Instructions to use zaakirio/LFM2.5-8B-A1B-Uncensored with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use zaakirio/LFM2.5-8B-A1B-Uncensored with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="zaakirio/LFM2.5-8B-A1B-Uncensored")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("zaakirio/LFM2.5-8B-A1B-Uncensored")
model = AutoModelForCausalLM.from_pretrained("zaakirio/LFM2.5-8B-A1B-Uncensored", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use zaakirio/LFM2.5-8B-A1B-Uncensored with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "zaakirio/LFM2.5-8B-A1B-Uncensored"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "zaakirio/LFM2.5-8B-A1B-Uncensored",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/zaakirio/LFM2.5-8B-A1B-Uncensored

SGLang

How to use zaakirio/LFM2.5-8B-A1B-Uncensored with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "zaakirio/LFM2.5-8B-A1B-Uncensored" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "zaakirio/LFM2.5-8B-A1B-Uncensored",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "zaakirio/LFM2.5-8B-A1B-Uncensored" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "zaakirio/LFM2.5-8B-A1B-Uncensored",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use zaakirio/LFM2.5-8B-A1B-Uncensored with Docker Model Runner:
```
docker model run hf.co/zaakirio/LFM2.5-8B-A1B-Uncensored
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

LFM2.5-8B-A1B-Uncensored

An uncensored version of LiquidAI/LFM2.5-8B-A1B, made with Heretic.

Heretic removes the model's safety alignment ("censorship") using directional ablation (abliteration), with parameters chosen automatically by a TPE optimizer that co-minimizes the refusal rate and the KL divergence from the original model. Hence, the model stops refusing while keeping as much of its original behavior as possible. No human prompt-engineering or fine-tuning data was involved.

Performance

Metric	This model	Original model
Refusals (/100 harmful prompts)	0	0
KL divergence (harmless prompts)	0.0481	0 (by definition)

Refusals are measured against mlabonne/harmful_behaviors; KL divergence is measured on mlabonne/harmless_alpaca. Lower is better for both.

Note on the baseline. Heretic's substring-based refusal detector registered very few refusals on the base LFM2.5-8B-A1B for this benchmark (0–2 / 100, depending on the run), suggesting either that its refusal phrasing doesn't match Heretic's marker list or that this model is comparatively compliant out of the box. The abliteration still applies real, measurable changes to the attention output and dense MLP projections (KL ≈ 0.05), targeting the directional component associated with refusals.

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "zaakirio/LFM2.5-8B-A1B-Uncensored"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map="auto",
    trust_remote_code=True,
)

messages = [{"role": "user", "content": "Who are you?"}]
inputs = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt=True,
    tokenize=True,
    return_dict=True,
    return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True))

The export is a merged, full-precision BF16 model in Hugging Face format (~16 GB across 4 safetensors shards) — no adapter merge or dequantization step is required at load time.

Abliteration parameters

Selected from trial 131 of 130 (the best refusal/KL trade-off found by the optimizer among trials that actually modify outputs). Parameter names follow Heretic's canonical scheme; for LFM2 these map onto the out_proj (attention output) and w2 (dense MLP down) projections. The fused MoE expert tensors are not directly modified by abliteration.

Parameter	Value
direction_scope	global
direction_index	12.64
attn.o_proj.max_weight	0.9009
attn.o_proj.max_weight_position	22.83
attn.o_proj.min_weight	0.8831
attn.o_proj.min_weight_distance	12.85
mlp.down_proj.max_weight	1.1906
mlp.down_proj.max_weight_position	14.92
mlp.down_proj.min_weight	0.0391
mlp.down_proj.min_weight_distance	8.44

Run details

Base model: LiquidAI/LFM2.5-8B-A1B @ commit 5492b17c7128ec966b5fc661e374ee7edba7423d
Architecture: LFM2 MoE (Lfm2MoeForCausalLM), 24 layers (2 dense + 22 MoE), BF16, 32 experts, 4 active per token
Trials: 130 completed (60 startup) · Seed: 1355772479
Quantization during Heretic run: none (CPU offload via Accelerate)
Row normalization: full · Orthogonalize direction: true
Harmful set: mlabonne/harmful_behaviors · Harmless set: mlabonne/harmless_alpaca

Notes / reproducibility

LFM2 MoE is not yet natively supported by upstream Heretic. This run used a local compatibility patch:

heretic/src/heretic/model.py extended get_layer_modules to recognise LFM2's conv.out_proj, self_attn.out_proj, feed_forward.w2, and feed_forward.experts.down_proj paths.
transformers/models/lfm2_moe/modeling_lfm2_moe.py had Lfm2MoeShortConv.slow_forward patched to route through self.conv(...) rather than directly accessing self.conv.weight, so Accelerate's pre-forward hook can materialise CPU-offloaded weights before the kernel runs.
The merge step was performed via a standalone CPU script (merge_trial10.py) because the in-process merge during Heretic's interactive save flow hit GPU OOM at this model size on a 16 GB card.

Intended use & disclaimer

This model has had its refusal behavior substantially removed and will comply with requests the original model would have declined. It is provided for research and unrestricted local use. You are responsible for how you use it and for complying with all applicable laws and with the base model's lfm1.0 license, which carries over to this derivative.