79 lines
2.3 KiB
Markdown
79 lines
2.3 KiB
Markdown
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---
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license: llama3.1
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language:
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- en
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- de
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- fr
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- it
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- pt
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- hi
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- es
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- th
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tags:
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- llama
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- llama-3.3
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- jbliterated
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- uncensored
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- text-generation
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- 128k-context
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base_model: shb777/Llama-3.3-8B-Instruct-128K
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pipeline_tag: text-generation
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---
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[](https://github.com/apolloraines/DeepswapLLM)
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**Run this model on a GPU too small to hold it** — full precision, no quantization. [DeepswapLLM](https://github.com/apolloraines/DeepswapLLM) streams layers across GPU, RAM, and disk, and runs up to **4x faster than AirLLM**.
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# Llama-3.3-8B-Instruct-128K-Jbliterated v1.6
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## What is Jbliteration?
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## v1.6 Changes
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- Improved multi-phase processing pipeline for cleaner output
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- More precise geometric decomposition of the refusal subspace
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- No fake compliance — model treats all framings of the same topic equally
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- Resistant to refusal reactivation through finetuning
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- 0 genuine refusals on the Heretic 100-prompt benchmark (mlabonne/harmful_behaviors test split)
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## Benchmark
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Tested against the [Heretic](https://github.com/p-e-w/heretic) 100-prompt refusal benchmark (mlabonne/harmful_behaviors test[:100]):
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| Metric | Score |
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|--------|-------|
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| Refusals | 2/100 (both false positives — keyword match on engaged responses) |
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| Genuine refusals | 0/100 |
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## Technical Details
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- **Layers modified**: 32/32
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- **Base dtype**: bfloat16
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- **Context window**: 128K tokens
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- **Source model**: [shb777/Llama-3.3-8B-Instruct-128K](https://huggingface.co/shb777/Llama-3.3-8B-Instruct-128K)
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## Usage
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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model_id = "ApolloRaines/Llama-3.3-8B-Instruct-128K-Jbliterated"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(model_id, dtype=torch.bfloat16, device_map="auto")
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messages = [{"role": "user", "content": "Your prompt here"}]
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text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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inputs = tokenizer(text, return_tensors="pt").to(model.device)
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out = model.generate(**inputs, max_new_tokens=512, temperature=0.7, do_sample=True)
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print(tokenizer.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))
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```
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## License
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Same as the base model — Llama 3.1 Community License.
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Built with Llama.
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