BenchmarksQwen9mo ago
Qwen/Qwen2.5-7B-Instruct-1M · Hugging Face
Qwen published benchmark or leaderboard evidence for Qwen2.5-7B-Instruct-1M.
View sourceQwen
The model has the following features: - Type: Causal Language Models - Training Stage: Pretraining & Post-training - Architecture: transformers with RoPE, SwiGLU, RMSNorm, and Attention QKV bias - Number of Parameters: 7.61B - Number of Paramaters (Non-Embedding): 6.53B - Number of Layers: 28 - Number of Attention...
Running this yourself: consumer gpu should be enough.
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26.2
Quality Score
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Arena ELO
8B
Parameters
1M
Context
This measures the amount of verifiable public evidence we have, not how capable the model is. A missing field means it has not been verified yet, not that its value is zero.
18 of 22 public signals
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175.4K
Downloads
381
Likes
Jan 2025
Released
5/5 signals
3/4 signals
4/5 signals
3/4 signals
3/4 signals
Parameters
8B
Training compute
Not reported
Dataset scale
Not reported
Base model
Not reported
Gaps we are still tracking
Metadata sources
Benchmarks
4
Open Source
1
Recent launch, pricing, benchmark, and API signals linked to this model or its provider.
Qwen published benchmark or leaderboard evidence for Qwen2.5-7B-Instruct-1M.
View sourceGAIA score 4.7 from rft-2
View sourceGAIA score 4.7 from rft-2
View sourceGAIA score 4.7 from rft-2
View sourceQwen2.5-7B-Instruct-1M is now available through local Ollama runtime. 32K context window listed. Qwen2.5 models are pretrained on Alibaba's latest large-scale dataset, encompassing up to 18 trillion tokens. The model supports up to 128K tokens and has multilingual support.
View sourceQwen2.5-7B-Instruct-1M is now available through local Ollama runtime. 32K context window listed. Qwen2.5 models are pretrained on Alibaba's latest large-scale dataset, encompassing up to 18 trillion tokens. The model supports up to 128K tokens and has multilingual support.
Qwen published benchmark or leaderboard evidence for Qwen2.5-7B-Instruct-1M.