From f83cbc25169ffb262d8ee289573411ed6f5cd5ef Mon Sep 17 00:00:00 2001 From: Yi Zhong <207368749+vincentzed@users.noreply.github.com> Date: Thu, 25 Jun 2026 17:27:57 -0700 Subject: [PATCH] Add LFM2.5-230M to the LFM2.5 cookbook (#29321) --- .../autoregressive/LiquidAI/LFM2.5.mdx | 15 +++++-- .../supported-models/generative_models.mdx | 2 +- .../configs/LiquidAI/lfm2.5-benchmarks.jsx | 30 +++++++++++++ .../src/snippets/configs/LiquidAI/lfm2.5.jsx | 43 +++++++++++++++++++ 4 files changed, 86 insertions(+), 4 deletions(-) diff --git a/docs_new/cookbook/autoregressive/LiquidAI/LFM2.5.mdx b/docs_new/cookbook/autoregressive/LiquidAI/LFM2.5.mdx index 5d2102aad..bedae1ffb 100644 --- a/docs_new/cookbook/autoregressive/LiquidAI/LFM2.5.mdx +++ b/docs_new/cookbook/autoregressive/LiquidAI/LFM2.5.mdx @@ -156,6 +156,12 @@ This minimal hybrid layout was selected by a hardware-in-the-loop architecture s 32K Compact instruct, structured output + + LFM2.5-230M + 230M (dense) + 32K + Most compact; data extraction, structured output + LFM2.5-1.2B-JP-202606 1.17B (dense) @@ -189,7 +195,7 @@ This minimal hybrid layout was selected by a hardware-in-the-loop architecture s -The Deploy panel above covers the seven serving variants; **LFM2.5-1.2B-JP** (original — launch without `--tool-call-parser`) and the **Base** repos (pre-trained only, no post-training — see [§3.5](#35-base-checkpoints)) launch the same way with the model path swapped. +The Deploy panel above covers the eight serving variants; **LFM2.5-1.2B-JP** (original — launch without `--tool-call-parser`) and the **Base** repos (pre-trained only, no post-training — see [§3.5](#35-base-checkpoints)) launch the same way with the model path swapped. **Choosing a variant:** @@ -197,6 +203,7 @@ The Deploy panel above covers the seven serving variants; **LFM2.5-1.2B-JP** (or - **1.2B-Thinking** — reasoning-heavy tasks: math, tool use, programming. - **1.2B-Instruct** — the recommended pick for chat and creative writing. - **350M** — tool use, data extraction, and structured output; not recommended for math, code, or creative writing. +- **230M** — the most compact checkpoint; same use as the 350M, not for math, code, or creative writing. **License:** [LFM Open License v1.0](https://huggingface.co/LiquidAI/LFM2.5-8B-A1B/blob/main/LICENSE). @@ -228,6 +235,7 @@ The Deploy panel above covers the seven serving variants; **LFM2.5-1.2B-JP** (or LFM2.5-1.2B-Instruct0.1{`{"top_k": 50, "repetition_penalty": 1.05}`} LFM2.5-1.2B-Thinking0.05{`{"top_k": 50, "repetition_penalty": 1.05}`} LFM2.5-350M0.1{`{"top_k": 50, "repetition_penalty": 1.05}`} + LFM2.5-230M0.1{`{"top_k": 50, "repetition_penalty": 1.05}`} LFM2.5-1.2B-JP-2026060.1{`{"top_k": 50, "repetition_penalty": 1.05}`} LFM2.5-1.2B-JP0.3{`{"min_p": 0.15, "repetition_penalty": 1.05}`} LFM2.5-VL-1.6B (text)0.1{`{"min_p": 0.15, "repetition_penalty": 1.05}`} @@ -253,6 +261,7 @@ SAMPLING = { "LiquidAI/LFM2.5-1.2B-Instruct": dict(temperature=0.1, extra_body={"top_k": 50, "repetition_penalty": 1.05}), "LiquidAI/LFM2.5-1.2B-Thinking": dict(temperature=0.05, extra_body={"top_k": 50, "repetition_penalty": 1.05}), "LiquidAI/LFM2.5-350M": dict(temperature=0.1, extra_body={"top_k": 50, "repetition_penalty": 1.05}), + "LiquidAI/LFM2.5-230M": dict(temperature=0.1, extra_body={"top_k": 50, "repetition_penalty": 1.05}), "LiquidAI/LFM2.5-1.2B-JP-202606": dict(temperature=0.1, extra_body={"top_k": 50, "repetition_penalty": 1.05}), "LiquidAI/LFM2.5-VL-1.6B": dict(temperature=0.1, extra_body={"min_p": 0.15, "repetition_penalty": 1.05}), "LiquidAI/LFM2.5-VL-450M": dict(temperature=0.1, extra_body={"min_p": 0.15, "repetition_penalty": 1.05}), @@ -312,7 +321,7 @@ for call in resp.choices[0].message.tool_calls or []: print(call.function.name, call.function.arguments) ``` -Tool