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chore(model): add qwen2.5-7b-nerd-uncensored-v1.7 to the gallery (#4171)
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
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- filename: Athene-V2-Chat-Q4_K_M.gguf
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- filename: Athene-V2-Chat-Q4_K_M.gguf
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sha256: bda8b784ad55982891e5aa69b08ce4030c91a2e28ad9c4c35284d45d3c7aeb16
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sha256: bda8b784ad55982891e5aa69b08ce4030c91a2e28ad9c4c35284d45d3c7aeb16
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uri: huggingface://bartowski/Athene-V2-Chat-GGUF/Athene-V2-Chat-Q4_K_M.gguf
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uri: huggingface://bartowski/Athene-V2-Chat-GGUF/Athene-V2-Chat-Q4_K_M.gguf
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- !!merge <<: *qwen25
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name: "qwen2.5-7b-nerd-uncensored-v1.7"
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urls:
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- https://huggingface.co/jeffmeloy/Qwen2.5-7B-nerd-uncensored-v1.7
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- https://huggingface.co/mradermacher/Qwen2.5-7B-nerd-uncensored-v1.7-GGUF
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description: |
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Model created by analyzing and selecting the optimal layers from other Qwen2.5-7B models based on their dimensional utilization efficiency, measured by the Normalized Effective Rank (NER). Computed like:
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Input: Weight matrix for each model layer
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Compute singular values σᵢ where σᵢ ≥ 0 # σᵢ represents the importance of each dimension
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Filter values above numerical threshold (>1e-12)
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Sum all singular values: S = Σσᵢ # S acts as normalization factor
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Create probability distribution: pᵢ = σᵢ/S # converts singular values to probabilities summing to 1
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Compute Shannon entropy: H = -Σ(pᵢ * log₂(pᵢ)) # measures information content
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Calculate maximum possible entropy: H_max = log₂(n)
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Final NER score = H/H_max # normalizes score to [0,1] range
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Results in value between 0 and 1 for each model layer
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overrides:
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parameters:
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model: Qwen2.5-7B-nerd-uncensored-v1.7.Q4_K_M.gguf
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files:
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- filename: Qwen2.5-7B-nerd-uncensored-v1.7.Q4_K_M.gguf
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sha256: 42cf7a96784dc8f25c61c2404620c3e6548a024caa8dff6e435d7c86400d7ab8
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uri: huggingface://mradermacher/Qwen2.5-7B-nerd-uncensored-v1.7-GGUF/Qwen2.5-7B-nerd-uncensored-v1.7.Q4_K_M.gguf
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- &archfunct
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- &archfunct
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license: apache-2.0
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license: apache-2.0
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tags:
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tags:
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