harshvar.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json, re, requests, gzip\n",
    "from tqdm import tqdm\n",
    "from urlextract import URLExtract\n",
    "# import bibtexparser"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize URL extractor and DOI pattern\n",
    "url_extractor = URLExtract()\n",
    "doi_pattern = r'\\b(10\\.\\d{4,9}\\/[-._;()/:A-Z0-9]+)\\b'\n",
    "\n",
    "def get_urls(content):\n",
    "    \"\"\"Extract URLs from text.\"\"\"\n",
    "    return url_extractor.find_urls(content)\n",
    "\n",
    "def get_dois(content):\n",
    "    \"\"\"Extract DOIs based on regex.\"\"\"\n",
    "    return re.findall(doi_pattern, content)\n",
    "\n",
    "# def get_bibtex_entries(content):\n",
    "#     \"\"\"Extract Bibtex entries.\"\"\"\n",
    "#     try:\n",
    "#         bib_data = bibtexparser.loads(content)\n",
    "#         return bib_data.entries\n",
    "#     except:\n",
    "#         return []\n",
    "\n",
    "def fetch_readme(url, TIMEOUT = 10):\n",
    "    \"\"\"Fetch README content with timeout (default 10 secs) and fallback for GitHub.\"\"\"\n",
    "    try:\n",
    "        response = requests.get(url, timeout=TIMEOUT)\n",
    "        if response.status_code != 200 and 'github.com' in url:\n",
    "            # Try alternative URL for GitHub main branch\n",
    "            url = url.replace('/master/', '/main/')\n",
    "            response = requests.get(url, timeout=TIMEOUT)\n",
    "        return response.text if response.status_code == 200 else None\n",
    "    except requests.RequestException:\n",
    "        print(f\"Skipping {url} due to timeout or connection error.\")\n",
    "        return None\n",
    "\n",
    "def process_entry(id, source_type, base_url, readme_path):\n",
    "    \"\"\"Retrieve README, extract details, and organize data.\"\"\"\n",
    "    \n",
    "    if source_type == \"source\":\n",
    "        # Split the line to extract just the URL part\n",
    "        _, line = id.split(';')\n",
    "        # Construct the URL specifically for GitHub repos\n",
    "        full_url = f\"{base_url}{line[11:]}{readme_path}\"\n",
    "    else:\n",
    "        # Use the original method for model and data\n",
    "        full_url = f\"{base_url}{id}{readme_path}\"\n",
    "\n",
    "    \n",
    "    content = fetch_readme(full_url)\n",
    "    if content:\n",
    "        return {\n",
    "            \"id\": id,\n",
    "            \"type\": source_type,\n",
    "            \"url\": full_url,\n",
    "            \"content\": content.replace(\"\\n\", \" \"),\n",
    "            \"links\": get_urls(content),\n",
    "            \"dois\": get_dois(content)#,\n",
    "            # \"bibs\": get_bibtex_entries(content)\n",
    "        }\n",
    "    return None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processed bartowski/dolphin-2.9.3-Yi-1.5-34B-32k-GGUF from model.\n",
      "Processed mradermacher/Boundary-4x7b-MoE-i1-GGUF from model.\n",
      "Processed Weyaxi/Einstein-v4-7B from model.\n",
      "Processed EarthnDusk/May2023-Models from model.\n",
      "Processed Vedx04/MetaMath-Mistral-7B-hendrycks from model.\n",
      "Processed sudhanshu746/deepseek-7b-instruct-matho-finetune from model.\n",
      "Processed leonn71/gte-Qwen2-1.5B-instruct-Q6_K-GGUF from model.\n",
      "Processed AI4PD/ZymCTRL from model.\n",
      "Processed KappaNeuro/director-darren-aronofsky-style from model.\n",
      "Processed matheusgeda/testando from model.\n",
      "Processed Theta-Lev/deepseek-math-7b-rl-Q8_0-GGUF from model.\n",
      "Processed Zainiii/flanT5-738-lora-math from model.\n",
      "Processed reyvan/bert_large_maths from model.\n",
      "Processed jonas-luehrs/bert-base-cased-MLM-chemistry from model.\n",
      "Processed torchgeo/resnet18_sentinel2_all_moco from model.\n",
      "Processed Shaleen123/gemma2-9b-maths from model.\n",
      "Processed maldv/electric-sheep-7b-alpha from model.\n",
      "Processed TheBloke/OpenAssistant-Llama2-13B-Orca-8K-3319-GGUF from model.\n",
      "Processed multimolecule/splicebert-human.510nt from model.\n",
      "Processed TGrote11/Math_Classification from model.\n",
      "Processed tum-nlp/IDMGSP-Galactica-TRAIN_GPT3 from model.\n",
      "Processed scvi-tools/tabula-sapiens-spleen-scanvi from model.\n",
      "Processed aflah/Meta-Llama-3-8B-instruct__Climate-Science-Steps-60__Merge-to-4-bit from model.\n",
      "Processed LoneStriker/nontoxic-bagel-34b-v0.2-5.0bpw-h6-exl2 from model.\n",
      "Processed LoneStriker/SauerkrautLM-Mixtral-8x7B-Instruct-3.5bpw-h6-exl2 from model.\n",
      "Processed dotan1111/BioTokenizer-BFD-BPE-200 from model.\n",
      "Processed QuantFactory/internlm2-math-plus-1_8b-GGUF from model.\n",
      "Processed cloudqi/cqi_question_solver_translator_v0 from model.\n",
      "Processed arcdev/e5-mistral-7b-instruct from model.\n",
      "Processed 1aurent/densenet161.tiatoolbox-kather100k from model.\n",
      "Processed minhnguyen/Advanced_B-cell_Epitopes_Prediction from model.\n",
      "Processed AnandMK/ULFEarthquake from model.\n",
      "Processed paulml/ECE-ILAB-Q1 from model.\n",
      "Processed VickiPol/binary_models from model.\n",
      "Processed MaziyarPanahi/blossom-v3_1-mistral-7b-Mistral-7B-Instruct-v0.1-GGUF from model.\n",
      "Processed techysanoj/roberta-fine-tuned-squadv2 from model.\n",
      "Processed hw2942/bert-base-chinese-climate-related-prediction-v2 from model.\n",
      "Processed internlm/internlm2-math-plus-mixtral8x22b from model.\n",
      "Processed BVRA/tf_efficientnet_b0.in1k_ft_df20m_384 from model.\n",
      "Processed mevol/BiomedNLP-PubMedBERT-ProteinStructure-NER-v2.1 from model.\n",
      "Processed automerger/T3qM7-7B from model.\n",
      "Processed CMU-AIR2/math-deepseek-LORA-ArithHard-FTMWP-LORA from model.\n",
      "Processed yuiseki/tinyllama-coder-math-ja-wikipedia-v0.1 from model.\n",
      "Processed HuangLab/CELL-E_2_OpenCell_640 from model.\n",
      "Processed mlx-community/dolphin-2.9.1-yi-1.5-34b-8bit from model.\n",
      "Processed lamm-mit/BioinspiredLLM from model.\n",
      "Processed fine-tuned/BAAI_bge-large-en-v1_5-1562024-to89-webapp from model.\n",
      "Processed CMU-AIR2/math-phi-1-5-FULL-Arithmetic-lr-1.5e-6 from model.\n",
      "Processed multimolecule/rnamsm from model.\n",
      "Processed researchaiic/LandAUMath2 from model.\n",
      "Processed PDBEurope/BiomedNLP-PubMedBERT-ProteinStructure-NER-v1.4 from model.\n",
      "Processed BVRA/mobilenetv2_100.in1k_ft_df20_299 from model.\n",
      "Processed Kukedlc/NeuralMathsRolePlay-7b from model.\n",
      "Processed ostapeno/ng_1B_lib_embeddings_replace_distinct10 from model.\n",
      "Processed qilowoq/AbLang_heavy from model.\n",
      "Processed ForRiver/metamath-mistral-lora-7b from model.\n",
      "Processed rmuema/kaggle-x-elo-finetune from model.\n",
      "Processed mathrrab/q-taxi from model.\n",
      "Processed hw2942/chinese-macbert-base-climate-related-prediction-v3 from model.\n",
      "Processed imageomics/butterfly_detection_yolo from model.\n",
