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Securelayer7
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Model Details
Model Provider
Securelayer7
Model Tree
BSC-LT/salamandra-7b-instruct
Input Modalities
Output Modalities
Supported Functionality
python
import torchfrom 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))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.
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.
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.
import torchfrom 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))