LFM3-Omni-2B-MLX-4bit
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View details ↗DISCOVER & COMPARE
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LFM3-Omni-2B-MLX-8bit
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View details ↗LFM3-Omni-2B-MLX-bf16
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View details ↗LFM3-Omni-2B-ONNX
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View details ↗LFM3-VL-2B
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View details ↗LFM3-VL-2B-FP8
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View details ↗LFM3-VL-2B-GGUF
LiquidAIThe Q4_0 build in this repository is the Quantization-Aware Distillation (QAD) checkpoint, `LFM3-VL-2B-QAD-Q4_0.gguf`. It was trained to recover the accuracy lost to Q4_0 quantization.
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View details ↗LFM3-VL-2B-MLX-4bit
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View details ↗LFM3-VL-2B-MLX-5bit
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View details ↗LFM3-VL-2B-MLX-6bit
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View details ↗LFM3-VL-2B-MLX-8bit
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View details ↗LFM3-VL-2B-MLX-bf16
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View details ↗Liquid-Codec
LiquidAIThe repository includes the codec configuration and model implementation, so it can be loaded with `AutoModel`:
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View details ↗基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗my_first_lora_v9-lora
LiseTY基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗d_m13
lisin01基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗d_m15
lisin01基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗chinese_light_novel_40k
listenrwt基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗anime-whisper
litagin**Anime Whisper** は、特に日本語のアニメ調演技セリフのドメインに特化した日本語音声認識モデルです。 このモデルは kotoba-whisper-v2.0 をベースモデルとして、約5,300時間373万ファイルのアニメ調の音声・台本データセット Galgame_Speech_ASR_16kHz でファインチューニングしたものです。 特にアニメ演技音声ドメインに特化していますが、それ以外の音声でも、他のモデルにはない特徴や高い性能を持っています。 **このモデルは初期プロンプト(initial prompt) との相性が悪いようで、設定をするとハルシネーションが起きて質が極めて劣化します。初期プロンプトを設定せずにご使用ください。** **Using an initial prompt causes a decline in quality, with the model exhibiting hallucinations and significantly degraded performance. Please avoid using an initial prompt.**
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View details ↗MiniMax-H3
Lite01385基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗c4ai-command-r-plus-GGUF
LiteLLMs基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗CarbonVillain-en-10.7B-v3-GGUF
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View details ↗dolphin-2.9-llama3-8b-GGUF
LiteLLMs基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗No matching models
Adjust provider, category or input/output filters. New models are added continuously.
COST EXPLORER
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Adjust monthly token usage to compare estimated API costs.
1 M = 1 million tokens · USD
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Compares base text-token rates within the same channel
Uses collected, comparable USD-per-million-token rates. Snapshots exclude cache discounts, tiers, extra reasoning output and taxes. Check current source prices.
MODEL HANDBOOK
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How should API prices be compared?+
Separate input, output and cache rates, then estimate the same workload. Image, audio and video rates may use items, duration or resolution rather than tokens.
Do open weights mean free commercial use?+
Not necessarily. Check each version’s license, commercial restrictions and policies. Self-hosting also incurs compute and maintenance costs.
Is the top-ranked model the best for me?+
Rankings depend on tasks, versions and methods. Compare accuracy, latency, reliability and costs on your own workload.