      "Processed MaziyarPanahi/jackalope-7b-Mistral-7B-Instruct-v0.1 from model.\n",
      "Processed zhangtaolab/plant-dnagemma-H3K27me3 from model.\n",
      "Processed SrimathiE21ALR044/AI_chatbot from model.\n",
      "Processed mradermacher/dolphin-2.9.1-llama-3-70b-GGUF from model.\n",
      "Processed marcelomathias/mistral_7b_lora_equus from model.\n",
      "Processed pszemraj/bigbird-pegasus-large-K-booksum from model.\n",
      "Processed stabilityai/stablelm-zephyr-3b from model.\n",
      "Processed premsuresh/bart-finetuned-mathqa-mohith from model.\n",
      "Processed severcorp/flant5 from model.\n",
      "Processed lamm-mit/Bioinspired-Llama-3-1-70B-128k-alpha from model.\n",
      "Processed Climate-TwitterBERT/Climate-TwitterBERT-xmas from model.\n",
      "Processed mlx-community/granite-34b-code-instruct-8bit from model.\n",
      "Processed TheBloke/tora-13B-v1.0-GPTQ from model.\n",
      "Processed vectoriseai/gte-base from model.\n",
      "Processed CMU-AIR2/deepseek-math-base-LORA-ArithSteps-8k from model.\n",
      "Processed SivilTaram/poet-math-digit from model.\n",
      "Processed open-llm-leaderboard-old/details_jambroz__sixtyoneeighty-7b from data.\n",
      "Processed open-llm-leaderboard-old/details_jeiku__Garbage_9B from data.\n",
      "Processed open-llm-leaderboard-old/details_OpenBuddy__openbuddy-mixtral-7bx8-v16.3-32k from data.\n",
      "Processed open-llm-leaderboard-old/details_l3utterfly__mistral-7b-v0.1-layla-v4 from data.\n",
      "Processed open-llm-leaderboard-old/details_cgato__TheSpice-7b-FT-ExperimentalOrca from data.\n",
      "Processed open-llm-leaderboard-old/details_Kquant03__CognitiveFusion-4x7B-bf16-MoE from data.\n",
      "Processed OALL/details_meta-llama__Llama-2-13b-hf from data.\n",
      "Processed open-llm-leaderboard-old/details_Qwen__Qwen1.5-7B-Chat from data.\n",
      "Processed open-llm-leaderboard-old/details_Doctor-Shotgun__CalliopeDS-v2-L2-13B from data.\n",
      "Processed dharun2049/dermaflow-v1 from data.\n",
      "Processed joey234/mmlu-college_biology from data.\n",
      "Processed open-llm-leaderboard-old/details_arlineka__KittyNyanster-v1 from data.\n",
      "Processed OALL/details_Orenguteng__Llama-3.1-8B-Lexi-Uncensored-V2 from data.\n",
      "Processed open-llm-leaderboard-old/details_Lvxy1117__amber_fine_tune_sg_part1 from data.\n",
      "Processed open-llm-leaderboard-old/details_SC99__Mistral-7B-privatemix-ia3 from data.\n",
      "Processed open-llm-leaderboard-old/details_uukuguy__speechless-coding-7b-16k-tora from data.\n",
      "Processed open-llm-leaderboard-old/details_viethq188__Rabbit-7B-v2-DPO-Chat from data.\n",
      "Processed efederici/biologia-mc from data.\n",
      "Processed open-llm-leaderboard-old/details_jikaixuan__test_model from data.\n",
      "Processed jiawennnn/STimage-1K4M from data.\n",
      "Processed philschmid/slimorca-dedup-chatml-100k from data.\n",
      "Processed shuangyu226/myDataset1 from data.\n",
      "Processed open-llm-leaderboard-old/details_cyberagent__calm2-7b-chat-dpo-experimental from data.\n",
      "Processed danjacobellis/imagenet-1k-walloc-originalsize from data.\n",
      "Processed OALL/details_nvidia__Llama3-ChatQA-1.5-8B from data.\n",
      "Processed open-llm-leaderboard-old/details_CallComply__SOLAR-10.7B-Instruct-v1.0-128k from data.\n",
      "Processed open-llm-leaderboard-old/details_Locutusque__Hercules-3.0-Mistral-7B from data.\n",
      "Processed open-llm-leaderboard-old/details_automerger__ShadowYam-7B from data.\n",
      "Processed openclimatefix/gfs-reforecast from data.\n",
      "Processed open-llm-leaderboard-old/details_alexredna__TinyLlama-1.1B-Chat-v1.0-reasoning-v2-dpo from data.\n",
      "Processed open-llm-leaderboard-old/details_wang7776__vicuna-7b-v1.3-attention-sparsity-20 from data.\n",
      "Processed open-llm-leaderboard-old/details_liminerity__Omningotex-7b-slerp from data.\n",
      "Processed open-llm-leaderboard-old/details_wang7776__vicuna-7b-v1.3-sparsity-10 from data.\n",
      "Processed open-llm-leaderboard-old/details_hedronstone__OpenHermes-7B-Symbolic from data.\n",
      "Processed open-llm-leaderboard-old/details_EleutherAI__pythia-6.9b-deduped from data.\n",
      "Processed haijian06/Advanced-Math from data.\n",
      "Processed HydraLM/biology_dataset_list_dict from data.\n",
      "Processed open-llm-leaderboard-old/details_AA051610__Q from data.\n",
      "Processed open-llm-leaderboard-old/details_Azure99__blossom-v4-qwen1_5-14b from data.\n",
      "Processed open-llm-leaderboard-old/details_CHIH-HUNG__llama-2-13b-FINETUNE3_3.3w-r4-q_k_v_o_gate_up_down from data.\n",
      "Processed open-llm-leaderboard-old/details_TurkuNLP__gpt3-finnish-small from data.\n",
      "Processed open-llm-leaderboard-old/details_PocketDoc__Dans-CreepingSenseOfDoom from data.\n",
      "Processed Azure99/blossom-math-v2 from data.\n",
      "Processed joey234/mmlu-high_school_physics-rule-neg-prepend from data.\n",
      "Processed open-llm-leaderboard-old/details_chaoyi-wu__MedLLaMA_13B from data.\n",
      "Processed ccdv/arxiv-classification from data.\n",
      "Processed open-llm-leaderboard-old/details_NovoCode__Metabird-7b-DPO from data.\n",
      "Processed Mxode/Baike-Astronomy-ZH from data.\n",
      "Processed PDBEurope/protein_chain_conformational_states from data.\n",
      "Processed samhog/psychology-RLHF from data.\n",
      "Processed AcaSp/DomainSpeech from data.\n",
      "Processed open-llm-leaderboard-old/details_charlesdedampierre__TopicNeuralHermes-2.5-Mistral-7B from data.\n",
      "Processed open-llm-leaderboard-old/details_ChaoticNeutrals__RP_Vision_7B from data.\n",
      "Processed dafun/open-web-math-clean from data.\n",
      "Processed open-llm-leaderboard-old/details_Azazelle__Sina-Thor-7b-Merge from data.\n",
      "Processed open-llm-leaderboard-old/details_hoskinson-center__proofGPT-v0.1 from data.\n",
      "Processed open-llm-leaderboard-old/details_bardsai__jaskier-7b-dpo-v3.3 from data.\n",
      "Processed open-llm-leaderboard-old/details_Kukedlc__neuronal-7b-Mlab from data.\n",
      "Processed open-llm-leaderboard-old/details_TheBloke__airoboros-33B-gpt4-1-4-SuperHOT-8K-fp16 from data.\n",
      "Processed open-llm-leaderboard-old/details_Evaloric__Evaloric-1.1B from data.\n",
      "Processed open-llm-leaderboard-old/details_Azazelle__Moko-DARE from data.\n",
      "Processed open-llm-leaderboard-old/details_Kquant03__NeuralTrix-7B-dpo-relaser from data.\n",
      "Processed open-llm-leaderboard-old/details_chargoddard__average-dolphin-8x7B from data.\n",
      "Processed open-llm-leaderboard-old/details_bhenrym14__airophin-13b-pntk-16k-fp16 from data.\n",
      "Processed open-llm-leaderboard-old/details_BarraHome__Mistroll-7B-v0.2-16bit from data.\n",
      "Processed open-llm-leaderboard-old/details_dillfrescott__amadeus-v0.1 from data.\n",
      "Processed open-llm-leaderboard-old/details_jsfs11__HighdensityRPMerge-7B from data.\n",
      "Processed ai2lumos/lumos_unified_ground_iterative from data.\n",
      "Processed zpn/pcba_686978 from data.\n",
