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6 changes: 6 additions & 0 deletions lfm/models/complete-library.mdx
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---
title: "Liquid Foundation Models"
description: "Browse every Liquid Foundation Model (LFM) for local AI: text, vision, audio, and decision models plus Liquid Nanos, with GGUF, MLX, and ONNX formats."

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Did you really mean 'Nanos'?
---

<div className="capabilities">

Across the library:

- Context length depends on the model: 32K tokens for most text, vision, audio, and omni models, 256K for LFM3-1.7B, 128K for LFM3-VL-2B, LFM2.5-8B-A1B, d1-2B, and d1-omni-2B, 131K for LFM2.5-24B-A2B, 8K for the LFM2.5 encoders, and 16K for d1-omni-600M. Each model page lists its exact limit.

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- Text and vision models run with [Transformers](/deployment/gpu-inference/transformers), [llama.cpp](/deployment/on-device/llama-cpp), [vLLM](/deployment/gpu-inference/vllm), [SGLang](/deployment/gpu-inference/sglang), [MLX](/deployment/on-device/mlx), [Ollama](/deployment/on-device/ollama), and [Atomic Chat](/deployment/on-device/atomic-chat). Audio, omni, retrieval, encoder, and decision models support fewer runtimes (d1-omni-600M: Transformers, llama.cpp, and mlx-vlm); check each model page.

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Did you really mean 'runtimes'?
- Most open-weight models can be fine-tuned with [Liquid Finetune](/lfm/fine-tuning/liquid-finetune), [TRL](/lfm/fine-tuning/trl), or [Unsloth](/lfm/fine-tuning/unsloth). The **Trainable?** column in the matrix below lists the documented path for each model.

</div>
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Interleaved audio/text models for TTS, ASR, and voice chat.
</Card>

<Card title="Omni Models" icon="circle-nodes" href="/lfm/models/omni-models">

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Text, image, video, and audio input with text or speech output in one model.
</Card>

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Purpose-built models for classification, routing, and scoring.
</Card>

<Card title="Liquid Nanos" icon="sparkles" href="/lfm/models/liquid-nanos">

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Task-specific models for extraction, summarization, RAG, and translation.
</Card>

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- **GGUF** — Best for local CPU/GPU inference on any platform. Use with [llama.cpp](/deployment/on-device/llama-cpp), [LM Studio](/deployment/on-device/lm-studio), [Ollama](/deployment/on-device/ollama), or [Atomic Chat](/deployment/on-device/atomic-chat). Every open-weight model in the matrix has a `LiquidAI/<model>-GGUF` repository.
- **MLX** — Best for Mac users with Apple Silicon. Leverages unified memory for fast inference via [MLX](/deployment/on-device/mlx) or [Atomic Chat](/deployment/on-device/atomic-chat). Many models have official `LiquidAI/<model>-MLX-<bits>` repositories; the others are community conversions on [mlx-community](https://huggingface.co/mlx-community/collections?search=LFM).
- **ONNX** — Best for production deployments and edge devices. Cross-platform with ONNX Runtime across CPUs, GPUs, and accelerators. Available as `LiquidAI/<model>-ONNX` for the LFM3 models and the main LFM2.5 text, vision, and audio models, and for some LFM2 models through [onnx-community](https://huggingface.co/onnx-community); see the matrix.

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### Quantization

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## Complete Model Matrix

<div className="model-matrix">

<p className="model-matrix-scroll-hint">Swipe horizontally to see all formats.</p>

| Model | HF | GGUF | MLX | ONNX | Drafter | Trainable? |
| ----- | ----- | ----- | ----- | ----- | ----- | ----- |
| **Text-to-text Models** | | | | | | |
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| [d1-ultra](/lfm/models/d1#models) | API | — | — | — | — | No |
| [d1.1](/lfm/models/d1) | API | — | — | — | — | No |

</div>

<Note>Looking for an older model? Deprecated models and their recommended replacements are listed on the [Deprecations](/lfm/help/deprecations) page.</Note>
94 changes: 94 additions & 0 deletions style.css
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Expand Up @@ -1203,6 +1203,100 @@ table:not(:has(thead th:nth-child(6))) a:hover {
text-decoration-line: underline !important;
}

/* Complete model matrix: keep the desktop layout; adapt only on mobile. */
.model-matrix-scroll-hint {
display: none;
}

@media (max-width: 767px) {
.model-matrix {
container-type: inline-size;
min-width: 0;
max-width: 100%;
}

.model-matrix [data-table-wrapper] {
--page-padding: 0px;
}

.model-matrix [data-component-part="scroll-area-viewport"] {
overscroll-behavior-x: contain;
scroll-padding-left: calc(12rem + var(--page-padding, 0px));
scroll-padding-right: var(--page-padding, 0px);
}

.model-matrix table:has(thead th:nth-child(7):not(:nth-child(8))) {
min-width: 48rem !important;
border-collapse: separate !important;
}

.model-matrix table:has(thead th:nth-child(7):not(:nth-child(8))) th,
.model-matrix table:has(thead th:nth-child(7):not(:nth-child(8))) td {
min-width: 0 !important;
width: auto !important;
overflow: visible !important;
text-overflow: clip !important;
white-space: normal !important;
overflow-wrap: anywhere;
text-align: center !important;
padding: 0.5rem !important;
background: var(--lfm-bg);
}

.model-matrix table:has(thead th:nth-child(7):not(:nth-child(8))) tbody td {
padding: 0 0.5rem !important;
border-bottom: 1px solid var(--lfm-border);
}

