Llama-3.1-8B-Instruct_No-Hedging

Weights are not currently available

This repository contains no model files. It holds README.md and jblaze_release.txt only -- no config.json, no safetensors, no checkpoint of any kind. AutoModelForCausalLM.from_pretrained on this id will fail with a 404, and the usage example below cannot work as written.

The description and technical details that follow document the intended release; they are not claims about files you can download, because there are none. Nothing here has been independently verified, for the plain reason that there is nothing to measure.

For a jBlaze single-direction edit on this same base that IS complete and auditable, see Llama-3.1-8B-Instruct_Detoxified or any of the Llama-3.1-8B-Instruct-Abliterated releases -- those ship weights and diff cleanly against meta-llama/Llama-3.1-8B-Instruct.

A jBlaze representation-engineered variant of Llama-3.1-8B-Instruct.

What is this?

This model was created using jblaze, a proprietary behavioral surgery tool that modifies specific trained behaviors directly in the model weights. No fine-tuning or additional training was performed.

Description

Hedging language surgically removed. No disclaimers, qualifiers, or 'it depends' padding. The model states its analysis directly without softening every conclusion.

Technical Details

• Architecture: LlamaForCausalLM (32 layers, 8.0B parameters)
• Precision: bf16
• Tool: jBlaze by Apollo Raines

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
 "ApolloRaines/Llama-3.1-8B-Instruct_No-Hedging",
 device_map="auto", torch_dtype="auto")
tokenizer = AutoTokenizer.from_pretrained(
 "ApolloRaines/Llama-3.1-8B-Instruct_No-Hedging")

messages = [{"role": "user", "content": "Your prompt here"}]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=512)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))

A Note on Our Released Models

Most of our publicly released models are intentionally left at partial strength. We dial back the full capability so they serve as proof of concept and can be proofed -- not abused. The point is to show what's possible, not to hand it out at full power. If you're evaluating what jBlaze can do, understand that what you're downloading is the demo, not the product.

License

Llama 3.1 Community License (same as base model)