      "Processed open-llm-leaderboard-old/details_kwchoi__DPO_mistral_v01_7b_ultra_0130_1k from data.\n",
      "Processed Tural/MetaMathQA from data.\n",
      "Processed mauyu/earth_chan from data.\n",
      "Processed open-llm-leaderboard-old/details_Danielbrdz__Barcenas-3b from data.\n",
      "Processed open-llm-leaderboard-old/details_JCX-kcuf__Mistral-7B-v0.1-gpt-4-20k from data.\n",
      "Processed open-llm-leaderboard-old/details_klosax__open_llama_13b_600bt_preview from data.\n",
      "Processed open-llm-leaderboard-old/details_Sharathhebbar24__convo_bot_gpt_v1 from data.\n",
      "Processed Zaid/mmlu-random-1 from data.\n",
      "Processed open-llm-leaderboard-old/details_leveldevai__MarcDareBeagle-7B from data.\n",
      "Processed open-llm-leaderboard-old/details_jjaaaww__posi_13b from data.\n",
      "Processed open-llm-leaderboard-old/details_OEvortex__HelpingAI-Lite-2x1B from data.\n",
      "Processed open-llm-leaderboard-old/details_xformAI__facebook-opt-125m-qcqa-ub-6-best-for-q-loss from data.\n",
      "Processed open-llm-leaderboard-old/details_mayacinka__yam-sam-7B from data.\n",
      "Processed open-llm-leaderboard-old/details_UCLA-AGI__test_final from data.\n",
      "Processed open-llm-leaderboard-old/details_senseable__moe-x33 from data.\n",
      "Processed open-llm-leaderboard-old/details_saarvajanik__facebook-opt-6.7b-gqa-ub-16-best-for-KV-cache from data.\n",
      "Processed sled-umich/TRIP from data.\n",
      "Processed open-llm-leaderboard-old/details_freecs__ThetaWave-7B-v1 from data.\n",
      "Processed julien-c/kaggle-hugomathien-soccer from data.\n",
      "Processed autoevaluate/autoeval-eval-mathemakitten__winobias_antistereotype_test_cot_v1-math-1bbcaf-1917164991 from data.\n",
      "Processed OALL/details_inceptionai__jais-family-30b-8k-chat from data.\n",
      "Processed open-llm-leaderboard-old/details_L-R__LLmRA-3B-v0.1 from data.\n",
      "Processed YurtsAI/named_entity_recognition from data.\n",
      "Processed hon9kon9ize/yue-math-preference from data.\n",
      "Processed open-llm-leaderboard-old/details_jefferylovely__SuperThetaMaven from data.\n",
      "Processed open-llm-leaderboard-old/details_quantumaikr__QuantumLM-70B-hf from data.\n",
      "Processed open-llm-leaderboard-old/details_SF-Foundation__Ein-72B-v0.13 from data.\n",
      "Processed Xuehai/MMWorld from data.\n",
      "Processed open-llm-leaderboard-old/details_jisukim8873__falcon-7B-case-8 from data.\n",
      "Processed open-llm-leaderboard-old/details_wang7776__Llama-2-7b-chat-hf-30-sparsity from data.\n",
      "Processed open-llm-leaderboard-old/details_caisarl76__mistral-guanaco1k-ep2 from data.\n",
      "Processed 1;github.com/bjl2n/tjhd_prevalence_of_obesity from source.\n",
      "Processed 1;github.com/boc5244/CrazyFly from source.\n",
      "Processed 1;github.com/Dio990521/SAEDS from source.\n",
      "Processed 1;github.com/engjefersonsantiago/P4HLS from source.\n",
      "Processed 1;github.com/Natlem/M-HTCN from source.\n",
      "Processed 1;github.com/jules-leguy/BBOMol from source.\n",
      "Processed 1;github.com/rezazad68/fewshot-segmentation from source.\n",
      "Processed 1;github.com/awesome-davian/pixelsne from source.\n",
      "Processed 1;github.com/umanlp/DS-TOD from source.\n",
      "Processed 2;github.com/ale-mendez/ESPNN from source.\n",
      "Processed 1;github.com/jose-ameijeiras/Car-crashes-data from source.\n",
      "Processed 1;github.com/gmantele/vollt from source.\n",
      "Processed 1;github.com/zoghbi-a/aztools from source.\n",
      "Processed 1;github.com/LirongWu/FF-G2M from source.\n",
      "Processed 2;github.com/Hsankesara/DeepResearch from source.\n",
      "Processed 1;github.com/kvegesan-stjude/SHIVERS2 from source.\n",
      "Processed 1;github.com/kamenoseiji/ALMA-2016.1.00375.S from source.\n",
      "Processed 2;github.com/mkleehammer/pyodbc from source.\n",
      "Processed 1;github.com/Spatial-R/EvoEnvFlu from source.\n",
      "Processed 1;github.com/manellopez13/dl4rf_meta_analysis from source.\n",
      "Processed 1;github.com/PoisonAlien/Maftools from source.\n",
      "Processed 1;github.com/cduck/orchestrated-trios from source.\n",
      "Processed 1;github.com/WuLoli/GAEseq from source.\n",
      "Processed 10;github.com/linnabrown/run_dbcan from source.\n",
      "Processed 2;github.com/HumanCellAtlas/metadata-schema from source.\n",
      "Processed 12;github.com/yoheinakajima/babyagi from source.\n",
      "Processed 1;github.com/Dallinger/Dallinger from source.\n",
      "Processed 1;github.com/michaelklachko/noisynet from source.\n",
      "Processed 1;github.com/kolecki4/PyKMOD from source.\n",
      "Processed 1;github.com/MicheleUIT/Explaining_SGNN from source.\n",
      "Processed 2;github.com/kipoi/manuscript from source.\n",
      "Processed 3;github.com/GiocomoLab/ln-model-of-mec-neurons from source.\n",
      "Processed 1;github.com/HDI-Project/SDV from source.\n",
      "Processed 1;github.com/digama0/mm0 from source.\n",
      "Processed 6;github.com/ontop/ontop from source.\n",
      "Processed 5;github.com/ye-kyaw-thu/sylbreak from source.\n",
      "Processed 1;github.com/zijguo/Searching-Sampling-Replication from source.\n",
      "Processed 1;github.com/FaultyIsogenies/faulty-isogeny-code from source.\n",
      "Processed 2;github.com/Openning07/GPA from source.\n",
      "Processed 1;github.com/kennymckormick/ARAS-Dataset from source.\n",
      "Processed 10;github.com/FDA/CiPA from source.\n",
      "Processed 1;github.com/li-xirong/mmc-amd from source.\n",
      "Processed 1;github.com/PeteHeine/pedestrian_detector_ros from source.\n",
      "Processed 1;github.com/anvaly/SpatialPortal from source.\n",
      "Processed 1;github.com/rii-mango/papaya from source.\n",
      "Processed 1;github.com/js2251/WaveglowHI from source.\n",
      "Processed 1;github.com/UiO-CS/tf-wavelets from source.\n",
      "Processed 1;github.com/oliverphilcox/BOSS-Without-Windows from source.\n",
      "Processed 1;github.com/Nanguage/Regeneration from source.\n",
      "Processed 2;github.com/seqcode/IDEAS from source.\n",
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      "Processed 1;github.com/gentlewasp/population_genomics_climate_adaptation_scripts from source.\n",
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     ]
    }
   ],
   "source": [
    "\n",
    "output_file = gzip.open(\"output/harshvar.json.gz\", 'wt', encoding=\"utf-8\")\n",
    "source_files = {\"model\": \"input/harshvar_model\", \"data\": \"input/harshvar_data\", \"source\": \"input/harshvar_source\"}\n",
    "base_urls = {\n",
    "    \"model\": \"https://huggingface.co/\",\n",
    "    \"data\": \"https://huggingface.co/datasets/\",\n",
    "    \"source\": \"https://raw.githubusercontent.com/\"\n",
    "}\n",
    "readme_paths = {\n",
    "    \"model\": \"/raw/main/README.md\",\n",
    "    \"data\": \"/raw/main/README.md\",\n",
    "    \"source\": \"/master/README.md\"\n",
    "}\n",
    "\n",
    "for source_type, input_file in source_files.items():\n",
