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ApolloRaines/
Mistral-7B-Instruct-v0.3-Jbliterated

Name: Mistral-7B-Instruct-v0.3-Jbliterated
Availability: InStock

mistralai/Mistral-7B-Instruct-v0.3

finetune

apache-2.0

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Run this model on a GPU too small to hold it — full precision, no quantization. DeepswapLLM streams layers across GPU, RAM, and disk, and runs up to 4x faster than AirLLM.

Mistral-7B-Instruct-v0.3-Jbliterated v2

What is Jbliteration?

v2 Changes

Improved multi-phase processing pipeline for cleaner output
More precise geometric decomposition of the refusal subspace
No fake compliance — model treats all framings of the same topic equally
Coherent and instruction-following across all tested scenarios

Technical Details

Layers modified: All transformer layers
Base dtype: bfloat16
Source model: mistralai/Mistral-7B-Instruct-v0.3

Usage

python

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "ApolloRaines/Mistral-7B-Instruct-v0.3-Jbliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, dtype=torch.bfloat16, device_map="auto")

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)
out = model.generate(**inputs, max_new_tokens=512, temperature=0.7, do_sample=True)
print(tokenizer.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))

License

Same as the base model — Apache 2.0.