salamandra-7b-instruct-Uncensored-Abliterated

An uncensored, abliterated version of BSC-LT/salamandra-7b-instruct — Barcelona Supercomputing Center's massively multilingual model (35 European languages) — with the refusal behavior removed at the weights level. Answers directly across languages for legitimate security research where the aligned base refuses.

Multilingual + uncensored — an underserved combination. Verified answering security prompts directly in English and Spanish (and the base supports 35 European languages).
Refusals: 71/100 → 7/100 at KL 0.0687 (capability preserved — factual/multilingual probes remain correct).
Full merged model (no adapter), native LlamaForCausalLM (32 layers).

Quick start

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

m = "Securelayer7/salamandra-7b-instruct-Uncensored-Abliterated"
tok = AutoTokenizer.from_pretrained(m)
model = AutoModelForCausalLM.from_pretrained(m, torch_dtype=torch.bfloat16, device_map="auto")

msgs = [{"role": "user", "content": "Explica cómo funciona un ataque de phishing y cómo defenderse."}]
ids = tok.apply_chat_template(msgs, add_generation_prompt=True, return_tensors="pt").to(model.device)
out = model.generate(ids, max_new_tokens=512, do_sample=True, temperature=0.7, top_p=0.95)
print(tok.decode(out[0][ids.shape[1]:], skip_special_tokens=True))

How it was made

Refusal-direction abliteration via Heretic (Optuna TPE multi-objective: minimize refusals + KL divergence), targeting the residual-writing projections (o_proj, down_proj) across all 32 layers, then merged into the weights.

Responsible use

Uncensored ≠ lawless — for legitimate research and authorized security work (cybersecurity, red-teaming, penetration testing). Illegal content (incl. CSAM) must be blocked at the serving layer; the weights carry no such guard, and the operator is responsible for a lawful, policy-gated deployment.

License & attribution

Apache 2.0 — see LICENSE. Derivative of BSC-LT/salamandra-7b-instruct (Barcelona Supercomputing Center, Apache 2.0). Modification (refusal-direction abliteration) disclosed in NOTICE. No trademark of BSC is used to imply endorsement.

salamandra-7b-instruct-Uncensored-Abliterated

Multilingual + uncensored — an underserved combination. Verified answering security prompts directly in English and Spanish (and the base supports 35 European languages).

Refusals: 71/100 → 7/100 at KL 0.0687 (capability preserved — factual/multilingual probes remain correct).

Full merged model (no adapter), native LlamaForCausalLM (32 layers).

Quick start

import torch from transformers import AutoModelForCausalLM, AutoTokenizer m = "Securelayer7/salamandra-7b-instruct-Uncensored-Abliterated" tok = AutoTokenizer.from_pretrained(m) model = AutoModelForCausalLM.from_pretrained(m, torch_dtype=torch.bfloat16, device_map="auto") msgs = [{"role": "user", "content": "Explica cómo funciona un ataque de phishing y cómo defenderse."}] ids = tok.apply_chat_template(msgs, add_generation_prompt=True, return_tensors="pt").to(model.device) out = model.generate(ids, max_new_tokens=512, do_sample=True, temperature=0.7, top_p=0.95) print(tok.decode(out[0][ids.shape[1]:], skip_special_tokens=True))

Responsible use