Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-NVFP4

4.3K
7
license:apache-2.0
by
mconcat
Language Model
OTHER
27B params
New
4K downloads
Early-stage
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Edge AI:
Mobile
Laptop
Server
61GB+ RAM
Mobile
Laptop
Server
Quick Summary

AI model with specialized capabilities.

Device Compatibility

Mobile
4-6GB RAM
Laptop
16GB RAM
Server
GPU
Minimum Recommended
26GB+ RAM

Training Data Analysis

🟡 Average (4.3/10)

Researched training datasets used by Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-NVFP4 with quality assessment

Specialized For

general
science
multilingual
reasoning

Training Datasets (3)

common crawl
🔴 2.5/10
general
science
Key Strengths
  • •Scale and Accessibility: At 9.5+ petabytes, Common Crawl provides unprecedented scale for training d...
  • •Diversity: The dataset captures billions of web pages across multiple domains and content types, ena...
  • •Comprehensive Coverage: Despite limitations, Common Crawl attempts to represent the broader web acro...
Considerations
  • •Biased Coverage: The crawling process prioritizes frequently linked domains, making content from dig...
  • •Large-Scale Problematic Content: Contains significant amounts of hate speech, pornography, violent c...
wikipedia
🟡 5/10
science
multilingual
Key Strengths
  • •High-Quality Content: Wikipedia articles are subject to community review, fact-checking, and citatio...
  • •Multilingual Coverage: Available in 300+ languages, enabling training of models that understand and ...
  • •Structured Knowledge: Articles follow consistent formatting with clear sections, allowing models to ...
Considerations
  • •Language Inequality: Low-resource language editions have significantly lower quality, fewer articles...
  • •Biased Coverage: Reflects biases in contributor demographics; topics related to Western culture and ...
arxiv
🟡 5.5/10
science
reasoning
Key Strengths
  • •Scientific Authority: Peer-reviewed content from established repository
  • •Domain-Specific: Specialized vocabulary and concepts
  • •Mathematical Content: Includes complex equations and notation
Considerations
  • •Specialized: Primarily technical and mathematical content
  • •English-Heavy: Predominantly English-language papers

Explore our comprehensive training dataset analysis

View All Datasets

Code Examples

Step 1: Install the validated runtimebashvllm
pip install -U vllm==0.17.1
pip install transformers==5.3.0
Step 2: Apply the SM120 TMA patchbashvllm
UTILS_FILE=$(python -c "import vllm, os; print(os.path.join(os.path.dirname(vllm.__file__), 'model_executor/layers/fla/ops/utils.py'))")

sed -i 's/is_nvidia and torch.cuda.get_device_capability(0)\[0\] >= 9/is_nvidia and 9 <= torch.cuda.get_device_capability(0)[0] < 12/' "$UTILS_FILE"

echo "Patched: $UTILS_FILE"
Usagebashvllm
pip install vllm>=0.17.0

vllm serve mconcat/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-NVFP4 \
  --max-model-len 262144 \
  --reasoning-parser qwen3

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