为了解决这个问题,Hugging Face 发布了text-generation-inference(TGI),这是一个开源的大语言模型部署解决方案,它使用了 Rust、Python 和 gRPC。TGI 被整合到了 Hugging Face 的推理解决方案中,包括Inference Endpoints和Inference API,所以你能通过简单几次点击创建优化过的服务接入点,或是向 Hugging Face 的推理 API...
sudo docker run -e HF_HUB_ENABLE_HF_TRANSFER=False --gpus all --shm-size 1g -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:latest --model-id $model --num-shard $num_shard --quantize bitsandbytes 2023-05-22T16:02:24.868158Z INFO text_generation_launcher: ...
containers: - name: mixtral-8x7b image: ghcr.io/huggingface/text-generation-inference:1.3.4 resources: limits: nvidia.com/gpu: 1 ports: - name: server-port containerPort: 8080 env: - name: MODEL_ID-- value: mistralai/Mistral-7B-Instruct-v0.1+- value: mistralai/mixtral-8x7b-Instruct-...
你还可以使用 Docker 在 2 张 A100 (80GB) 上本地运行 TGI,如下所示: docker run --gpus all --shm-size 1g -p 3000:80 -v /data:/data ghcr.io/huggingface/text-generation-inference:1.3.0 \ --model-id mistralai/Mixtral-8x7B-Instruct-v0.1 \ --num-shard 2 \ --max-batch-total-tokens...
TranslationPipeline."text2text-generation": will return a Text2TextGenerationPipeline."text-generation...
Build the Docker image. Upload image to OCI Container Registry (OCIR). To get started, you can use the following example image: ‘iad.ocir.io/idqr4wptq3qu/llm-blog/model-downloader:v1’ Inference client container Huggingface-hub is a Python package that features the InferenceClient class, ...
api_inference_community status code: input parsing, no exception (#460) 2个月前 docker_images Diffusers, txt2img and img2img, make sure guidance scale defaults to 0… 1个月前 scripts Resolve numerous typos (#224) 2年前 tests Fix status code: split pipeline load from input parsing (#459...
用的是随便找的nvidia nemo的一个docker。 https://github.com/NVIDIA/NeMo/blob/main/Dockerfile 我的目前的环境设置: root@a7034605291e:/workspace/asr/peft/examples/causal_language_modeling# pip list|grep transformer sentence-transformers 2.2.2 ...
inference_mode=False, r=8, lora_alpha=32, lora_dropout=0.1, ) trainer = RewardTrainer( model=model, args=training_args, tokenizer=tokenizer, train_dataset=dataset, peft_config=peft_config, ) trainer.train()RLHF微调(用于对齐) 在这一步中,我们将从第1步开始训练SFT模型,生成最大化奖励模型分数...
QDQBert (来自 NVIDIA) 伴随论文 Integer Quantization for Deep Learning Inference: Principles and Empirical Evaluation 由Hao Wu, Patrick Judd, Xiaojie Zhang, Mikhail Isaev and Paulius Micikevicius 发布。 RAG (来自 Facebook) 伴随论文 Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks 由Pa...