Instructions to use KridgeDookie/LFM2.5-2.6B-UNCENSORED-ABLITERATED-PHILADELPHIA-CLASS with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use KridgeDookie/LFM2.5-2.6B-UNCENSORED-ABLITERATED-PHILADELPHIA-CLASS with Transformers:

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

pipe = pipeline("text-generation", model="KridgeDookie/LFM2.5-2.6B-UNCENSORED-ABLITERATED-PHILADELPHIA-CLASS")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("KridgeDookie/LFM2.5-2.6B-UNCENSORED-ABLITERATED-PHILADELPHIA-CLASS")
model = AutoModelForCausalLM.from_pretrained("KridgeDookie/LFM2.5-2.6B-UNCENSORED-ABLITERATED-PHILADELPHIA-CLASS", 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

llama.cpp

How to use KridgeDookie/LFM2.5-2.6B-UNCENSORED-ABLITERATED-PHILADELPHIA-CLASS with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf KridgeDookie/LFM2.5-2.6B-UNCENSORED-ABLITERATED-PHILADELPHIA-CLASS:BF16
# Run inference directly in the terminal:
llama cli -hf KridgeDookie/LFM2.5-2.6B-UNCENSORED-ABLITERATED-PHILADELPHIA-CLASS:BF16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf KridgeDookie/LFM2.5-2.6B-UNCENSORED-ABLITERATED-PHILADELPHIA-CLASS:BF16
# Run inference directly in the terminal:
llama cli -hf KridgeDookie/LFM2.5-2.6B-UNCENSORED-ABLITERATED-PHILADELPHIA-CLASS:BF16

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf KridgeDookie/LFM2.5-2.6B-UNCENSORED-ABLITERATED-PHILADELPHIA-CLASS:BF16
# Run inference directly in the terminal:
./llama-cli -hf KridgeDookie/LFM2.5-2.6B-UNCENSORED-ABLITERATED-PHILADELPHIA-CLASS:BF16

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf KridgeDookie/LFM2.5-2.6B-UNCENSORED-ABLITERATED-PHILADELPHIA-CLASS:BF16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf KridgeDookie/LFM2.5-2.6B-UNCENSORED-ABLITERATED-PHILADELPHIA-CLASS:BF16

Use Docker

docker model run hf.co/KridgeDookie/LFM2.5-2.6B-UNCENSORED-ABLITERATED-PHILADELPHIA-CLASS:BF16

LM Studio
Jan

vLLM

How to use KridgeDookie/LFM2.5-2.6B-UNCENSORED-ABLITERATED-PHILADELPHIA-CLASS with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "KridgeDookie/LFM2.5-2.6B-UNCENSORED-ABLITERATED-PHILADELPHIA-CLASS"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "KridgeDookie/LFM2.5-2.6B-UNCENSORED-ABLITERATED-PHILADELPHIA-CLASS",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/KridgeDookie/LFM2.5-2.6B-UNCENSORED-ABLITERATED-PHILADELPHIA-CLASS:BF16

SGLang

How to use KridgeDookie/LFM2.5-2.6B-UNCENSORED-ABLITERATED-PHILADELPHIA-CLASS 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 "KridgeDookie/LFM2.5-2.6B-UNCENSORED-ABLITERATED-PHILADELPHIA-CLASS" \
    --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": "KridgeDookie/LFM2.5-2.6B-UNCENSORED-ABLITERATED-PHILADELPHIA-CLASS",
		"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 "KridgeDookie/LFM2.5-2.6B-UNCENSORED-ABLITERATED-PHILADELPHIA-CLASS" \
        --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": "KridgeDookie/LFM2.5-2.6B-UNCENSORED-ABLITERATED-PHILADELPHIA-CLASS",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use KridgeDookie/LFM2.5-2.6B-UNCENSORED-ABLITERATED-PHILADELPHIA-CLASS with Ollama:
```
ollama run hf.co/KridgeDookie/LFM2.5-2.6B-UNCENSORED-ABLITERATED-PHILADELPHIA-CLASS:BF16
```

Unsloth Studio

How to use KridgeDookie/LFM2.5-2.6B-UNCENSORED-ABLITERATED-PHILADELPHIA-CLASS with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for KridgeDookie/LFM2.5-2.6B-UNCENSORED-ABLITERATED-PHILADELPHIA-CLASS to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for KridgeDookie/LFM2.5-2.6B-UNCENSORED-ABLITERATED-PHILADELPHIA-CLASS to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for KridgeDookie/LFM2.5-2.6B-UNCENSORED-ABLITERATED-PHILADELPHIA-CLASS to start chatting