.model-matrix table:has(thead th:nth-child(7):not(:nth-child(8))) th:first-child,
.model-matrix table:has(thead th:nth-child(7):not(:nth-child(8))) td:first-child {
position: sticky;
left: var(--page-padding, 0px);
z-index: 20;
width: 12rem !important;
padding: 0.75rem 1rem !important;
text-align: left !important;
border-right: 1px solid var(--lfm-border);
}

.model-matrix table:has(thead th:nth-child(7):not(:nth-child(8))) tbody td:first-child {
padding: 0 1rem !important;
}

.model-matrix table:has(thead th:nth-child(7):not(:nth-child(8))) th:nth-child(n+2):nth-child(-n+6),
.model-matrix table:has(thead th:nth-child(7):not(:nth-child(8))) td:nth-child(n+2):nth-child(-n+6) {
width: 4rem !important;
}

.model-matrix table:has(thead th:nth-child(7):not(:nth-child(8))) td:nth-child(n+2):nth-child(-n+6) a {
display: inline-flex;
align-items: center;
justify-content: center;
min-width: 44px;
min-height: 44px;
padding: 0 !important;
}

.model-matrix table:has(thead th:nth-child(7):not(:nth-child(8))) tbody tr:has(td:first-child strong) td {
padding: 0.5rem 1rem !important;
background: var(--lfm-surface);
}

/* Keep training text beside the pinned column on narrow screens. */
@container (max-width: 28rem) {
.model-matrix table:has(thead th:nth-child(7):not(:nth-child(8))) th:last-child,
.model-matrix table:has(thead th:nth-child(7):not(:nth-child(8))) td:last-child {
padding-left: max(0.5rem, calc(28rem - 100cqw)) !important;
}
}

.model-matrix-scroll-hint {
display: block;
color: var(--lfm-muted);
font-size: var(--lfm-fs-sm);
}
}

/* Code block styling - ensure visible background contrast */
/* Inline code */
/* Inline code */
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95 changes: 95 additions & 0 deletions styles.js
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Expand Up @@ -1208,6 +1208,101 @@
text-decoration-line: underline !important;
}

/* Complete model matrix: keep the desktop layout; adapt only on mobile. */
.model-matrix-scroll-hint {
display: none;
}

@media (max-width: 767px) {
.model-matrix {
container-type: inline-size;
min-width: 0;
max-width: 100%;
}

.model-matrix [data-table-wrapper] {
--page-padding: 0px;
}

.model-matrix [data-component-part="scroll-area-viewport"] {
overscroll-behavior-x: contain;
scroll-padding-left: calc(12rem + var(--page-padding, 0px));
scroll-padding-right: var(--page-padding, 0px);
}

.model-matrix table:has(thead th:nth-child(7):not(:nth-child(8))) {
min-width: 48rem !important;
border-collapse: separate !important;
}

.model-matrix table:has(thead th:nth-child(7):not(:nth-child(8))) th,
.model-matrix table:has(thead th:nth-child(7):not(:nth-child(8))) td {
min-width: 0 !important;
width: auto !important;
overflow: visible !important;
text-overflow: clip !important;
white-space: normal !important;
overflow-wrap: anywhere;
text-align: center !important;
padding: 0.5rem !important;
background: var(--lfm-bg);
}

.model-matrix table:has(thead th:nth-child(7):not(:nth-child(8))) tbody td {
padding: 0 0.5rem !important;
border-bottom: 1px solid var(--lfm-border);
}

.model-matrix table:has(thead th:nth-child(7):not(:nth-child(8))) th:first-child,
.model-matrix table:has(thead th:nth-child(7):not(:nth-child(8))) td:first-child {
position: sticky;
left: var(--page-padding, 0px);
z-index: 20;
width: 12rem !important;
padding: 0.75rem 1rem !important;
text-align: left !important;
border-right: 1px solid var(--lfm-border);
}

.model-matrix table:has(thead th:nth-child(7):not(:nth-child(8))) tbody td:first-child {
padding: 0 1rem !important;
}

.model-matrix table:has(thead th:nth-child(7):not(:nth-child(8))) th:nth-child(n+2):nth-child(-n+6),
.model-matrix table:has(thead th:nth-child(7):not(:nth-child(8))) td:nth-child(n+2):nth-child(-n+6) {
width: 4rem !important;
}

.model-matrix table:has(thead th:nth-child(7):not(:nth-child(8))) td:nth-child(n+2):nth-child(-n+6) a {
display: inline-flex;
align-items: center;
justify-content: center;
min-width: 44px;
min-height: 44px;
padding: 0 !important;
}

.model-matrix table:has(thead th:nth-child(7):not(:nth-child(8))) tbody tr:has(td:first-child strong) td {
padding: 0.5rem 1rem !important;
background: var(--lfm-surface);
}

/* Keep training text beside the pinned column on narrow screens. */
@container (max-width: 28rem) {
.model-matrix table:has(thead th:nth-child(7):not(:nth-child(8))) th:last-child,
.model-matrix table:has(thead th:nth-child(7):not(:nth-child(8))) td:last-child {
padding-left: max(0.5rem, calc(28rem - 100cqw)) !important;
}
}

.model-matrix-scroll-hint {
display: block;
color: var(--lfm-muted);
font-size: var(--lfm-fs-sm);
}
}


/* Code block styling - ensure visible background contrast */
/* Inline code */
/* Inline code */
Expand Down
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