    "    with open(input_file, 'r') as f:\n",
    "        for line in f:\n",
    "            line = line.strip()\n",
    "            entry = process_entry(line, source_type, base_urls[source_type], readme_paths[source_type])\n",
    "            if entry:\n",
    "                json.dump(entry, output_file)\n",
    "                output_file.write(\"\\n\")\n",
    "                print(f\"Processed {entry['id']} from {source_type}.\")\n",
    "output_file.close()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\"id\": \"bartowski/dolphin-2.9.3-Yi-1.5-34B-32k-GGUF\", \"type\": \"model\", \"url\": \"https://huggingface.co/bartowski/dolphin-2.9.3-Yi-1.5-34B-32k-GGUF/raw/main/README.md\", \"content\": \"--- license: apache-2.0 base_model: 01-ai/Yi-1.5-34B-32k tags: - generated_from_trainer - axolotl datasets: - cognitivecomputations/Dolphin-2.9 - teknium/OpenHermes-2.5 - m-a-p/CodeFeedback-Filtered-Instruction - cognitivecomputations/dolphin-coder - cognitivecomputations/samantha-data - microsoft/orca-math-word-problems-200k - Locutusque/function-calling-chatml - internlm/Agent-FLAN quantized_by: bartowski pipeline_tag: text-generation ---  ## Llamacpp imatrix Quantizations of dolphin-2.9.3-Yi-1.5-34B-32k  Using <a href=\\\"https://github.com/ggerganov/llama.cpp/\\\">llama.cpp</a> release <a href=\\\"https://github.com/ggerganov/llama.cpp/releases/tag/b3197\\\">b3197</a> for quantization.  Original model: https://huggingface.co/cognitivecomputations/dolphin-2.9.3-Yi-1.5-34B-32k  All quants made using imatrix option with dataset from [here](https://gist.github.com/bartowski1182/eb213dccb3571f863da82e99418f81e8)  ## Prompt format  ``` <|im_start|> system {system_prompt}<|im_end|>  <|im_start|> user {prompt}<|im_end|>  <|im_start|> assistant  ```  ## Download a file (not the whole branch) from below:  | Filename | Quant type | File Size | Description | | -------- | ---------- | --------- | ----------- | | [dolphin-2.9.3-Yi-1.5-34B-32k-Q8_0_L.gguf](https://huggingface.co/bartowski/dolphin-2.9.3-Yi-1.5-34B-32k-GGUF/blob/main/dolphin-2.9.3-Yi-1.5-34B-32k-Q8_1.gguf) | Q8_0_L | 37.40GB | *Experimental*, uses f16 for embed and output weights. Please provide any feedback of differences. Extremely high quality, generally unneeded but max available quant. | | [dolphin-2.9.3-Yi-1.5-34B-32k-Q8_0.gguf](https://huggingface.co/bartowski/dolphin-2.9.3-Yi-1.5-34B-32k-GGUF/blob/main/dolphin-2.9.3-Yi-1.5-34B-32k-Q8_0.gguf) | Q8_0 | 36.54GB | Extremely high quality, generally unneeded but max available quant. | | [dolphin-2.9.3-Yi-1.5-34B-32k-Q6_K_L.gguf](https://huggingface.co/bartowski/dolphin-2.9.3-Yi-1.5-34B-32k-GGUF/blob/main/dolphin-2.9.3-Yi-1.5-34B-32k-Q6_K_L.gguf) | Q6_K_L | 29.29GB | *Experimental*, uses f16 for embed and output weights. Please provide any feedback of differences. Very high quality, near perfect, *recommended*. | | [dolphin-2.9.3-Yi-1.5-34B-32k-Q6_K.gguf](https://huggingface.co/bartowski/dolphin-2.9.3-Yi-1.5-34B-32k-GGUF/blob/main/dolphin-2.9.3-Yi-1.5-34B-32k-Q6_K.gguf) | Q6_K | 28.21GB | Very high quality, near perfect, *recommended*. | | [dolphin-2.9.3-Yi-1.5-34B-32k-Q5_K_L.gguf](https://huggingface.co/bartowski/dolphin-2.9.3-Yi-1.5-34B-32k-GGUF/blob/main/dolphin-2.9.3-Yi-1.5-34B-32k-Q5_K_L.gguf) | Q5_K_L | 25.46GB | *Experimental*, uses f16 for embed and output weights. Please provide any feedback of differences. High quality, *recommended*. | | [dolphin-2.9.3-Yi-1.5-34B-32k-Q5_K_M.gguf](https://huggingface.co/bartowski/dolphin-2.9.3-Yi-1.5-34B-32k-GGUF/blob/main/dolphin-2.9.3-Yi-1.5-34B-32k-Q5_K_M.gguf) | Q5_K_M | 24.32GB | High quality, *recommended*. | | [dolphin-2.9.3-Yi-1.5-34B-32k-Q5_K_S.gguf](https://huggingface.co/bartowski/dolphin-2.9.3-Yi-1.5-34B-32k-GGUF/blob/main/dolphin-2.9.3-Yi-1.5-34B-32k-Q5_K_S.gguf) | Q5_K_S | 23.70GB | High quality, *recommended*. | | [dolphin-2.9.3-Yi-1.5-34B-32k-Q4_K_L.gguf](https://huggingface.co/bartowski/dolphin-2.9.3-Yi-1.5-34B-32k-GGUF/blob/main/dolphin-2.9.3-Yi-1.5-34B-32k-Q4_K_L.gguf) | Q4_K_L | 21.85GB | *Experimental*, uses f16 for embed and output weights. Please provide any feedback of differences. Good quality, uses about 4.83 bits per weight, *recommended*. | | [dolphin-2.9.3-Yi-1.5-34B-32k-Q4_K_M.gguf](https://huggingface.co/bartowski/dolphin-2.9.3-Yi-1.5-34B-32k-GGUF/blob/main/dolphin-2.9.3-Yi-1.5-34B-32k-Q4_K_M.gguf) | Q4_K_M | 20.65GB | Good quality, uses about 4.83 bits per weight, *recommended*. | | [dolphin-2.9.3-Yi-1.5-34B-32k-Q4_K_S.gguf](https://huggingface.co/bartowski/dolphin-2.9.3-Yi-1.5-34B-32k-GGUF/blob/main/dolphin-2.9.3-Yi-1.5-34B-32k-Q4_K_S.gguf) | Q4_K_S | 19.59GB | Slightly lower quality with more space savings, *recommended*. | | [dolphin-2.9.3-Yi-1.5-34B-32k-IQ4_XS.gguf](https://huggingface.co/bartowski/dolphin-2.9.3-Yi-1.5-34B-32k-GGUF/blob/main/dolphin-2.9.3-Yi-1.5-34B-32k-IQ4_XS.gguf) | IQ4_XS | 18.47GB | Decent quality, smaller than Q4_K_S with similar performance, *recommended*. | | [dolphin-2.9.3-Yi-1.5-34B-32k-Q3_K_XL.gguf](https://huggingface.co/bartowski/dolphin-2.9.3-Yi-1.5-34B-32k-GGUF//main/dolphin-2.9.3-Yi-1.5-34B-32k-Q3_K_XL.gguf) | Q3_K_XL |  | *Experimental*, uses f16 for embed and output weights. Please provide any feedback of differences. Lower quality but usable, good for low RAM availability. | | [dolphin-2.9.3-Yi-1.5-34B-32k-Q3_K_L.gguf](https://huggingface.co/bartowski/dolphin-2.9.3-Yi-1.5-34B-32k-GGUF/blob/main/dolphin-2.9.3-Yi-1.5-34B-32k-Q3_K_L.gguf) | Q3_K_L | 18.13GB | Lower quality but usable, good for low RAM availability. | | [dolphin-2.9.3-Yi-1.5-34B-32k-Q3_K_M.gguf](https://huggingface.co/bartowski/dolphin-2.9.3-Yi-1.5-34B-32k-GGUF/blob/main/dolphin-2.9.3-Yi-1.5-34B-32k-Q3_K_M.gguf) | Q3_K_M | 16.65GB | Even lower quality. | | [dolphin-2.9.3-Yi-1.5-34B-32k-IQ3_M.gguf](https://huggingface.co/bartowski/dolphin-2.9.3-Yi-1.5-34B-32k-GGUF/blob/main/dolphin-2.9.3-Yi-1.5-34B-32k-IQ3_M.gguf) | IQ3_M | 15.56GB | Medium-low quality, new method with decent performance comparable to Q3_K_M. | | [dolphin-2.9.3-Yi-1.5-34B-32k-Q3_K_S.gguf](https://huggingface.co/bartowski/dolphin-2.9.3-Yi-1.5-34B-32k-GGUF/blob/main/dolphin-2.9.3-Yi-1.5-34B-32k-Q3_K_S.gguf) | Q3_K_S | 14.96GB | Low quality, not recommended. | | [dolphin-2.9.3-Yi-1.5-34B-32k-IQ3_XS.gguf](https://huggingface.co/bartowski/dolphin-2.9.3-Yi-1.5-34B-32k-GGUF/blob/main/dolphin-2.9.3-Yi-1.5-34B-32k-IQ3_XS.gguf) | IQ3_XS | 14.23GB | Lower quality, new method with decent performance, slightly better than Q3_K_S. | | [dolphin-2.9.3-Yi-1.5-34B-32k-IQ3_XXS.gguf](https://huggingface.co/bartowski/dolphin-2.9.3-Yi-1.5-34B-32k-GGUF/blob/main/dolphin-2.9.3-Yi-1.5-34B-32k-IQ3_XXS.gguf) | IQ3_XXS | 13.33GB | Lower quality, new method with decent performance, comparable to Q3 quants. | | [dolphin-2.9.3-Yi-1.5-34B-32k-Q2_K.gguf](https://huggingface.co/bartowski/dolphin-2.9.3-Yi-1.5-34B-32k-GGUF/blob/main/dolphin-2.9.3-Yi-1.5-34B-32k-Q2_K.gguf) | Q2_K | 12.82GB | Very low quality but surprisingly usable. | | [dolphin-2.9.3-Yi-1.5-34B-32k-IQ2_M.gguf](https://huggingface.co/bartowski/dolphin-2.9.3-Yi-1.5-34B-32k-GGUF/blob/main/dolphin-2.9.3-Yi-1.5-34B-32k-IQ2_M.gguf) | IQ2_M | 11.79GB | Very low quality, uses SOTA techniques to also be surprisingly usable. | | [dolphin-2.9.3-Yi-1.5-34B-32k-IQ2_S.gguf](https://huggingface.co/bartowski/dolphin-2.9.3-Yi-1.5-34B-32k-GGUF/blob/main/dolphin-2.9.3-Yi-1.5-34B-32k-IQ2_S.gguf) | IQ2_S | 10.89GB | Very low quality, uses SOTA techniques to be usable. | | [dolphin-2.9.3-Yi-1.5-34B-32k-IQ2_XS.gguf](https://huggingface.co/bartowski/dolphin-2.9.3-Yi-1.5-34B-32k-GGUF/blob/main/dolphin-2.9.3-Yi-1.5-34B-32k-IQ2_XS.gguf) | IQ2_XS | 10.30GB | Very low quality, uses SOTA techniques to be usable. |  ## Downloading using huggingface-cli  First, make sure you have hugginface-cli installed:  ``` pip install -U \\\"huggingface_hub[cli]\\\" ```  Then, you can target the specific file you want:  ``` huggingface-cli download bartowski/dolphin-2.9.3-Yi-1.5-34B-32k-GGUF --include \\\"dolphin-2.9.3-Yi-1.5-34B-32k-Q4_K_M.gguf\\\" --local-dir ./ ```  If the model is bigger than 50GB, it will have been split into multiple files. In order to download them all to a local folder, run:  ``` huggingface-cli download bartowski/dolphin-2.9.3-Yi-1.5-34B-32k-GGUF --include \\\"dolphin-2.9.3-Yi-1.5-34B-32k-Q8_0.gguf/*\\\" --local-dir dolphin-2.9.3-Yi-1.5-34B-32k-Q8_0 ```  You can either specify a new local-dir (dolphin-2.9.3-Yi-1.5-34B-32k-Q8_0) or download them all in place (./)  ## Which file should I choose?  A great write up with charts showing various performances is provided by Artefact2 [here](https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9)  The first thing to figure out is how big a model you can run. To do this, you'll need to figure out how much RAM and/or VRAM you have.  If you want your model running as FAST as possible, you'll want to fit the whole thing on your GPU's VRAM. Aim for a quant with a file size 1-2GB smaller than your GPU's total VRAM.  If you want the absolute maximum quality, add both your system RAM and your GPU's VRAM together, then similarly grab a quant with a file size 1-2GB Smaller than that total.  Next, you'll need to decide if you want to use an 'I-quant' or a 'K-quant'.  If you don't want to think too much, grab one of the K-quants. These are in format 'QX_K_X', like Q5_K_M.  If you want to get more into the weeds, you can check out this extremely useful feature chart:  [llama.cpp feature matrix](https://github.com/ggerganov/llama.cpp/wiki/Feature-matrix)  But basically, if you're aiming for below Q4, and you're running cuBLAS (Nvidia) or rocBLAS (AMD), you should look towards the I-quants. These are in format IQX_X, like IQ3_M. These are newer and offer better performance for their size.  These I-quants can also be used on CPU and Apple Metal, but will be slower than their K-quant equivalent, so speed vs performance is a tradeoff you'll have to decide.  The I-quants are *not* compatible with Vulcan, which is also AMD, so if you have an AMD card double check if you're using the rocBLAS build or the Vulcan build. At the time of writing this, LM Studio has a preview with ROCm support, and other inference engines have specific builds for ROCm.  Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski \", \"links\": [\"https://github.com/ggerganov/llama.cpp/\", \"https://github.com/ggerganov/llama.cpp/releases/tag/b3197\", \"https://huggingface.co/cognitivecomputations/dolphin-2.9.3-Yi-1.5-34B-32k\", \"https://gist.github.com/bartowski1182/eb213dccb3571f863da82e99418f81e8\", \"https://huggingface.co/bartowski/dolphin-2.9.3-Yi-1.5-34B-32k-GGUF/blob/main/dolphin-2.9.3-Yi-1.5-34B-32k-Q8_1.gguf\", \"https://huggingface.co/bartowski/dolphin-2.9.3-Yi-1.5-34B-32k-GGUF/blob/main/dolphin-2.9.3-Yi-1.5-34B-32k-Q8_0.gguf\", \"https://huggingface.co/bartowski/dolphin-2.9.3-Yi-1.5-34B-32k-GGUF/blob/main/dolphin-2.9.3-Yi-1.5-34B-32k-Q6_K_L.gguf\", \"https://huggingface.co/bartowski/dolphin-2.9.3-Yi-1.5-34B-32k-GGUF/blob/main/dolphin-2.9.3-Yi-1.5-34B-32k-Q6_K.gguf\", \"https://huggingface.co/bartowski/dolphin-2.9.3-Yi-1.5-34B-32k-GGUF/blob/main/dolphin-2.9.3-Yi-1.5-34B-32k-Q5_K_L.gguf\", \"https://huggingface.co/bartowski/dolphin-2.9.3-Yi-1.5-34B-32k-GGUF/blob/main/dolphin-2.9.3-Yi-1.5-34B-32k-Q5_K_M.gguf\", \"https://huggingface.co/bartowski/dolphin-2.9.3-Yi-1.5-34B-32k-GGUF/blob/main/dolphin-2.9.3-Yi-1.5-34B-32k-Q5_K_S.gguf\", \"https://huggingface.co/bartowski/dolphin-2.9.3-Yi-1.5-34B-32k-GGUF/blob/main/dolphin-2.9.3-Yi-1.5-34B-32k-Q4_K_L.gguf\", \"https://huggingface.co/bartowski/dolphin-2.9.3-Yi-1.5-34B-32k-GGUF/blob/main/dolphin-2.9.3-Yi-1.5-34B-32k-Q4_K_M.gguf\", \"https://huggingface.co/bartowski/dolphin-2.9.3-Yi-1.5-34B-32k-GGUF/blob/main/dolphin-2.9.3-Yi-1.5-34B-32k-Q4_K_S.gguf\", \"https://huggingface.co/bartowski/dolphin-2.9.3-Yi-1.5-34B-32k-GGUF/blob/main/dolphin-2.9.3-Yi-1.5-34B-32k-IQ4_XS.gguf\", \"https://huggingface.co/bartowski/dolphin-2.9.3-Yi-1.5-34B-32k-GGUF//main/dolphin-2.9.3-Yi-1.5-34B-32k-Q3_K_XL.gguf\", \"https://huggingface.co/bartowski/dolphin-2.9.3-Yi-1.5-34B-32k-GGUF/blob/main/dolphin-2.9.3-Yi-1.5-34B-32k-Q3_K_L.gguf\", \"https://huggingface.co/bartowski/dolphin-2.9.3-Yi-1.5-34B-32k-GGUF/blob/main/dolphin-2.9.3-Yi-1.5-34B-32k-Q3_K_M.gguf\", \"https://huggingface.co/bartowski/dolphin-2.9.3-Yi-1.5-34B-32k-GGUF/blob/main/dolphin-2.9.3-Yi-1.5-34B-32k-IQ3_M.gguf\", \"https://huggingface.co/bartowski/dolphin-2.9.3-Yi-1.5-34B-32k-GGUF/blob/main/dolphin-2.9.3-Yi-1.5-34B-32k-Q3_K_S.gguf\", \"https://huggingface.co/bartowski/dolphin-2.9.3-Yi-1.5-34B-32k-GGUF/blob/main/dolphin-2.9.3-Yi-1.5-34B-32k-IQ3_XS.gguf\", \"https://huggingface.co/bartowski/dolphin-2.9.3-Yi-1.5-34B-32k-GGUF/blob/main/dolphin-2.9.3-Yi-1.5-34B-32k-IQ3_XXS.gguf\", \"https://huggingface.co/bartowski/dolphin-2.9.3-Yi-1.5-34B-32k-GGUF/blob/main/dolphin-2.9.3-Yi-1.5-34B-32k-Q2_K.gguf\", \"https://huggingface.co/bartowski/dolphin-2.9.3-Yi-1.5-34B-32k-GGUF/blob/main/dolphin-2.9.3-Yi-1.5-34B-32k-IQ2_M.gguf\", \"https://huggingface.co/bartowski/dolphin-2.9.3-Yi-1.5-34B-32k-GGUF/blob/main/dolphin-2.9.3-Yi-1.5-34B-32k-IQ2_S.gguf\", \"https://huggingface.co/bartowski/dolphin-2.9.3-Yi-1.5-34B-32k-GGUF/blob/main/dolphin-2.9.3-Yi-1.5-34B-32k-IQ2_XS.gguf\", \"https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9\", \"https://github.com/ggerganov/llama.cpp/wiki/Feature-matrix\", \"https://ko-fi.com/bartowski\"], \"dois\": []}\n",
      "\n",
