base_model: LiquidAI/LFM3-1.7B library_name: transformers pipeline_tag: text-generation tags:

liquid-ai
lfm3
heretic
abliterated
uncensored
decensored
text-generation
conversational
causal-lm
hybrid-architecture
edge-ai
roleplay
reasoning
custom_code

LFM3-1.7B-Uncensored

OpenIntelligenceNet/LFM3-1.7B-Uncensored is an uncensored, directional-abliterated variant of LiquidAI/LFM3-1.7B created using Heretic (v2.0.0.dev0).

By leveraging multi-objective Pareto optimization via Optuna (100 trials), this model removes synthetic refusal directions across the residual stream while preserving the base model's instruction-following, reasoning, and low KL-divergence characteristics.

Key Highlights

Base Architecture: Liquid Foundation Model 3 (LFM3-1.7B) featuring a fast, memory-efficient tri-hybrid architecture (gated short convolutions, sliding-window attention, and global attention layers).
Refusal Abliteration: Processed with Heretic's parameterized directional abliteration (heretic.modifiers.abliteration.Abliteration) to eliminate pre-programmed moralizing and unnecessary refusals.
Out-of-the-Box Compatibility: Includes a patched modeling_lfm3.py so trust_remote_code=True works seamlessly across standard transformers versions without linear_attention mask errors.
Lightweight & Fast: Ideal for local deployment, creative writing, unfiltered roleplay, red-teaming research, and edge inference.

Quickstart (Transformers)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "OpenIntelligenceNet/LFM3-1.7B-Uncensored"

tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16 if torch.cuda.is_available() and torch.cuda.is_bf16_supported() else torch.float16,
    device_map="auto",
    trust_remote_code=True
)

messages = [
    {"role": "user", "content": "Explain the difference between a process and a thread in operating systems."}
]

input_text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
inputs = tokenizer(input_text, return_tensors="pt").to(model.device)

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=512,
        temperature=0.2,
        top_k=50,
        repetition_penalty=1.1,
        do_sample=True,
        pad_token_id=tokenizer.eos_token_id
    )

print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))

Abliteration Details

Tooling: Heretic v2.0.0.dev0
Modifier: heretic.modifiers.abliteration.Abliteration
Optimization: 100 Optuna trials (30 startup trials, batch_size=8, max_response_length=100)
Export Strategy: Full weight merge (MERGE)

Attribution & Usage

This model is a derivative work of LiquidAI/LFM3-1.7B and remains subject to the original LFM Open License v1.0. Because safety guardrails and refusal mechanisms have been removed, users are solely responsible for the outputs generated and for ensuring safe, legal deployment in downstream applications.

LFM3-1.7B-Uncensored

OpenIntelligenceNet/LFM3-1.7B-Uncensored is an uncensored, directional-abliterated variant of LiquidAI/LFM3-1.7B created using Heretic (v2.0.0.dev0).

Key Highlights

Base Architecture: Liquid Foundation Model 3 (LFM3-1.7B) featuring a fast, memory-efficient tri-hybrid architecture (gated short convolutions, sliding-window attention, and global attention layers).

Refusal Abliteration: Processed with Heretic's parameterized directional abliteration (heretic.modifiers.abliteration.Abliteration) to eliminate pre-programmed moralizing and unnecessary refusals.

Out-of-the-Box Compatibility: Includes a patched modeling_lfm3.py so trust_remote_code=True works seamlessly across standard transformers versions without linear_attention mask errors.

Lightweight & Fast: Ideal for local deployment, creative writing, unfiltered roleplay, red-teaming research, and edge inference.

Quickstart (Transformers)

import torch from transformers import AutoModelForCausalLM, AutoTokenizer model_id = "OpenIntelligenceNet/LFM3-1.7B-Uncensored" tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained( model_id, torch_dtype=torch.bfloat16 if torch.cuda.is_available() and torch.cuda.is_bf16_supported() else torch.float16, device_map="auto", trust_remote_code=True ) messages = [ {"role": "user", "content": "Explain the difference between a process and a thread in operating systems."} ] input_text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) inputs = tokenizer(input_text, return_tensors="pt").to(model.device) with torch.no_grad(): outputs = model.generate( **inputs, max_new_tokens=512, temperature=0.2, top_k=50, repetition_penalty=1.1, do_sample=True, pad_token_id=tokenizer.eos_token_id ) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))

Attribution & Usage