appvoid/cloud-2

TEXT GENERATIONConcurrent Unit Cost:1Model Size:0.35BQuant:BF16Context Size:32kPublished:Jul 6, 2026Architecture:Transformer Featherless Exclusive Cold

appvoid/cloud-2 is a 0.35 billion parameter language model created by appvoid through a TIES merge of LiquidAI/LFM2.5-350M, MihaiPopa-1/LFM2.5-350M-heretic, and squ11z1/claude-oss-350m. This model leverages the strengths of its constituent models, offering a compact yet capable solution for general language tasks. With a context length of 32768 tokens, it is suitable for applications requiring processing of moderately long inputs.

Loading preview...

Overview

appvoid/cloud-2 is a compact 0.35 billion parameter language model, developed by appvoid using the TIES (Trimmed, Iterative, and Self-consistent) merge method. This model integrates the capabilities of several pre-trained language models, specifically:

  • LiquidAI/LFM2.5-350M (serving as the base model)
  • MihaiPopa-1/LFM2.5-350M-heretic
  • squ11z1/claude-oss-350m

Merge Details

The merge process utilized a specific configuration, applying a density of 0.5 and a weight of 0.5 to both MihaiPopa-1/LFM2.5-350M-heretic and squ11z1/claude-oss-350m models. The base model, LiquidAI/LFM2.5-350M, provided the foundational architecture. The TIES method was configured with normalize: false and int8_mask: true, and the model was produced with dtype: float16.

Key Characteristics

  • Efficient Size: At 0.35 billion parameters, it offers a balance between performance and computational efficiency.
  • Extended Context Window: Supports a context length of 32768 tokens, allowing for processing of substantial input texts.
  • Merged Intelligence: Combines the learned representations from multiple specialized models, potentially enhancing its general language understanding and generation capabilities.

Potential Use Cases

This model is well-suited for applications where a smaller footprint is desired without significantly compromising on context handling. It can be considered for:

  • Text summarization of moderately long documents.
  • Chatbot implementations requiring a decent memory of conversation history.
  • Lightweight content generation tasks.
  • Prototyping and development where quick iteration and resource efficiency are important.

Finetunes
2 models