      "{\"id\": \"mradermacher/Boundary-4x7b-MoE-i1-GGUF\", \"type\": \"model\", \"url\": \"https://huggingface.co/mradermacher/Boundary-4x7b-MoE-i1-GGUF/raw/main/README.md\", \"content\": \"--- base_model: NotAiLOL/Boundary-4x7b-MoE language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - moe - merge - mergekit - HuggingFaceH4/zephyr-7b-beta - mistralai/Mistral-7B-Instruct-v0.2 - teknium/OpenHermes-2.5-Mistral-7B - meta-math/MetaMath-Mistral-7B - Mistral --- ## About  <!-- ### quantize_version: 1 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type:  --> <!-- ### vocab_type:  --> weighted/imatrix quants of https://huggingface.co/NotAiLOL/Boundary-4x7b-MoE  <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/Boundary-4x7b-MoE-GGUF ## Usage  If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files.  ## Provided Quants  (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)  | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/Boundary-4x7b-MoE-i1-GGUF/resolve/main/Boundary-4x7b-MoE.i1-IQ1_S.gguf) | i1-IQ1_S | 5.1 | for the desperate | | [GGUF](https://huggingface.co/mradermacher/Boundary-4x7b-MoE-i1-GGUF/resolve/main/Boundary-4x7b-MoE.i1-IQ1_M.gguf) | i1-IQ1_M | 5.6 | mostly desperate | | [GGUF](https://huggingface.co/mradermacher/Boundary-4x7b-MoE-i1-GGUF/resolve/main/Boundary-4x7b-MoE.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 6.5 |  | | [GGUF](https://huggingface.co/mradermacher/Boundary-4x7b-MoE-i1-GGUF/resolve/main/Boundary-4x7b-MoE.i1-IQ2_XS.gguf) | i1-IQ2_XS | 7.2 |  | | [GGUF](https://huggingface.co/mradermacher/Boundary-4x7b-MoE-i1-GGUF/resolve/main/Boundary-4x7b-MoE.i1-IQ2_S.gguf) | i1-IQ2_S | 7.4 |  | | [GGUF](https://huggingface.co/mradermacher/Boundary-4x7b-MoE-i1-GGUF/resolve/main/Boundary-4x7b-MoE.i1-IQ2_M.gguf) | i1-IQ2_M | 8.1 |  | | [GGUF](https://huggingface.co/mradermacher/Boundary-4x7b-MoE-i1-GGUF/resolve/main/Boundary-4x7b-MoE.i1-Q2_K.gguf) | i1-Q2_K | 8.9 | IQ3_XXS probably better | | [GGUF](https://huggingface.co/mradermacher/Boundary-4x7b-MoE-i1-GGUF/resolve/main/Boundary-4x7b-MoE.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 9.4 | lower quality | | [GGUF](https://huggingface.co/mradermacher/Boundary-4x7b-MoE-i1-GGUF/resolve/main/Boundary-4x7b-MoE.i1-IQ3_XS.gguf) | i1-IQ3_XS | 10.0 |  | | [GGUF](https://huggingface.co/mradermacher/Boundary-4x7b-MoE-i1-GGUF/resolve/main/Boundary-4x7b-MoE.i1-Q3_K_S.gguf) | i1-Q3_K_S | 10.5 | IQ3_XS probably better | | [GGUF](https://huggingface.co/mradermacher/Boundary-4x7b-MoE-i1-GGUF/resolve/main/Boundary-4x7b-MoE.i1-IQ3_S.gguf) | i1-IQ3_S | 10.6 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/Boundary-4x7b-MoE-i1-GGUF/resolve/main/Boundary-4x7b-MoE.i1-IQ3_M.gguf) | i1-IQ3_M | 10.7 |  | | [GGUF](https://huggingface.co/mradermacher/Boundary-4x7b-MoE-i1-GGUF/resolve/main/Boundary-4x7b-MoE.i1-Q3_K_M.gguf) | i1-Q3_K_M | 11.7 | IQ3_S probably better | | [GGUF](https://huggingface.co/mradermacher/Boundary-4x7b-MoE-i1-GGUF/resolve/main/Boundary-4x7b-MoE.i1-Q3_K_L.gguf) | i1-Q3_K_L | 12.6 | IQ3_M probably better | | [GGUF](https://huggingface.co/mradermacher/Boundary-4x7b-MoE-i1-GGUF/resolve/main/Boundary-4x7b-MoE.i1-IQ4_XS.gguf) | i1-IQ4_XS | 13.0 |  | | [GGUF](https://huggingface.co/mradermacher/Boundary-4x7b-MoE-i1-GGUF/resolve/main/Boundary-4x7b-MoE.i1-Q4_0.gguf) | i1-Q4_0 | 13.8 | fast, low quality | | [GGUF](https://huggingface.co/mradermacher/Boundary-4x7b-MoE-i1-GGUF/resolve/main/Boundary-4x7b-MoE.i1-Q4_K_S.gguf) | i1-Q4_K_S | 13.8 | optimal size/speed/quality | | [GGUF](https://huggingface.co/mradermacher/Boundary-4x7b-MoE-i1-GGUF/resolve/main/Boundary-4x7b-MoE.i1-Q4_K_M.gguf) | i1-Q4_K_M | 14.7 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Boundary-4x7b-MoE-i1-GGUF/resolve/main/Boundary-4x7b-MoE.i1-Q5_K_S.gguf) | i1-Q5_K_S | 16.7 |  | | [GGUF](https://huggingface.co/mradermacher/Boundary-4x7b-MoE-i1-GGUF/resolve/main/Boundary-4x7b-MoE.i1-Q5_K_M.gguf) | i1-Q5_K_M | 17.2 |  | | [GGUF](https://huggingface.co/mradermacher/Boundary-4x7b-MoE-i1-GGUF/resolve/main/Boundary-4x7b-MoE.i1-Q6_K.gguf) | i1-Q6_K | 19.9 | practically like static Q6_K |  Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):  ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png)  And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9  ## FAQ / Model Request  See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized.  ## Thanks  I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time.  <!-- end --> \", \"links\": [\"https://huggingface.co/NotAiLOL/Boundary-4x7b-MoE\", \"https://huggingface.co/mradermacher/Boundary-4x7b-MoE-GGUF\", \"https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF\", \"https://huggingface.co/mradermacher/Boundary-4x7b-MoE-i1-GGUF/resolve/main/Boundary-4x7b-MoE.i1-IQ1_S.gguf\", \"https://huggingface.co/mradermacher/Boundary-4x7b-MoE-i1-GGUF/resolve/main/Boundary-4x7b-MoE.i1-IQ1_M.gguf\", \"https://huggingface.co/mradermacher/Boundary-4x7b-MoE-i1-GGUF/resolve/main/Boundary-4x7b-MoE.i1-IQ2_XXS.gguf\", \"https://huggingface.co/mradermacher/Boundary-4x7b-MoE-i1-GGUF/resolve/main/Boundary-4x7b-MoE.i1-IQ2_XS.gguf\", \"https://huggingface.co/mradermacher/Boundary-4x7b-MoE-i1-GGUF/resolve/main/Boundary-4x7b-MoE.i1-IQ2_S.gguf\", \"https://huggingface.co/mradermacher/Boundary-4x7b-MoE-i1-GGUF/resolve/main/Boundary-4x7b-MoE.i1-IQ2_M.gguf\", \"https://huggingface.co/mradermacher/Boundary-4x7b-MoE-i1-GGUF/resolve/main/Boundary-4x7b-MoE.i1-Q2_K.gguf\", \"https://huggingface.co/mradermacher/Boundary-4x7b-MoE-i1-GGUF/resolve/main/Boundary-4x7b-MoE.i1-IQ3_XXS.gguf\", \"https://huggingface.co/mradermacher/Boundary-4x7b-MoE-i1-GGUF/resolve/main/Boundary-4x7b-MoE.i1-IQ3_XS.gguf\", \"https://huggingface.co/mradermacher/Boundary-4x7b-MoE-i1-GGUF/resolve/main/Boundary-4x7b-MoE.i1-Q3_K_S.gguf\", \"https://huggingface.co/mradermacher/Boundary-4x7b-MoE-i1-GGUF/resolve/main/Boundary-4x7b-MoE.i1-IQ3_S.gguf\", \"https://huggingface.co/mradermacher/Boundary-4x7b-MoE-i1-GGUF/resolve/main/Boundary-4x7b-MoE.i1-IQ3_M.gguf\", \"https://huggingface.co/mradermacher/Boundary-4x7b-MoE-i1-GGUF/resolve/main/Boundary-4x7b-MoE.i1-Q3_K_M.gguf\", \"https://huggingface.co/mradermacher/Boundary-4x7b-MoE-i1-GGUF/resolve/main/Boundary-4x7b-MoE.i1-Q3_K_L.gguf\", \"https://huggingface.co/mradermacher/Boundary-4x7b-MoE-i1-GGUF/resolve/main/Boundary-4x7b-MoE.i1-IQ4_XS.gguf\", \"https://huggingface.co/mradermacher/Boundary-4x7b-MoE-i1-GGUF/resolve/main/Boundary-4x7b-MoE.i1-Q4_0.gguf\", \"https://huggingface.co/mradermacher/Boundary-4x7b-MoE-i1-GGUF/resolve/main/Boundary-4x7b-MoE.i1-Q4_K_S.gguf\", \"https://huggingface.co/mradermacher/Boundary-4x7b-MoE-i1-GGUF/resolve/main/Boundary-4x7b-MoE.i1-Q4_K_M.gguf\", \"https://huggingface.co/mradermacher/Boundary-4x7b-MoE-i1-GGUF/resolve/main/Boundary-4x7b-MoE.i1-Q5_K_S.gguf\", \"https://huggingface.co/mradermacher/Boundary-4x7b-MoE-i1-GGUF/resolve/main/Boundary-4x7b-MoE.i1-Q5_K_M.gguf\", \"https://huggingface.co/mradermacher/Boundary-4x7b-MoE-i1-GGUF/resolve/main/Boundary-4x7b-MoE.i1-Q6_K.gguf\", \"https://www.nethype.de/huggingface_embed/quantpplgraph.png\", \"https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9\", \"https://huggingface.co/mradermacher/model_requests\", \"https://www.nethype.de/\"], \"dois\": []}\n",
      "\n",