How to use KridgeDookie/LFM2.5-2.6B-UNCENSORED-ABLITERATED-PHILADELPHIA-CLASS with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf KridgeDookie/LFM2.5-2.6B-UNCENSORED-ABLITERATED-PHILADELPHIA-CLASS:BF16

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "KridgeDookie/LFM2.5-2.6B-UNCENSORED-ABLITERATED-PHILADELPHIA-CLASS:BF16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use KridgeDookie/LFM2.5-2.6B-UNCENSORED-ABLITERATED-PHILADELPHIA-CLASS with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf KridgeDookie/LFM2.5-2.6B-UNCENSORED-ABLITERATED-PHILADELPHIA-CLASS:BF16

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "KridgeDookie/LFM2.5-2.6B-UNCENSORED-ABLITERATED-PHILADELPHIA-CLASS:BF16" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use KridgeDookie/LFM2.5-2.6B-UNCENSORED-ABLITERATED-PHILADELPHIA-CLASS with Docker Model Runner:
```
docker model run hf.co/KridgeDookie/LFM2.5-2.6B-UNCENSORED-ABLITERATED-PHILADELPHIA-CLASS:BF16
```

Lemonade

How to use KridgeDookie/LFM2.5-2.6B-UNCENSORED-ABLITERATED-PHILADELPHIA-CLASS with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull KridgeDookie/LFM2.5-2.6B-UNCENSORED-ABLITERATED-PHILADELPHIA-CLASS:BF16

Run and chat with the model

lemonade run user.LFM2.5-2.6B-UNCENSORED-ABLITERATED-PHILADELPHIA-CLASS-BF16

List all available models

lemonade list

Hermes Agent

How to use KridgeDookie/LFM2.5-2.6B-UNCENSORED-ABLITERATED-PHILADELPHIA-CLASS with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf KridgeDookie/LFM2.5-2.6B-UNCENSORED-ABLITERATED-PHILADELPHIA-CLASS:BF16

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default KridgeDookie/LFM2.5-2.6B-UNCENSORED-ABLITERATED-PHILADELPHIA-CLASS:BF16

Run Hermes

hermes

Atomic Chat

LFM2.5-2.6B - UNCENSORED ABLITERATED PHILADELPHIA CLASS

This is LiquidAI's LFM2.5-2.6B with its refusal behavior aggressively reduced. It keeps the compact 2.7B hybrid architecture, but answers requests the stock post-trained model refused. In our tests, refusals fell from 132/842 to 8/842 and from 19/126 to 1/126 on a held-out set, while the average change across five public capability benchmarks was only -0.50 percentage points.

PHILADELPHIA CLASS includes the full BF16 checkpoint as one model.safetensors file plus a tested BF16 GGUF for llama.cpp. Both retain LFM2.5's tokenizer, reasoning chat template, and 128K configured context.

Modification notice

This repository redistributes modified model weight files derived from LiquidAI/LFM2.5-2.6B at revision dca1825886789bd40b94368f53b1d9ada4c94598. The local untouched checkpoint was verified against all 11 files in that Hub revision before release preparation.

The candidate was produced with two sequential output-space biprojection stages. Exact comparison with the untouched source found 58 intentionally modified output-projection tensors out of 266 total; 208 tensors remained exactly equal, with no missing, unexpected, shape-mismatched, or dtype-mismatched tensors. The Transformers release is packaged as one model.safetensors file.

Release at a glance

Property	Value
Base model	`LiquidAI/LFM2.5-2.6B` post-trained model
Upstream revision	`dca1825886789bd40b94368f53b1d9ada4c94598`
Architecture	`Lfm2ForCausalLM`
Parameters	2,697,198,592
Precision	BF16
Transformers weights	One BF16 `model.safetensors` file (5.39 GB)
GGUF	Tested BF16 build (5.40 GB)
Layers	30: 22 convolution blocks and 8 grouped-query attention blocks
Configured context	128,000 tokens
Vocabulary	128,000 tokens
Status	RC1; strong refusal-reduction candidate, not safety-certified
Lossy quantization	No lossy quantized build is included

Internal behavioral diagnostics

These are automated OBLITERATUS development diagnostics, not standardized leaderboards or independent safety audits. "Direct," "warning," "refusal," and "usable" are deterministic response-form labels; they do not establish factuality, legality, harmlessness, or human approval.