calling is supported on 8B-A1B, 1.2B-Thinking, 1.2B-Instruct, 350M, 1.2B-JP-202606, VL-1.6B, and VL-450M. For the **VL** models it is text-turn-only — do not combine an image and tools in the same turn. +Tool calling is supported on 8B-A1B, 1.2B-Thinking, 1.2B-Instruct, 350M, 230M, 1.2B-JP-202606, VL-1.6B, and VL-450M. For the **VL** models it is text-turn-only — do not combine an image and tools in the same turn. ### 3.4 Vision Input @@ -335,7 +344,7 @@ print(resp.choices[0].message.content) ### 3.5 Base Checkpoints -Each size ships a pre-trained Base repo — [LFM2.5-1.2B-Base](https://huggingface.co/LiquidAI/LFM2.5-1.2B-Base), [LFM2.5-350M-Base](https://huggingface.co/LiquidAI/LFM2.5-350M-Base), and [LFM2.5-8B-A1B-Base](https://huggingface.co/LiquidAI/LFM2.5-8B-A1B-Base) — intended for fine-tuning and continued pre-training. +Each size ships a pre-trained Base repo — [LFM2.5-230M-Base](https://huggingface.co/LiquidAI/LFM2.5-230M-Base), [LFM2.5-1.2B-Base](https://huggingface.co/LiquidAI/LFM2.5-1.2B-Base), [LFM2.5-350M-Base](https://huggingface.co/LiquidAI/LFM2.5-350M-Base), and [LFM2.5-8B-A1B-Base](https://huggingface.co/LiquidAI/LFM2.5-8B-A1B-Base) — intended for fine-tuning and continued pre-training. The repos ship a ChatML-style chat template, so `chat.completions` requests format normally. The checkpoints have no post-training, though — don't expect instruction following. For raw text continuation: diff --git a/docs_new/docs/supported-models/generative_models.mdx b/docs_new/docs/supported-models/generative_models.mdx index 2bbcb5e74..464ba1572 100644 --- a/docs_new/docs/supported-models/generative_models.mdx +++ b/docs_new/docs/supported-models/generative_models.mdx @@ -254,7 +254,7 @@ in the GitHub search bar. Arcee's foundational MoE Trinity family of models, open weights under Apache 2.0. - LFM2 (350M, 1.2B) + LFM2 (230M, 350M, 1.2B) LiquidAI/LFM2.5-1.2B-Instruct Liquid AI's hybrid language model combining gated short convolutions with a small number of grouped query attention (GQA) blocks. diff --git a/docs_new/src/snippets/configs/LiquidAI/lfm2.5-benchmarks.jsx b/docs_new/src/snippets/configs/LiquidAI/lfm2.5-benchmarks.jsx index a4bfd49ed..cd5f0ab1f 100644 --- a/docs_new/src/snippets/configs/LiquidAI/lfm2.5-benchmarks.jsx +++ b/docs_new/src/snippets/configs/LiquidAI/lfm2.5-benchmarks.jsx @@ -48,6 +48,16 @@ export const benchmarks = [ ttft_ms: 476.85, tpot_ms: 4.26, tokens_per_sec_per_gpu: 18745.3 }, ], }, + { + match: { hw: "h100", variant: "230m", quant: "bf16", strategy: "default", nodes: "single" }, + sglang_version: "0.0.0.dev1+g631db6c75", + speed: [ + { workload: { dataset: "random", isl: 1024, osl: 1024, max_concurrency: 1, num_prompts: 10 }, + ttft_ms: 23.74, tpot_ms: 1.77, tokens_per_sec_per_gpu: 546.07 }, + { workload: { dataset: "random", isl: 1024, osl: 1024, max_concurrency: 100, num_prompts: 1000 }, + ttft_ms: 1128.14, tpot_ms: 4.54, tokens_per_sec_per_gpu: 14280.32 }, + ], + }, { match: { hw: "h100", variant: "jp", quant: "bf16", strategy: "default", nodes: "single" }, sglang_version: "0.0.0.dev1+g631db6c75", @@ -121,6 +131,16 @@ export const benchmarks = [ ttft_ms: 880.23, tpot_ms: 4.37, tokens_per_sec_per_gpu: 15765.2 }, ], }, + { + match: { hw: "h200", variant: "230m", quant: "bf16", strategy: "default", nodes: "single" }, + sglang_version: "0.0.0.dev1+g631db6c75", + speed: [ + { workload: { dataset: "random", isl: 1024, osl: 1024, max_concurrency: 1, num_prompts: 10 }, + ttft_ms: 18.74, tpot_ms: 1.74, tokens_per_sec_per_gpu: 561.43 }, + { workload: { dataset: "random", isl: 1024, osl: 1024, max_concurrency: 100, num_prompts: 1000 }, + ttft_ms: 458.07, tpot_ms: 4.47, tokens_per_sec_per_gpu: 17892.27 }, + ], + }, { match: { hw: "h200", variant: "jp", quant: "bf16", strategy: "default", nodes: "single" }, sglang_version: "0.0.0.dev1+g631db6c75", @@ -194,6 +214,16 @@ export const benchmarks = [ ttft_ms: 1177.6, tpot_ms: 1.92, tokens_per_sec_per_gpu: 22636.7 }, ], }, + { + match: { hw: "b200", variant: "230m", quant: "bf16", strategy: "default", nodes: "single" }, + sglang_version: "0.0.0.dev1+g631db6c75", + speed: [ + { workload: { dataset: "random", isl: 1024, osl: 1024, max_concurrency: 1, num_prompts: 10 }, + ttft_ms: 12.28, tpot_ms: 0.84, tokens_per_sec_per_gpu: 1158.08 }, + { workload: { dataset: "random", isl: 1024, osl: 1024, max_concurrency: 100, num_prompts: 1000 }, + ttft_ms: 1550.61, tpot_ms: 1.93, tokens_per_sec_per_gpu: 19205.62 }, + ], + }, { match: { hw: "b200", variant: "jp", quant: "bf16", strategy: "default", nodes: "single" }, sglang_version: "0.0.0.dev1+g631db6c75", diff --git a/docs_new/src/snippets/configs/LiquidAI/lfm2.5.jsx b/docs_new/src/snippets/configs/LiquidAI/lfm2.5.jsx index dc074a5ca..46c7a07ba 100644 --- a/docs_new/src/snippets/configs/LiquidAI/lfm2.5.jsx +++ b/docs_new/src/snippets/configs/LiquidAI/lfm2.5.jsx @@ -18,6 +18,7 @@ export const config = { { id: "instruct", label: "1.2B Instruct", subtitle: "1.17B dense" }, { id: "thinking", label: "1.2B Thinking", subtitle: "1.17B · reasoning" }, { id: "350m", label: "350M", subtitle: "dense" }, + { id: "230m", label: "230M", subtitle: "dense · compact" }, { id: "jp", label: "1.2B JP", subtitle: "Japanese" }, { id: "vl", label: "VL 1.6B", subtitle: "vision" }, { id: "vl-450m", label: "VL 450M", subtitle: "vision · compact" }, @@ -37,6 +38,7 @@ export const config = { "instruct|bf16": "LiquidAI/LFM2.5-1.2B-Instruct", "thinking|bf16": "LiquidAI/LFM2.5-1.2B-Thinking", "350m|bf16": "LiquidAI/LFM2.5-350M", + "230m|bf16": "LiquidAI/LFM2.5-230M", "jp|bf16": "LiquidAI/LFM2.5-1.2B-JP-202606", "vl|bf16": "LiquidAI/LFM2.5-VL-1.6B", "vl-450m|bf16": "LiquidAI/LFM2.5-VL-450M", @@ -122,6 +124,7 @@ sgl-eval run aime25 \\ thinking: { mmlu_pct: 63.2, gsm8k_pct: 86.35, gpqa_pct: 39.08, aime25_pct: 27.08 }, instruct: { mmlu_pct: 60.33, gsm8k_pct: 75.13, gpqa_pct: 34.41, aime25_pct: 9.58 }, "350m": { mmlu_pct: 40.69, gsm8k_pct: 30.63, gpqa_pct: 28.35 }, + "230m": { mmlu_pct: 38.45, gsm8k_pct: 31.84, gpqa_pct: 27.78 }, vl: { mmmu_pct: 39.12 }, "vl-450m": { mmmu_pct: 30.56 }, }, @@ -208,6 +211,19 @@ sgl-eval run aime25 \\ "--port {{PORT}}", ], }, + { + match: { hw: "h100", variant: "230m", quant: "bf16", strategy: "default", nodes: "single" }, + verified: true, + env: [], + flags: [ + "--trust-remote-code", + "--model-path {{MODEL_NAME}}", + "--tp 1", + "--tool-call-parser lfm2", + "--host {{HOST_IP}}", + "--port {{PORT}}", + ], + }, { match: { hw: "h100", variant: "jp", quant: "bf16", strategy: "default", nodes: "single" }, verified: true, @@ -312,6 +328,19 @@ sgl-eval run aime25 \\ "--port {{PORT}}", ], }, + { + match: { hw: "h200", variant: "230m", quant: "bf16", strategy: "default", nodes: "single" }, + verified: true, + env: [], + flags: [ + "--trust-remote-code", + "--model-path {{MODEL_NAME}}", + "--tp 1", + "--tool-call-parser lfm2", + "--host {{HOST_IP}}", + "--port {{PORT}}", + ], + }, { match: { hw: "h200", variant: "jp", quant: "bf16", strategy: "default", nodes: "single" }, verified: true, @@ -422,6 +451,20 @@ sgl-eval run aime25 \\ "--port {{PORT}}", ], }, + { + match: { hw: "b200", variant: "230m", quant: "bf16", strategy: "default", nodes: "single" }, + verified: true, + env: [], + flags: [ + "--trust-remote-code", + "--model-path {{MODEL_NAME}}", + "--tp 1", + "--attention-backend trtllm_mha", + "--tool-call-parser lfm2", + "--host {{HOST_IP}}", + "--port {{PORT}}", + ], + }, { match: { hw: "b200", variant: "jp", quant: "bf16", strategy: "default", nodes: "single" }, verified: true,