grub2-editenv - unset tuned_params tuned_initrd &>/dev/null || : # unpatch BLS entries MACHINE_ID=`cat /etc/machine-id 2>/dev/null` if [ "$MACHINE_ID" ] then for f in /boot/loader/entries/$MACHINE_ID-*.conf
(seq, seq_group.sampling_params) File "/usr/local/lib/python3.8/dist-packages/vllm/engine/llm_engine.py", line 631, in _decode_sequence read_offset) = detokenize_incrementally( File "/usr/local/lib/python3.8/dist-packages/vllm/transformers_utils/tokenizer.py", line 140, in detokenize_...
It would be great to see LangChain integrate with Standford's Alpaca 7B model, a fine-tuned LlaMa (see #1473). Standford created an AI able to generate outputs that were largely on par with OpenAI’s text-davinci-003 and regularly better ...
30,parameters=params__TMD,color=ColorRGB,0/255,79/255,121/255,legend=Tuned: Response with no tuned mass damper: > p2≔MagnitudePlotsys,range=5..30,parameters=params__noTMD,...
base_score = model.get_xgb_params()['base_score'] booster = model.get_booster() predicted_value = booster.predict(dmatrix) + float32(base_score) return predicted_value that said, this shouldn't be treated as a permanent fix -- it's simply a stopgap to buy us more time as we make...
trainerSaveLoadParams mobile_baidu intel-update-authors wangkuiyi-patch-2 fea/docker_cudnn7 helinwang-patch-1 shanyi15-patch-2 gh-pages optimizer tonyyang-svail-patch-1 fix_conll05_bug release/0.11.0 yu239-patch-1 emailweixu-patch-1 ...
{\n\"Content-Type\":\"application/json\",\n\"Authorization\":f\"Bearer{api_key}\"\n }\ndata = {\n\"input_data\": [input_data],\n\"params\": {\n\"temperature\":0.7,\n\"max_new_tokens\":128,\n\"do_sample\":True,\n\...
Compare Number of params2440M# 1020 Compare Domain GeneralizationImageNet-AModel soups (BASIC-L)Top-1 accuracy %94.17# 1 Compare Domain GeneralizationImageNet-AModel soups (ViT-G/14)Top-1 accuracy %92.67# 2 Compare Unsupervised Domain AdaptationImageNet-RModel soups (ViT-G/14)Top 1 Error4.54...
Training Data Params Context Length GQA Tokens LR Llama 1 See Touvron et al. (2023) 7B 2k ✗ 1.0T 3.0×10−43.0superscript1043.0\times 10^{-4} 13B 2k ✗ 1.0T 3.0×10−43.0superscript1043.0\times 10^{-4} 33B 2k ✗ 1.4T 1.5×10−41.5superscript1041.5\times 10^{-4} 65B ...
pip安装mistralinference 下载 从huggingface_hub导入snapshot_download 从路径库导入路径 mistral_models_path=path.home().joinpath(“管理模型”,“Nemo-v0.1”) mistral_models_path.mkdir(父项=True,存在项=True) snapshot_download(repo_id=“mistrali/Mistral-Nemo-Base-2407”,allow-patters=[“params.json...