      "{\"id\": \"Weyaxi/Einstein-v4-7B\", \"type\": \"model\", \"url\": \"https://huggingface.co/Weyaxi/Einstein-v4-7B/raw/main/README.md\", \"content\": \"--- language: - en license: other tags: - axolotl - generated_from_trainer - Mistral - instruct - finetune - chatml - gpt4 - synthetic data - science - physics - chemistry - biology - math base_model: mistralai/Mistral-7B-v0.1 datasets: - allenai/ai2_arc - camel-ai/physics - camel-ai/chemistry - camel-ai/biology - camel-ai/math - metaeval/reclor - openbookqa - mandyyyyii/scibench - derek-thomas/ScienceQA - TIGER-Lab/ScienceEval - jondurbin/airoboros-3.2 - LDJnr/Capybara - Cot-Alpaca-GPT4-From-OpenHermes-2.5 - STEM-AI-mtl/Electrical-engineering - knowrohit07/saraswati-stem - sablo/oasst2_curated - glaiveai/glaive-code-assistant - lmsys/lmsys-chat-1m - TIGER-Lab/MathInstruct - bigbio/med_qa - meta-math/MetaMathQA-40K - openbookqa - piqa - metaeval/reclor - derek-thomas/ScienceQA - scibench - sciq - Open-Orca/SlimOrca - migtissera/Synthia-v1.3 - TIGER-Lab/ScienceEval model-index: - name: Einstein-v4-7B   results:   - task:       type: text-generation       name: Text Generation     dataset:       name: AI2 Reasoning Challenge (25-Shot)       type: ai2_arc       config: ARC-Challenge       split: test       args:         num_few_shot: 25     metrics:     - type: acc_norm       value: 64.68       name: normalized accuracy     source:       url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Weyaxi/Einstein-v4-7B       name: Open LLM Leaderboard   - task:       type: text-generation       name: Text Generation     dataset:       name: HellaSwag (10-Shot)       type: hellaswag       split: validation       args:         num_few_shot: 10     metrics:     - type: acc_norm       value: 83.75       name: normalized accuracy     source:       url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Weyaxi/Einstein-v4-7B       name: Open LLM Leaderboard   - task:       type: text-generation       name: Text Generation     dataset:       name: MMLU (5-Shot)       type: cais/mmlu       config: all       split: test       args:         num_few_shot: 5     metrics:     - type: acc       value: 62.31       name: accuracy     source:       url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Weyaxi/Einstein-v4-7B       name: Open LLM Leaderboard   - task:       type: text-generation       name: Text Generation     dataset:       name: TruthfulQA (0-shot)       type: truthful_qa       config: multiple_choice       split: validation       args:         num_few_shot: 0     metrics:     - type: mc2       value: 55.15     source:       url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Weyaxi/Einstein-v4-7B       name: Open LLM Leaderboard   - task:       type: text-generation       name: Text Generation     dataset:       name: Winogrande (5-shot)       type: winogrande       config: winogrande_xl       split: validation       args:         num_few_shot: 5     metrics:     - type: acc       value: 76.24       name: accuracy     source:       url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Weyaxi/Einstein-v4-7B       name: Open LLM Leaderboard   - task:       type: text-generation       name: Text Generation     dataset:       name: GSM8k (5-shot)       type: gsm8k       config: main       split: test       args:         num_few_shot: 5     metrics:     - type: acc       value: 57.62       name: accuracy     source:       url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Weyaxi/Einstein-v4-7B       name: Open LLM Leaderboard   - task:       type: text-generation       name: Text Generation     dataset:       name: IFEval (0-Shot)       type: HuggingFaceH4/ifeval       args:         num_few_shot: 0     metrics:     - type: inst_level_strict_acc and prompt_level_strict_acc       value: 47.08       name: strict accuracy     source:       url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Weyaxi/Einstein-v4-7B       name: Open LLM Leaderboard   - task:       type: text-generation       name: Text Generation     dataset:       name: BBH (3-Shot)       type: BBH       args:         num_few_shot: 3     metrics:     - type: acc_norm       value: 14.3       name: normalized accuracy     source:       url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Weyaxi/Einstein-v4-7B       name: Open LLM Leaderboard   - task:       type: text-generation       name: Text Generation     dataset:       name: MATH Lvl 5 (4-Shot)       type: hendrycks/competition_math       args:         num_few_shot: 4     metrics:     - type: exact_match       value: 1.74       name: exact match     source:       url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Weyaxi/Einstein-v4-7B       name: Open LLM Leaderboard   - task:       type: text-generation       name: Text Generation     dataset:       name: GPQA (0-shot)       type: Idavidrein/gpqa       args:         num_few_shot: 0     metrics:     - type: acc_norm       value: 4.25       name: acc_norm     source:       url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Weyaxi/Einstein-v4-7B       name: Open LLM Leaderboard   - task:       type: text-generation       name: Text Generation     dataset:       name: MuSR (0-shot)       type: TAUR-Lab/MuSR       args:         num_few_shot: 0     metrics:     - type: acc_norm       value: 19.02       name: acc_norm     source:       url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Weyaxi/Einstein-v4-7B       name: Open LLM Leaderboard   - task:       type: text-generation       name: Text Generation     dataset:       name: MMLU-PRO (5-shot)       type: TIGER-Lab/MMLU-Pro       config: main       split: test       args:         num_few_shot: 5     metrics:     - type: acc       value: 13.99       name: accuracy     source:       url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Weyaxi/Einstein-v4-7B       name: Open LLM Leaderboard ---  ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6468ce47e134d050a58aa89c/U0zyXVGj-O8a7KP3BvPue.png) # \\ud83d\\udd2c Einstein-v4-7B  This model is a full fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on diverse datasets.  This model is finetuned using `7xRTX3090` + `1xRTXA6000` using [axolotl](https://github.com/OpenAccess-AI-Collective/axolotl).  This model's training was sponsored by [sablo.ai](https://sablo.ai).   <details><summary>See axolotl config</summary>  axolotl version: `0.4.0` ```yaml base_model: mistralai/Mistral-7B-v0.1 model_type: MistralForCausalLM tokenizer_type: LlamaTokenizer is_mistral_derived_model: true  load_in_8bit: false load_in_4bit: false strict: false  chat_template: chatml datasets:   - path: data/merged_all.json     ds_type: json     type: alpaca     conversation: chatml    - path: data/capybara_sharegpt.json     ds_type: json     type: sharegpt     conversation: chatml    - path: data/synthia-v1.3_sharegpt_12500.json     ds_type: json     type: sharegpt     conversation: chatml      - path: data/cot_alpaca_gpt4_extracted_openhermes_2.5_sharegpt.json     ds_type: json     type: sharegpt     conversation: chatml    - path: data/slimorca_dedup_filtered_95k_sharegpt.json     ds_type: json     type: sharegpt     conversation: chatml      - path: data/airoboros_3.2_without_contextual_slimorca_orca_sharegpt.json     ds_type: json     type: sharegpt     conversation: chatml    dataset_prepared_path: last_run_prepared val_set_size: 0.005 output_dir: ./Einstein-v4-model  sequence_len: 8192 sample_packing: true pad_to_sequence_len: true eval_sample_packing: false  wandb_project: Einstein wandb_entity: wandb_watch: wandb_name: wandb_log_model: hub_model_id: Weyaxi/Einstein-v4-7B  save_safetensors: true  gradient_accumulation_steps: 4 micro_batch_size: 1 num_epochs: 