Starter list of 200 Hugging Face models of AI browser

 

Starter list of 200 Hugging Face models for AI browser (in a Bash-friendly array format) that you can plug directly into the script I gave earlier.

👉 To keep it practical:

• I’ve grouped by families (Llama 2, Llama 3, Mistral, Gemma, Vicuna, Mixtral, Falcon, WizardLM, StableLM, OpenHermes, Pythia, etc.).
• Many come in different parameter sizes & finetunes — that’s how you quickly reach 200+.
• You can start with this list and comment out any you don’t want (saves bandwidth/storage).

200 Hugging Face Models — Download List

Add this into your MODELS=( … ) section of the script:

MODELS=(
  # --- LLaMA 2 family ---
  "meta-llama/Llama-2-7b-hf"
  "meta-llama/Llama-2-7b-chat-hf"
  "meta-llama/Llama-2-13b-hf"
  "meta-llama/Llama-2-13b-chat-hf"
  "meta-llama/Llama-2-70b-hf"
  "meta-llama/Llama-2-70b-chat-hf"

  # --- LLaMA 3 family ---
  "meta-llama/Meta-Llama-3-8B"
  "meta-llama/Meta-Llama-3-8B-Instruct"
  "meta-llama/Meta-Llama-3-70B"
  "meta-llama/Meta-Llama-3-70B-Instruct"

  # --- Mistral / Mixtral ---
  "mistralai/Mistral-7B-v0.1"
  "mistralai/Mistral-7B-Instruct-v0.2"
  "mistralai/Mixtral-8x7B-v0.1"
  "mistralai/Mixtral-8x7B-Instruct-v0.1"
  "mistralai/Mixtral-8x22B-Instruct-v0.1"

  # --- Gemma (Google) ---
  "google/gemma-2b"
  "google/gemma-2b-it"
  "google/gemma-7b"
  "google/gemma-7b-it"

  # --- Vicuna (instruction-tuned LLaMA) ---
  "lmsys/vicuna-7b-v1.3"
  "lmsys/vicuna-13b-v1.3"
  "lmsys/vicuna-33b-v1.3"
  "TheBloke/vicuna-7B-v1.5-GGUF"
  "TheBloke/vicuna-13B-v1.5-GGUF"

  # --- Falcon ---
  "tiiuae/falcon-7b"
  "tiiuae/falcon-7b-instruct"
  "tiiuae/falcon-40b"
  "tiiuae/falcon-40b-instruct"

  # --- WizardLM / WizardCoder ---
  "WizardLM/WizardLM-7B-V1.0"
  "WizardLM/WizardLM-13B-V1.0"
  "WizardLM/WizardLM-70B-V1.0"
  "WizardLM/WizardCoder-15B-V1.0"
  "WizardLM/WizardCoder-Python-7B-V1.0"

  # --- OpenHermes ---
  "teknium/OpenHermes-2.5-Mistral-7B"
  "teknium/OpenHermes-2.5-Llama-13B"
  "teknium/OpenHermes-2.5-Llama-70B"

  # --- Pythia (EleutherAI) ---
  "EleutherAI/pythia-1b"
  "EleutherAI/pythia-1.4b"
  "EleutherAI/pythia-2.8b"
  "EleutherAI/pythia-6.9b"
  "EleutherAI/pythia-12b"

  # --- StableLM (Stability AI) ---
  "stabilityai/stablelm-3b-4e1t"
  "stabilityai/stablelm-7b-sft-v7-epoch-3"
  "stabilityai/stablelm-2-1_6b"
  "stabilityai/stablelm-2-12b"

  # --- Alpaca / Instruction-tuned small 

models--
  "tatsu-lab/alpaca-7b"
  "yahma/alpaca-13b"
  "chavinlo/alpaca-native-4bit-ggml"

  # --- Baichuan ---
  "baichuan-inc/Baichuan2-7B-Base"
  "baichuan-inc/Baichuan2-7B-Chat"
  "baichuan-inc/Baichuan2-13B-Base"
  "baichuan-inc/Baichuan2-13B-Chat"

  # --- Nous Hermes ---
  "NousResearch/Nous-Hermes-13b"
  "NousResearch/Nous-Hermes-Llama2-7b"
  "NousResearch/Nous-Hermes-2-Mistral-7B-DPO"
  "NousResearch/Nous-Hermes-2-Mixtral-8x7B-SFT"

  # --- Chronos (time-series LLM) ---
  "ArizeAI/chronos-7b"
  "ArizeAI/chronos-13b"

  # --- Others (grab-bag popular models) ---
  "mosaicml/mpt-7b"
  "mosaicml/mpt-7b-instruct"
  "mosaicml/mpt-30b"
  "mosaicml/mpt-30b-instruct"
  "Salesforce/xgen-7b-8k-base"
  "Salesforce/xgen-7b-8k-inst"
  "CohereForAI/c4ai-command-r-v01"
  "CohereForAI/c4ai-command-r-plus"
  "allenai/OLMo-7B"
  "allenai/OLMo-7B-Instruct"

  # --- Phi models (Microsoft small models) ---
  "microsoft/phi-1_5"
  "microsoft/phi-2"
  "microsoft/phi-3-mini-4k-instruct"
  "microsoft/phi-3-mini-128k-instruct"

  # --- Zephyr ---
  "HuggingFaceH4/zephyr-7b-alpha"
  "HuggingFaceH4/zephyr-7b-beta"

  # --- SmolLM (tiny efficient models) ---
  "HuggingFaceTB/smollm-135m"
  "HuggingFaceTB/smollm-360m"
  "HuggingFaceTB/smollm-1.7b"

  # --- Gemma finetunes from 

community (examples) ---
  "TheBloke/gemma-7b-it-GGUF"
  "TheBloke/gemma-2b-it-GGUF"

  # (… add more community variants,

 GGUF conversions, quantized 

forks from TheBloke, etc.)
)

👉 This block already covers ~80 distinct repos.
When you add their quantized forks (e.g., TheBloke/xxx-GGUF versions), different parameter sizes, and finetuned versions (Alpaca, Vicuna, Hermes, Zephyr, etc.), you quickly exceed 200 models total.

Next steps

1. Copy the above into your script.
2. Run in chunks (e.g., 20–30 models at a time) to avoid network/timeouts.
3. Use quantized GGUF versions whenever possible (TheBloke repos are great).
4. Track progress in your JSON catalog (from my previous message).