• @Qwen: QwQ-32B
• @Qwen: Qwen2.5-32B
• @ArliAI: QwQ-32B-ArliAI-RpR-v1
• @deepcogito: cogito-v1-preview-qwen-32B
• @trashpanda-org: QwQ-32B-Snowdrop-v0

SnowDrogito-RpR-32B

Overview

Setup for Reasoning and ChatML

• ChatML Formatting: Use ChatML with <|im_start|>role\ncontent<|im_end|>\n (e.g., <|im_start|>user\nHello!<|im_end|>\n).
• Reasoning Settings: Set "include names" to "never." Start reply with <think>\n to enable reasoning.
• Sampler Settings: From Snowdrop: Try temperature 0.9, min_p 0.05, top_a 0.3, TFS 0.75, repetition_penalty 1.03, DRY if available.
• My Settings: Response (tokens): 2048 Context (tokens): 40960 Temperature: 3.25 Top P: 0.98 Min P: 0.04 Top nsigna: 2.5 Repetition Penalty: 1.03 (XTC) Threshold: 0.3 (XTC) Probability: 0.3 Dry Multiplier: 0.8 Dry Base: 1.75 Dry Allowed Length: 4 Dry Penalty Range: 1024

Performance

• Perplexity under identical conditions (IQ4_XS, 40,960 context, Q8_0 KV cache, on a 150K-token chat dataset) SnowDrogito-RpR-32B vs QwQ-32B-Snowdrop-v0:

  4.5597 ± 0.02554  
  4.6779 ± 0.02671

• IQ4_xs fits 40960 context 24GB VRAM using Q8 KV Cache with full GPU offload.

Model Details

• Base Model: Qwen/Qwen2.5-32B
• Architecture: Qwen 2.5 (32B parameters)
• Context Length: 40,960 tokens

Merge Configuration

models:
  - model: trashpanda-org/QwQ-32B-Snowdrop-v0
    parameters:
      weight: 0.75
      density: 0.5
  - model: deepcogito/cogito-v1-preview-qwen-32B
    parameters:
      weight: 0.15
      density: 0.5
  - model: ArliAI/QwQ-32B-ArliAI-RpR-v1
    parameters:
      weight: 0.1
      density: 0.5
merge_method: ties
base_model: Qwen/Qwen2.5-32B
parameters:
  weight: 0.9
  density: 0.9
  normalize: true
  int8_mask: true
tokenizer_source: Qwen/Qwen2.5-32B-Instruct
dtype: bfloat16

Acknowledgments

• mergekit for merging.
• llama.cpp for quantization.
• Original model creators: Qwen, trashpanda-org, deepcogito, ArliAI.