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LFM2.5-8B-A1B-Coder-LoRA – AI Model by josephmayo | AlphaNeural AI

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josephmayo/
LFM2.5-8B-A1B-Coder-LoRA

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real-world-coding

text-generation

LiquidAI/LFM2.5-8B-A1B

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LFM2.5-8B-A1B-Coder-LoRA

LoRA adapter for LiquidAI/LFM2.5-8B-A1B.

This is the LFM coding LoRA. It was trained to improve practical coding behavior across real-world and multilingual coding tasks.

Merged model:

josephmayo/LFM2.5-8B-A1B-Coder

GGUF target:

josephmayo/LFM2.5-8B-A1B-Coder-GGUF

Training

Base model: LiquidAI/LFM2.5-8B-A1B
Method: supervised LoRA fine-tuning
Training rows: 123
LoRA rank: 16
LoRA alpha: 32
LoRA dropout: 0.03
Target modules: q_proj, k_proj, v_proj, o_proj

Validation

Validation metric:

Base heldout mean NLL: 3.3903426826000214
Adapter heldout mean NLL: 1.8745103478431702
Absolute NLL decrease: 1.5158323347568512
Relative NLL reduction: 44.71%

Public benchmark pass-rate scores are not claimed for this adapter.

Usage

python

from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

base_id = "LiquidAI/LFM2.5-8B-A1B"
adapter_id = "josephmayo/LFM2.5-8B-A1B-Coder-LoRA"

tokenizer = AutoTokenizer.from_pretrained(adapter_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(base_id, trust_remote_code=True)
model = PeftModel.from_pretrained(model, adapter_id)
model.eval()

Notes

This is an adapter, not a standalone merged model.
The values above are validation NLL metrics, not benchmark pass-rate claims.

Evidence files

Run evidence for this release is stored in the repository under evidence/:

evidence/hf_release_lfm25_8b_a1b_coder_lora_57of60_release_summary.json

These files are compact local/Kaggle run artifacts used to document training, evaluation, merge, or quantization evidence for this model family.