sonic-coder/CPU-LLM-Inference
0
1# ------------------------------2# Torch-Compatible Model Definitions with Adjusted Descriptions3# ------------------------------4MODELS = {5 6 # 1B7 "K0D3IN/MiniCPM5-1B-heretic": {8 "repo_id": "K0D3IN/MiniCPM5-1B-heretic",9 "description": "K0D3IN/MiniCPM5-1B-heretic",10 "params_b": 1.011 },12 # 1.5B13 "Nemotron-Research-Reasoning-Qwen-1.5B": {14 "repo_id": "nvidia/Nemotron-Research-Reasoning-Qwen-1.5B",15 "description": "Nemotron-Research-Reasoning-Qwen-1.5B",16 "params_b": 1.517 },18 "Falcon-H1-1.5B-Instruct": {19 "repo_id": "tiiuae/Falcon-H1-1.5B-Instruct",20 "description": "Falcon‑H1 model with 1.5 B parameters, instruction‑tuned",21 "params_b": 1.522 },23 "Qwen2.5-Taiwan-1.5B-Instruct": {24 "repo_id": "benchang1110/Qwen2.5-Taiwan-1.5B-Instruct",25 "description": "Qwen2.5-Taiwan-1.5B-Instruct",26 "params_b": 1.527 },28 29 # 1.2B30 "LFM2-1.2B": {31 "repo_id": "LiquidAI/LFM2-1.2B",32 "description": "A 1.2B parameter hybrid language model from Liquid AI, designed for efficient on-device and edge AI deployment, outperforming larger models like Llama-2-7b-hf in specific tasks.",33 "params_b": 1.234 },35 36 # 1.1B37 "Taiwan-ELM-1_1B-Instruct": {38 "repo_id": "liswei/Taiwan-ELM-1_1B-Instruct",39 "description": "Taiwan-ELM-1_1B-Instruct",40 "params_b": 1.141 },42 43 # 1B44 "Llama-3.2-Taiwan-1B": {45 "repo_id": "lianghsun/Llama-3.2-Taiwan-1B",46 "description": "Llama-3.2-Taiwan base model with 1 B parameters",47 "params_b": 1.048 },49 "gemma-3-1b-it-heretic-abliterated-uncensored": {50 "repo_id": "DavidAU/gemma-3-1b-it-heretic-abliterated-uncensored",51 "description": "uncencored version of gemma-3-1b-it",52 "params_b": 1.053 },54 55 # 700M56 "LFM2-700M": {57 "repo_id": "LiquidAI/LFM2-700M",58 "description": "A 700M parameter model from the LFM2 family, designed for high efficiency on edge devices with a hybrid architecture of multiplicative gates and short convolutions.",59 "params_b": 0.760 },61 62 # 600M63 "Qwen3-0.6B": {64 "repo_id": "Qwen/Qwen3-0.6B",65 "description": "Dense causal language model with 0.6 B total parameters (0.44 B non-embedding), 28 transformer layers, 16 query heads & 8 KV heads, native 32 768-token context window, dual-mode generation, full multilingual & agentic capabilities.",66 "params_b": 0.667 },68 "Qwen3-0.6B-Taiwan": {69 "repo_id": "ShengweiPeng/Qwen3-0.6B-Taiwan",70 "description": "Qwen3-Taiwan model with 0.6 B parameters",71 "params_b": 0.672 },73 74 # 500M75 "Qwen2.5-0.5B-Taiwan-Instruct": {76 "repo_id": "ShengweiPeng/Qwen2.5-0.5B-Taiwan-Instruct",77 "description": "Qwen2.5-Taiwan model with 0.5 B parameters, instruction-tuned",78 "params_b": 0.579 },80 81 # 360M82 "SmolLM2-360M-Instruct": {83 "repo_id": "HuggingFaceTB/SmolLM2-360M-Instruct",84 "description": "Original SmolLM2‑360M Instruct",85 "params_b": 0.3686 },87 "SmolLM2-360M-Instruct-TaiwanChat": {88 "repo_id": "Luigi/SmolLM2-360M-Instruct-TaiwanChat",89 "description": "SmolLM2‑360M Instruct fine-tuned on TaiwanChat",90 "params_b": 0.3691 },92 93 # 350M94 "LFM2-350M": {95 "repo_id": "LiquidAI/LFM2-350M",96 "description": "A compact 350M parameter hybrid model optimized for edge and on-device applications, offering significantly faster training and inference speeds compared to models like Qwen3.",97 "params_b": 0.3598 },99 100 # 270M101 "parser_model_ner_gemma_v0.1": {102 "repo_id": "myfi/parser_model_ner_gemma_v0.1",103 "description": "A lightweight named‑entity‑like (NER) parser fine‑tuned from Google’s **Gemma‑3‑270M** model. The base Gemma‑3‑270M is a 270 M‑parameter, hyper‑efficient LLM designed for on‑device inference, supporting >140 languages, a 128 k‑token context window, and instruction‑following capabilities [2][7]. This variant is further trained on standard NER corpora (e.g., CoNLL‑2003, OntoNotes) to extract PERSON, ORG, LOC, and MISC entities with high precision while keeping the memory footprint low (≈240 MB VRAM in BF16 quantized form) [1]. It is released under the Apache‑2.0 license and can be used for fast, cost‑effective entity extraction in low‑resource environments.",104 "params_b": 0.27105 },106 "Gemma-3-Taiwan-270M-it": {107 "repo_id": "lianghsun/Gemma-3-Taiwan-270M-it",108 "description": "google/gemma-3-270m-it fintuned on Taiwan Chinese dataset",109 "params_b": 0.27110 },111 "gemma-3-270m-it": {112 "repo_id": "google/gemma-3-270m-it",113 "description": "Gemma‑3‑270M‑IT is a compact, 270‑million‑parameter language model fine‑tuned for Italian, offering fast and efficient on‑device text generation and comprehension in the Italian language.",114 "params_b": 0.27115 },116 "Taiwan-ELM-270M-Instruct": {117 "repo_id": "liswei/Taiwan-ELM-270M-Instruct",118 "description": "Taiwan-ELM-270M-Instruct",119 "params_b": 0.27120 },121 122 # 135M123 "SmolLM2-135M-multilingual-base": {124 "repo_id": "agentlans/SmolLM2-135M-multilingual-base",125 "description": "SmolLM2-135M-multilingual-base",126 "params_b": 0.135127 },128 "SmolLM-135M-Taiwan-Instruct-v1.0": {129 "repo_id": "benchang1110/SmolLM-135M-Taiwan-Instruct-v1.0",130 "description": "135-million-parameter F32 safetensors instruction-finetuned variant of SmolLM-135M-Taiwan, trained on the 416 k-example ChatTaiwan dataset for Traditional Chinese conversational and instruction-following tasks",131 "params_b": 0.135132 },133 "SmolLM2_135M_Grpo_Gsm8k": {134 "repo_id": "prithivMLmods/SmolLM2_135M_Grpo_Gsm8k",135 "description": "SmolLM2_135M_Grpo_Gsm8k",136 "params_b": 0.135137 },138 "SmolLM2-135M-Instruct": {139 "repo_id": "HuggingFaceTB/SmolLM2-135M-Instruct",140 "description": "Original SmolLM2‑135M Instruct",141 "params_b": 0.135142 },143 "SmolLM2-135M-Instruct-TaiwanChat": {144 "repo_id": "Luigi/SmolLM2-135M-Instruct-TaiwanChat",145 "description": "SmolLM2‑135M Instruct fine-tuned on TaiwanChat",146 "params_b": 0.135147 },148}