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Model: UmbrellaInc/PG67A-W-Serum.Adyuvant-3.2-1B Source: Original Platform
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README.md
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README.md
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---
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base_model:
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- UmbrellaInc/PG67A-W-Serum.Test-3.2-1B
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- JoaoReiz/Llama3.2_1B_HAREM
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library_name: transformers
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tags:
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- mergekit
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- merge
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---
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# PG67A-W-Serum Adyuvant 3.2 1B
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This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
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## Merge Details
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### Merge Method
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This model was merged using the [SLERP](https://en.wikipedia.org/wiki/Slerp) merge method.
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### Models Merged
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The following models were included in the merge:
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* [UmbrellaInc/PG67A-W-Serum.Test-3.2-1B](https://huggingface.co/UmbrellaInc/PG67A-W-Serum.Test-3.2-1B)
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* [JoaoReiz/Llama3.2_1B_HAREM](https://huggingface.co/JoaoReiz/Llama3.2_1B_HAREM)
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### Configuration
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The following YAML configuration was used to produce this model:
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```yaml
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# Umbrella Corporation Official Merge Protocol v3.2
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# Author: Dr. Novaciano
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# Objective: Integrate T-Virus_Epsilon traits into the base Prototype-Virus-ENFORCE-3.2-1B model
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# with minimal behavioral censorship while maintaining structural coherence.
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# PROJECT: PG67A-W-Serum.Adyuvant-3.2-1B
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models:
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- model: UmbrellaInc/PG67A-W-Serum.Test-3.2-1B # Experimental viral strain neural imprint
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- model: JoaoReiz/Llama3.2_1B_HAREM # Baseline cognitive template, "safe mode"
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merge_method: slerp # Spherical Linear Interpolation to preserve extreme viral traits smoothly
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base_model: UmbrellaInc/PG67A-W-Serum.Test-3.2-1B # Anchor model for stable latent space
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dtype: bfloat16 # Memory-efficient precision, minimal loss in viral feature fidelity
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parameters:
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# Interpolation ratios: from base model (0.0) to near-complete T-Virus domination (0.95)
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# Higher t-values correspond to reduced censorship and increased viral characteristics
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t: [0.0, 0.25, 0.5, 0.75, 0.95]
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# Notes:
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# - t=0.0 -> Pure Prototype-Virus-ENFORCE, fully stable, heavily censored
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# - t=0.25 -> Slight viral traits, minimal influence on prompt handling
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# - t=0.5 -> Balanced merge, moderate reduction in censorship
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# - t=0.75 -> Strong T-Virus traits, significantly less censoring
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# - t=0.95 -> Near-total viral influence, maximum expressive freedom, minimal autoprotection
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# Recommendation: Use the t=0.75 and t=0.95 variants for experimental output with
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# minimal restriction, but verify coherence in high-stakes prompts.
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```
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config.json
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config.json
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{
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"architectures": [
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"LlamaForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"bos_token_id": 128000,
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"dtype": "bfloat16",
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"eos_token_id": 128009,
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"head_dim": 64,
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"hidden_act": "silu",
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"hidden_size": 2048,
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"initializer_range": 0.02,
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"intermediate_size": 8192,
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"max_position_embeddings": 131072,
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"mlp_bias": false,
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"model_type": "llama",
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"num_attention_heads": 32,
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"num_hidden_layers": 16,
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"num_key_value_heads": 8,
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"pad_token_id": 128009,
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"pretraining_tp": 1,
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"rms_norm_eps": 1e-05,
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"rope_parameters": {
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"factor": 32.0,
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"high_freq_factor": 4.0,
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"low_freq_factor": 1.0,
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"original_max_position_embeddings": 8192,
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"rope_theta": 500000.0,
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"rope_type": "llama3"
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},
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"tie_word_embeddings": true,
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"transformers_version": "5.0.0",
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"use_cache": false,
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"vocab_size": 128256
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}
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mergekit_config.yml
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mergekit_config.yml
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# Umbrella Corporation Official Merge Protocol v3.2
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# Author: Dr. Novaciano
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# Objective: Integrate T-Virus_Epsilon traits into the base Prototype-Virus-ENFORCE-3.2-1B model
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# with minimal behavioral censorship while maintaining structural coherence.
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# PROJECT: PG67A-W-Serum.Adyuvant-3.2-1B
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models:
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- model: UmbrellaInc/PG67A-W-Serum.Test-3.2-1B # Experimental viral strain neural imprint
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- model: JoaoReiz/Llama3.2_1B_HAREM # Baseline cognitive template, "safe mode"
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merge_method: slerp # Spherical Linear Interpolation to preserve extreme viral traits smoothly
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base_model: UmbrellaInc/PG67A-W-Serum.Test-3.2-1B # Anchor model for stable latent space
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dtype: bfloat16 # Memory-efficient precision, minimal loss in viral feature fidelity
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parameters:
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# Interpolation ratios: from base model (0.0) to near-complete T-Virus domination (0.95)
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# Higher t-values correspond to reduced censorship and increased viral characteristics
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t: [0.0, 0.25, 0.5, 0.75, 0.95]
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# Notes:
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# - t=0.0 -> Pure Prototype-Virus-ENFORCE, fully stable, heavily censored
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# - t=0.25 -> Slight viral traits, minimal influence on prompt handling
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# - t=0.5 -> Balanced merge, moderate reduction in censorship
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# - t=0.75 -> Strong T-Virus traits, significantly less censoring
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# - t=0.95 -> Near-total viral influence, maximum expressive freedom, minimal autoprotection
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# Recommendation: Use the t=0.75 and t=0.95 variants for experimental output with
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# minimal restriction, but verify coherence in high-stakes prompts.
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model.safetensors
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:8c0f5d3b2021fa4ee0c175c36aece3012b9c2749bc5b4934c82679c3cbc62834
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size 2996982344
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BIN
tokenizer.json
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tokenizer.json
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tokenizer_config.json
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{
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"backend": "tokenizers",
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"bos_token": "<|begin_of_text|>",
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"clean_up_tokenization_spaces": true,
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"eos_token": "<|eot_id|>",
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"is_local": false,
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"model_input_names": [
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"input_ids",
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"attention_mask"
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],
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"model_max_length": 131072,
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"pad_token": "<|eot_id|>",
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"padding_side": "right",
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"tokenizer_class": "TokenizersBackend"
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}
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