MODEL PROFILE

LFM2.5-ExecuTorch

software-mansion · LFM2.5-ExecuTorch

This repository hosts the **lfm-2.5** models exported for the React Native ExecuTorch library as ExecuTorch `.pte` programs, ready to run on device. `2_6b/` contains the instruction-tuned LiquidAI/LFM2.5-2.6B checkpoint, exported for chat assistants with an 8,192-token context and 128-token prefill chunks. Its chat template strips thinking from every assistant message when rendering history, matching Qwen3 and keeping incremental KV-cache prompts monotonic. Generation still starts with ` `. `tokenizer_config.json` contains the corrected template. Both XNNPACK and MLX artifacts were executed on a physical Apple Silicon Mac, including generation and the maximum prefill chunk size. See `2_6b/validation.json` for smoke-test results and `2_6b/export_manifest.json` for checkpoint provenance and SHA-256 hashes. The MLX artifact requires the React Native ExecuTorch MLX runtime from the software-mansion-labs ExecuTorch fork. The embedding lookup uses a portable int4 kernel; the decoder runs in one MLX partition.

Visual QAlicense:other
Context windowNot disclosed
Input → OutputText、Image → Text
Model weightsNot verified
Profile updated2026/11/1 11:07:23
OVERVIEW

Model overview

This repository hosts the **lfm-2.5** models exported for the React Native ExecuTorch library as ExecuTorch `.pte` programs, ready to run on device. `2_6b/` contains the instruction-tuned LiquidAI/LFM2.5-2.6B checkpoint, exported for chat assistants with an 8,192-token context and 128-token prefill chunks. Its chat template strips thinking from every assistant message when rendering history, matching Qwen3 and keeping incremental KV-cache prompts monotonic. Generation still starts with ` `. `tokenizer_config.json` contains the corrected template. Both XNNPACK and MLX artifacts were executed on a physical Apple Silicon Mac, including generation and the maximum prefill chunk size. See `2_6b/validation.json` for smoke-test results and `2_6b/export_manifest.json` for checkpoint provenance and SHA-256 hashes. The MLX artifact requires the React Native ExecuTorch MLX runtime from the software-mansion-labs ExecuTorch fork. The embedding lookup uses a portable int4 kernel; the decoder runs in one MLX partition.

Key features

特点摘要待依据来源补齐,不凭模型名称推断能力。

Limitations & considerations

Sources are insufficient to establish specific limitations. Verify task performance, latency, data policies, licensing and version lifecycle.

PRICING & COST

Source prices & cost analysis

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Estimate monthly base token costs

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INDEPENDENT BENCHMARKS

Public benchmarks & run configuration

暂无已采集的可核验公开评分。模型未上榜不代表能力较低。

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Frequently asked questions

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Sources & traceability

许可证:other。模型介绍与第三方评测均以原来源的具体版本和方法为准。