1.huggingface博客讲解RLHF: Illustrating Reinforcement Learning from Human Feedback (RLHF) 2.RRHF代码实现:RRHF: Rank Responses to Align Language Models with Human Feedback without tears 3.RLTF代码实现:RLTF: Reinforcemen
docker run --gpus all --shm-size 1g -p 8080:80 -v $volume:/data \ ghcr.io/huggingface/text-generation-inference:3.1.0 --model-id deepseek-ai/DeepSeek-R1 What's Changed Attempt to remove AWS S3 flaky cache for sccache by@mfuntowiczin#2953 ...
Code Llama在HumanEval数据集上一次生成通过率(pass@1)为62.2%,大于GPT3.5,比GPT4差一点。 补充一下,WizardCoder在8月26日发布了最新版本,已经超过了GPT4,HumanEval pass@1 达到73.2,简直无敌!最新的开源大模型排行榜单,可以参考huggingface open LLM leaderboard补充...
HuggingFace 上的开源版本是一个在 40,000 小时数据上进行无监督微调的预训练模型。 3.4 ChatTTS 部署 3.4.1 创建conda环境 代码语言:javascript 代码运行次数:0 运行 AI代码解释 conda create-n chattts conda activate chattts 3.4.2 拉取源代码 代码语言:javascript ...
By trying out several models at once, I can see if any of them are setup correctly to work. Here is my test code: from huggingface_hub import InferenceClient import requests from pathlib import Path # Load Hugging Face API secret - Put the secret in a text file and read it hf_secret...
To see all options to serve your models (in thecodeor in the cli): text-generation-launcher --help API documentation You can consult the OpenAPI documentation of thetext-generation-inferenceREST API using the/docsroute. The Swagger UI is also available at:https://huggingface.github.io/text-...
Hugging face格式模型 https://huggingface.co/codellama 选择Base Model 然后依次下载下方红框中的文件 下载好后,然后选中这九个文件剪切,回到text-generation-webui目录中,进入models目录中,新建要给文件夹,名为codellama-7b 把刚才9个文件,都粘贴到这个新文件夹中 ...
Here’s a glimpse of the C# code snippet required to kickstart the integration: Copy // Initializes the Kernelvarkernel = Kernel .CreateBuilder() .AddHuggingFaceImageToText("Salesforce/blip-image-captioning-base") .Build();// Gets the ImageToText Servicevarservice =this._ker...
Huggingface's transformers library is a great resource for natural language processing tasks, and it includes an implementation of OpenAI's CLIP model including a pretrained model clip-vit-large-patch14. The CLIP model is a powerful image and text embedding model that can ...
huggingface/transformersには、このためのメソッド(convert_ids_to_tokens)があらかじめ用意されています。 code2 tokenized_text = tokenizer.convert_ids_to_tokens(model_inputs['input_ids'][0]) result2 ['[CLS]', '吾', '輩', 'は', '猫', 'で', '##ある', '。', '名', '前'...