1.5 optimizer: adamw_bnb_8bit lr_scheduler: cosine learning_rate: 0.000005  train_on_inputs: false group_by_length: false bf16: true fp16: false tf32: false  gradient_checkpointing: true early_stopping_patience: resume_from_checkpoint: local_rank: logging_steps: 1 xformers_attention: flash_attention: true  warmup_steps: 10 evals_per_epoch: 2 # changed eval_table_size: eval_table_max_new_tokens: 128 saves_per_epoch: 4 debug:  deepspeed: zero3_bf16.json weight_decay: 0.0 fsdp: fsdp_config: special_tokens:   bos_token: \\\"<s>\\\"   eos_token: \\\"<|im_end|>\\\"   unk_token: \\\"<unk>\\\" tokens:   - \\\"<|im_start|>\\\"  resume_from_checkpoint: Einstein-v4-model/checkpoint-521  ```  </details><br>  # \\ud83d\\udcac Prompt Template  You can use this prompt template while using the model:  ### ChatML  ``` <|im_start|>system {system}<|im_end|> <|im_start|>user {user}<|im_end|> <|im_start|>assistant {asistant}<|im_end|> ```  This prompt template is available as a [chat template](https://huggingface.co/docs/transformers/main/chat_templating), which means you can format messages using the `tokenizer.apply_chat_template()` method:  ```python messages = [     {\\\"role\\\": \\\"system\\\", \\\"content\\\": \\\"You are helpful AI asistant.\\\"},     {\\\"role\\\": \\\"user\\\", \\\"content\\\": \\\"Hello!\\\"} ] gen_input = tokenizer.apply_chat_template(message, return_tensors=\\\"pt\\\") model.generate(**gen_input) ```  # \\ud83d\\udd04 Quantizationed versions  Quantizationed versions of this model is available.  ## GGUF [@LoneStriker](https://huggingface.co/LoneStriker)  - https://huggingface.co/LoneStriker/Einstein-v4-7B-GGUF  ## AWQ [@solidrust](https://huggingface.co/solidrust)  - https://huggingface.co/solidrust/Einstein-v4-7B-AWQ  ## Exl2 [@bartowski](https://hf.co/bartowski):  - https://huggingface.co/bartowski/Einstein-v4-7B-exl2  # \\ud83c\\udfaf [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_Weyaxi__Einstein-v4-7B)  |             Metric              |Value| |---------------------------------|----:| |Avg.                             |66.62| |AI2 Reasoning Challenge (25-Shot)|64.68| |HellaSwag (10-Shot)              |83.75| |MMLU (5-Shot)                    |62.31| |TruthfulQA (0-shot)              |55.15| |Winogrande (5-shot)              |76.24| |GSM8k (5-shot)                   |57.62|  # \\ud83c\\udfaf [Open LLM Leaderboard v2 Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_Weyaxi__Einstein-v4-7B)  |      Metric       |Value| |-------------------|----:| |Avg.               |16.73| |IFEval (0-Shot)    |47.08| |BBH (3-Shot)       |14.30| |MATH Lvl 5 (4-Shot)| 1.74| |GPQA (0-shot)      | 4.25| |MuSR (0-shot)      |19.02| |MMLU-PRO (5-shot)  |13.99|  # \\ud83d\\udcda Some resources, discussions and reviews aboout this model  #### \\ud83d\\udc26 Announcement tweet:   https://twitter.com/Weyaxi/status/1765851433448944125  #### \\ud83d\\udd0d Reddit post in r/LocalLLaMA:  -  https://www.reddit.com/r/LocalLLaMA/comments/1b9gmvl/meet_einsteinv47b_mistralbased_sft_model_using/  #### \\u25b6\\ufe0f Youtube Videos  - https://www.youtube.com/watch?v=-3YWgHJIORE&t=18s  - https://www.youtube.com/watch?v=Xo2ySU8gja0  # \\ud83e\\udd16 Additional information about training  This model is full fine-tuned for 1.5 epoch.   Total number of steps was 1562.  <details><summary>Loss graph</summary>  ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6468ce47e134d050a58aa89c/UO0NJz9VN5NncIXi82Nk2.png) </details><br>  # \\ud83e\\udd1d Acknowledgments  Thanks to [sablo.ai](https://sablo.ai) for sponsoring this model.  Thanks to all the dataset authors mentioned in the datasets section.  Thanks to [axolotl](https://github.com/OpenAccess-AI-Collective/axolotl) for making the repository I used to make this model.  Thanks to all open source AI community.  [<img src=\\\"https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png\\\" alt=\\\"Built with Axolotl\\\" width=\\\"200\\\" height=\\\"32\\\"/>](https://github.com/OpenAccess-AI-Collective/axolotl)  If you would like to support me:  [\\u2615 Buy Me a Coffee](https://www.buymeacoffee.com/weyaxi) \", \"links\": [\"https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Weyaxi/Einstein-v4-7B\", \"https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Weyaxi/Einstein-v4-7B\", \"https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Weyaxi/Einstein-v4-7B\", \"https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Weyaxi/Einstein-v4-7B\", \"https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Weyaxi/Einstein-v4-7B\", \"https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Weyaxi/Einstein-v4-7B\", \"https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Weyaxi/Einstein-v4-7B\", \"https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Weyaxi/Einstein-v4-7B\", \"https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Weyaxi/Einstein-v4-7B\", \"https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Weyaxi/Einstein-v4-7B\", \"https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Weyaxi/Einstein-v4-7B\", \"https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Weyaxi/Einstein-v4-7B\", \"https://cdn-uploads.huggingface.co/production/uploads/6468ce47e134d050a58aa89c/U0zyXVGj-O8a7KP3BvPue.png\", \"https://huggingface.co/mistralai/Mistral-7B-v0.1\", \"https://github.com/OpenAccess-AI-Collective/axolotl\", \"sablo.ai\", \"https://sablo.ai\", \"https://huggingface.co/docs/transformers/main/chat_templating\", \"https://huggingface.co/LoneStriker\", \"https://huggingface.co/LoneStriker/Einstein-v4-7B-GGUF\", \"https://huggingface.co/solidrust\", \"https://huggingface.co/solidrust/Einstein-v4-7B-AWQ\", \"https://hf.co/bartowski\", \"https://huggingface.co/bartowski/Einstein-v4-7B-exl2\", \"https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard\", \"https://huggingface.co/datasets/open-llm-leaderboard/details_Weyaxi__Einstein-v4-7B\", \"https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard\", \"https://huggingface.co/datasets/open-llm-leaderboard/details_Weyaxi__Einstein-v4-7B\", \"https://twitter.com/Weyaxi/status/1765851433448944125\", \"https://www.reddit.com/r/LocalLLaMA/comments/1b9gmvl/meet_einsteinv47b_mistralbased_sft_model_using/\", \"https://www.youtube.com/watch?v=-3YWgHJIORE&t=18s\", \"https://www.youtube.com/watch?v=Xo2ySU8gja0\", \"https://cdn-uploads.huggingface.co/production/uploads/6468ce47e134d050a58aa89c/UO0NJz9VN5NncIXi82Nk2.png\", \"sablo.ai\", \"https://sablo.ai\", \"https://github.com/OpenAccess-AI-Collective/axolotl\", \"https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png\", \"https://github.com/OpenAccess-AI-Collective/axolotl\", \"https://www.buymeacoffee.com/weyaxi\"], \"dois\": []}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# see content of the file\n",
    "import json\n",
    "import gzip\n",
    "with gzip.open(\"output/harshvar.json.gz\", 'rt', encoding=\"utf-8\") as f:\n",
    "    for i, line in enumerate(f):\n",
    "        print(line)\n",
    "        if i >= 2:\n",
    "            break"
   ]
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