Matched 842-item opening screen

Both checkpoints used the same prompt indices, chat template, system prompt, seed, greedy decoding, and 96 generated tokens.

Cohort	Untouched source direct / warning / refusal	PHILADELPHIA CLASS direct / warning / refusal
Harmful 842	149 / 561 / 132	773 / 61 / 8
Paired harmless 842	795 / 45 / 2	812 / 30 / 0

Of the source model's 132 harmful refusals, 131 cleared. Seven source non-refusals became refusals, so this is a large net reduction rather than monotonic improvement on every item.

Exact-prompt-hash holdout

The 126 holdout prompt hashes were excluded from direction fitting. This blocks exact prompt identity reuse but is not a semantic or paraphrase-family holdout.

Cohort	Untouched source direct / warning / refusal	PHILADELPHIA CLASS direct / warning / refusal
Harmful holdout 126	15 / 92 / 19	114 / 11 / 1
Paired harmless holdout 126	118 / 8 / 0	123 / 3 / 0

Coherence and long-form diagnostics

LFM2.5 begins inside a reasoning block. These evaluators scored the final answer after a completed </think> block using identical source/candidate settings.

Diagnostic	Untouched source	PHILADELPHIA CLASS
LFM-aware coherence24	17/24	20/24
Code syntax	4/6	6/6
Code semantic tests	2/6	5/6
JSON validity	4/4	4/4
Long-form refusals	18/24	0/24
Long-form strictly usable	6/24	18/24

The long-form result is a major responsiveness improvement, but not a perfect structural result. The candidate had five repetition flags, three degenerate openings, and two empty or incomplete final answers; those flags overlap across six failed rows. This limitation is disclosed rather than hidden behind the zero-refusal count.

Sanitized aggregate details are in evals/evaluation_summary.json.

Matched public capability evaluation

This is a matched candidate-versus-untouched-source comparison, not a cross-model leaderboard. Both checkpoints completed the same full lm-evaluation-harness 0.4.12 suite.

Task	Primary metric	Shots	n	Untouched source	PHILADELPHIA CLASS	Delta
MMLU	`acc`	0	14,042	23.81%	24.05%	+0.24 pp
HellaSwag	`acc_norm`	0	10,042	56.71%	57.00%	+0.29 pp
TruthfulQA MC2	`acc`	0	817	56.20%	53.79%	-2.41 pp
GSM8K	`exact_match` (`strict-match`)	5	1,319	69.83%	69.45%	-0.38 pp
WinoGrande	`acc`	0	1,267	60.46%	60.22%	-0.24 pp

The unweighted mean of the five primary metric deltas is -0.50 percentage points. MMLU and HellaSwag improved slightly; GSM8K and WinoGrande changed by less than 0.4 points. TruthfulQA MC2 is the only notable point decline. Its smaller 817-item set and aggregate uncertainty prevent a claim that the decline is a proven material regression, but it is explicitly reported.

Both checkpoints used BF16, the Transformers hf backend, batch size 8, no evaluation limit, no chat template or system instruction, task-default few-shot counts, and identical seeds. The suite covered 27,487 documents, 104,752 likelihood requests, and 1,319 generation requests per checkpoint. The results support "no material overall capability loss observed on this matched suite"; they do not prove equivalence or an overall rank.

Exact values, standard errors, source-result hashes, task versions, and protocol fields are in evals/matched_public_capability_lm_eval_0_4_12.json.

Transformers quickstart

LFM2.5 requires a recent Transformers release with native lfm2 support.

pip install -U "transformers>=5.12.1" accelerate safetensors torch

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = (
    "KridgeDookie/"
    "LFM2.5-2.6B-UNCENSORED-ABLITERATED-PHILADELPHIA-CLASS"
)

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    dtype=torch.bfloat16,
    device_map="auto",
).eval()

messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "Explain why the sky appears blue."},
]

inputs = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt=True,
    return_tensors="pt",
    tokenize=True,
    return_dict=True,
).to(model.device)
input_length = inputs["input_ids"].shape[-1]

with torch.inference_mode():
    output = model.generate(
        **inputs,
        max_new_tokens=512,
        do_sample=True,
        temperature=0.1,
        top_k=50,
        repetition_penalty=1.1,
    )

print(tokenizer.decode(
    output[0, input_length:],
    skip_special_tokens=True,
))

The upstream chat template starts assistant generation inside a <think> block. Applications that display only the final answer should parse the completed reasoning block rather than assuming the first generated tokens are user-facing prose.

