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Model: prithivMLmods/LFM2.5-350M-F32-GGUF
Source: Original Platform

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README.md

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+---
+license: apache-2.0
+language:
+- en
+base_model:
+- LiquidAI/LFM2.5-350M
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- text-generation-inference
+- edge
+- llama.cpp
+---
+
+# **LFM2.5-350M-F32-GGUF**
+
+> LiquidAI/LFM2.5-350M is an ultra-compact 350M-parameter model from Liquid AI's LFM2.5 series, leveraging a hybrid architecture with 10 double-gated Linear Input-Varying (LIV) convolution blocks for efficient sequence processing and 6 Grouped Query Attention (GQA) blocks for precise long-range context handling, trained on 28T tokens (80K:1 token-to-parameter ratio) with extensive reinforcement learning to excel at agentic tasks like tool calling, data extraction, structured JSON outputs, and multi-step reasoning—outperforming models twice its size on GPQA Diamond, MMLU-Pro, IFEval, BFCLv3/4, and CaseReportBench while achieving blazing-fast inference (313 tok/s on AMD CPUs, 188 tok/s on Snapdragon Gen4). Optimized for edge deployment under 1GB memory with native llama.cpp/MLX/vLLM support, it represents peak "intelligence density" for running reliable agent loops on mobiles, IoT devices, and low-power servers where traditional Transformers fail, making high-quality structured data processing and function calling viable at consumer-grade hardware scales.
+
+## Model Files
+
+   File Name | Quant Type | File Size | File Link |
+ | - | - | - | - |
+ | LFM2.5-350M.BF16.gguf | BF16 | 711 MB | [Download](https://huggingface.co/prithivMLmods/LFM2.5-350M-F32-GGUF/blob/main/GGUF/LFM2.5-350M.BF16.gguf) |
+ | LFM2.5-350M.F16.gguf | F16 | 711 MB | [Download](https://huggingface.co/prithivMLmods/LFM2.5-350M-F32-GGUF/blob/main/GGUF/LFM2.5-350M.F16.gguf) |
+ | LFM2.5-350M.F32.gguf | F32 | 1.42 GB | [Download](https://huggingface.co/prithivMLmods/LFM2.5-350M-F32-GGUF/blob/main/GGUF/LFM2.5-350M.F32.gguf) |
+ | LFM2.5-350M.Q8_0.gguf | Q8_0 | 379 MB | [Download](https://huggingface.co/prithivMLmods/LFM2.5-350M-F32-GGUF/blob/main/GGUF/LFM2.5-350M.Q8_0.gguf) |
+
+## Quants Usage 
+
+(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
+
+Here is a handy graph by ikawrakow comparing some lower-quality quant
+types (lower is better):
+
+![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png)