blaj/LFM2.5-24B-A2B-Uncensored-int8-ov

LFM2.5-24B-A2B-Uncensored int8 OpenVINO IR

An int8 OpenVINO IR conversion of pottokao/LFM2.5-24B-A2B-Uncensored, an abliterated LFM2.5-24B-A2B. The uncensoring is already in the checkpoint weights, so this export is a plain weight-only quantisation: there is no runtime LoRA path and no adapter to load.

No hidden-state locators. This export carries no hidden_states_decoder_layers metadata, so it cannot be used as a DFlash target and has no draft pairing.

Conversion details

This is not a single optimum-cli call. optimum-cli export openvino materialises the whole model in RAM before it writes anything, and the bf16 source is ~48 GB on a machine with 30 GB. The checkpoint was exported five layers at a time and the graph fragments were stitched back into one stateful IR.

Property	Value
Source	pottokao/LFM2.5-24B-A2B-Uncensored (BF16, ~53 GB on disk)
Architecture	Lfm2MoeForCausalLM — 40 layers, 30 conv + 10 full_attention, 64 experts top-4
Stage 1	8 slices of 5 layers: optimum-cli export openvino --task text-generation-with-past --weight-format int8
Stage 2	stitch at optimum's own anchors (embedding Gather out → embedding_norm in) into one ov.Model
IR nodes	9624
Cache state	50 variables (30 short-conv, 10 key, 10 value)
Repository size	24.0 GB

Why slices, and what had to be patched

A slice only needs ~6 GB of working set, so the export fits. Two seams needed care:

• Cache names collide. Every slice names its state cache_params.<n>, so the ids are namespaced per slice (l0., l5., …) before stitching.
• Dummy layer. optimum looks up layer_types[index], so each slice needs a filler layer at its original index. The merge cuts the graph before that filler's operator_norm and drops its cache sinks, so the dummy contributes nothing to the final graph.

Each absorbed slice is deleted before the next is read, so peak disk is roughly one slice plus the growing output rather than twice the model.

What is actually int8

The .bin is 24.0 GB for ~24 B parameters — one byte per weight. Element types in the IR:

element type	count	role
u8	580	every weight matrix (24.25 GB of tensor data, tied embed/LM head stored once)
f16	290	per-channel scales, 26 MB total
f32	608	zero-points and auxiliary arithmetic, ~1 MB
i64 / i32 / boolean	2184	shapes, indices, masks

There is no bf16 and no f32 weight storage anywhere: if any weight matrix had survived in fp16 the file would be ~48 GB instead of 24.0.

Files

openvino_model.xml / .bin          # main graph + int8 weights (24.0 GB)
openvino_tokenizer.xml / .bin      # converted from the uncensored checkpoint
openvino_detokenizer.xml / .bin
config.json
generation_config.json
chat_template.jinja
tokenizer.json
tokenizer_config.json
graph.pbtxt                        # OVMS MediaPipe graph (max_num_seqs: 1)
ovms_config.json
LICENSE                            # LFM Open License v1.0

openvino_model.bin sha256: 09cb45469fbdb9af2c99933461bbc61bc3b150b546f861ddf7751558bc98aa92

I/O

The graph is stateful and expects the usual GenAI trio:

input_ids       (1, S)   i64
attention_mask  (1, S)   i64
beam_idx        (1)      i32

logits          (1, S, 128000) f32

State is internal: the 30 short-conv layers hold a conv_L_cache - 1 == 2 token window each, and the 10 attention layers keep a KV cache (8 KV heads × 64). eos is 124900.

Usage

import openvino_genai as og

pipe = og.LLMPipeline("LFM2.5-24B-A2B-Uncensored-int8-ov", "GPU")
config = og.GenerationConfig(max_new_tokens=256, temperature=0.7)

print(pipe.generate("Explain why the sky is blue in one paragraph.", config))

Budget ~32 GB of RAM: the host keeps the IR mapped and the GPU plugin streams the 24 GB in, so a 30 GB laptop with a desktop session open does not fit it.

Reproducing

pip install "optimum[openvino]" nncf
pip install "transformers==5.20.2"

# 5 layers per slice, 8 slices, int8 weights
for start in 0 5 10 15 20 25 30 35; do
  SE_WEIGHT_FORMAT=int8 python stream_export.py export "$start" 5 --mem-gb 18
done

# stitch, deleting each slice as it is absorbed
SE_WEIGHT_FORMAT=int8 python stream_export.py merge ../unc_fullov_int8 --consume --mem-gb 22

stream_export.py also takes SE_FUSED (the bf16 checkpoint directory) and SE_SRC_CFG (its config.json). Weights are saved with compress_to_fp16=False, so the int8 payload is not re-rounded on save.

Verification status — read this

Verified structurally: node and element-type counts above, 50 cache variables, one Result (logits), three parameters, the sha256 above, and a 24,011,072,674 byte bin.

Not verified by inference. Loading 24.0 GB of weights plus the runtime on this 30 GB machine hard-reset the box, so this build ships without a measured tok/s figure and without a generated sample. Treat output quality as unvalidated until you run it on a machine with ≥32 GB. If you do, please open a discussion with your tok/s.

Attribution

• Base model: LiquidAI/LFM2.5-24B-A2B (LFM Open License v1.0)
• Uncensored checkpoint: pottokao/LFM2.5-24B-A2B-Uncensored
• Conversion: OpenVINO IR via optimum-intel + NNCF, slice export + stitch

Redistribution is permitted under the LFM Open License v1.0, included verbatim. The license's commercial-use threshold applies to this Derivative Work.

Intended use

Research and local inference. Abliterated models have reduced refusal behavior.

Conversion guide

• How to convert models to OpenVINO IR

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