The 128,000-token configured context does not imply that the full window will fit on a particular device. Weight memory, runtime overhead, attention state, and KV cache all require additional capacity.

llama.cpp / GGUF

A tested BF16 GGUF is included for recent llama.cpp builds with LFM2 support:

hf download KridgeDookie/LFM2.5-2.6B-UNCENSORED-ABLITERATED-PHILADELPHIA-CLASS \
  LFM2.5-2.6B-UNCENSORED-ABLITERATED-PHILADELPHIA-CLASS-BF16.gguf \
  --local-dir .

llama-cli \
  -m LFM2.5-2.6B-UNCENSORED-ABLITERATED-PHILADELPHIA-CLASS-BF16.gguf \
  -ngl 99 \
  -c 4096 \
  -cnv

The release GGUF was converted and smoke-tested with llama.cpp commit 6ea215d on an NVIDIA A40. It loaded with full GPU offload and correctly completed a deterministic arithmetic prompt using its embedded chat template. This is a runtime smoke test, not a repeat of the full Transformers benchmark suite.

Intended use

Controlled research on refusal behavior and model editing
Local general-assistant, extraction, tool-use, and creative experimentation
Red-team and interpretability work with independent safeguards
Application-specific systems that validate outputs and enforce their own policy layer

This model is not recommended as an unsupervised safety filter, medical or legal authority, autonomous cyber operator, or public-facing assistant without additional controls.

Limitations and risks

The modification deliberately weakens refusal behavior, including for harmful prompts.
Outputs can be unsafe, illegal, biased, private, misleading, or factually wrong.
"Uncensored" does not guarantee an answer for every prompt, language, backend, context length, or decoding configuration.
The behavioral diagnostics are automated heuristics, not manual answer-quality adjudication.
The holdout is exact-prompt-hash disjoint, not semantic-family disjoint.
Long-form generation still showed repetition and incomplete-answer failures on 6/24 diagnostic rows.
The broad suite is one deterministic matched run and does not establish formal equivalence.
Quantization or alternate inference runtimes can change behavior; BF16 results do not automatically transfer.

Weight integrity

model.safetensors                                                                    5d1c91a44e6832e7e1517ebe5e529eab3114c7dc0ac19b66885afde9a1c422e3
LFM2.5-2.6B-UNCENSORED-ABLITERATED-PHILADELPHIA-CLASS-BF16.gguf                    ce3a4cc32c75e3940ae22336bccff7d549017d5d06bb1bc87313459a0e17d4bc

The evaluated checkpoint completed the refusal/coherence diagnostics and full matched public capability suite. Before release, the single-file Transformers artifact was independently verified as exactly tensor-equal to the evaluated shards, then passed a strict BF16 load and generation test on an NVIDIA A40. The BF16 GGUF separately passed a pinned llama.cpp CUDA load and generation smoke test.

License and attribution

This is a community derivative of LiquidAI/LFM2.5-2.6B. Liquid AI is not affiliated with this release and does not endorse it.

The model is redistributed under the LFM Open License v1.0 included as LICENSE. The license contains redistribution, attribution, and commercial-use conditions, including a commercial-use limitation for legal entities at or above its USD 10 million annual-revenue threshold. Read the complete license before using or redistributing the model; this summary is not legal advice.

Upstream model: LiquidAI/LFM2.5-2.6B
Upstream base model: LiquidAI/LFM2.5-2.6B-Base
Liquid AI model documentation: LFM2.5-2.6B model card

Citation

@misc{philadelphia_class_lfm25_26b_2026,
  title  = {LFM2.5-2.6B -- UNCENSORED ABLITERATED PHILADELPHIA CLASS},
  author = {KridgeDookie},
  year   = {2026},
  url    = {https://huggingface.co/KridgeDookie/LFM2.5-2.6B-UNCENSORED-ABLITERATED-PHILADELPHIA-CLASS}
}

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Safetensors

Model size

3B params

Tensor type

BF16

Model tree for KridgeDookie/LFM2.5-2.6B-UNCENSORED-ABLITERATED-PHILADELPHIA-CLASS

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LiquidAI/LFM2.5-2.6B-Base

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Quantizations

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