids[0], skip_special_tokens=True) # Aye, matey! The Pythagorean theorem is a nautical rule that helps us find the length of the third side of a triangle. ...
print(response)

性能表现

Faro-Yi-9B 在多数维度上相比 Yi-9B-200K 均有能力提升,尤其在长文本建模和双语(英文、中文)理解方面表现突出。在参数规模约为 90 亿的所有开源模型中,Faro 具备较强的竞争力。

基准测试结果

事实性评估(Open LLM Leaderboard)

指标MMLUGSM8KHellaSwagTruthfulQAArcWinogrande
Yi-9B-200K65.7350.4956.7233.8069.2571.67
Faro-Yi-9B68.8063.0857.2840.8672.5871.11

长文本建模(LongBench)

名称Average_zhAverage_enCode Completion
Yi-9B-200K30.28836.707172.2
Faro-Yi-9B41.09240.953646.0
分数细分
名称Few-shot Learning_enSynthetic Tasks_enSingle-Doc QA_enMulti-Doc QA_enSummarization_enFew-shot Learning_zhSynthetic Tasks_zhSingle-Doc QA_zhMulti-Doc QA_zhSummarization_zh
Yi-9B-200K60.622.830.938.925.846.528.049.617.79.7
Faro-Yi-9B63.840.236.238.026.330.075.155.630.714.1

双语能力(CMMLU & MMLU)

名称MMLUCMMLU
Yi-9B-200K65.7371.97
Faro-Yi-9B68.8073.28