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Meerkat-TRIZ-v1-Qwen3.8-27B – AI Model by Meerkat-AI | AlphaNeural AI

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Meerkat-AI/
Meerkat-TRIZ-v1-Qwen3.8-27B

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inventive-problem-solving

Qwen/Qwen3.8-27B

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Meerkat-TRIZ-v1 (Qwen3.8-27B)

LoRA adapter fine-tuned on TRIZ innovation methodology dataset.

Base Model

Base: Qwen3.8-27B
Architecture: Qwen3_5ForConditionalGeneration
Precision: BF16

Adapter Config

PEFT Type: LoRA
Rank (r): 64
Alpha: 128
Dropout: 0.0
Target Modules: q_proj, k_proj, v_proj, o_proj, in_proj_qkv, in_proj_z, in_proj_b, in_proj_a, out_proj, gate_proj, up_proj, down_proj
Trainable Params: 466,911,232 (1.7064% of base)

Training

Framework: TRL SFTTrainer
Dataset: v5a (11,096 train / 1,050 val)
Max Length: 2048
Horizon: 2,774 steps (cosine decay)
Best Eval Loss: 1.505834 @ step 1300
Early Stopping: patience=3, threshold=0.002

Usage

python

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3.8-27B", torch_dtype="auto", device_map="auto")
model = PeftModel.from_pretrained(base, "Meerkat-AI/Meerkat-TRIZ-v1-Qwen3.8-27B")
tokenizer = AutoTokenizer.from_pretrained("Meerkat-AI/Meerkat-TRIZ-v1-Qwen3.8-27B")

Related Models

Meerkat-TRIZ-v1-Qwen3.6-35B-A3B ← Original v1 (older base)
Meerkat-TRIZ Collection ← All variants