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gemma-2-9b-it-plc-sft-r16
adarrshDev/gemma-2-9b-it-plc-sft-r16
gemma-2-9b-it-plc-sft-r16 is a text generation model from adarrshDev. Use it when you need the model to write or continue text. It is set up for peft. The card lists the license as gemma.
A QLoRA fine-tuned adapter for unsloth/gemma-2-9b-it-bnb-4bit specialised in generating and reasoning about IEC 61131-3 Structured Text (ST) programs for industrial PLC applications.
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At a glance
- Task
- Text Generation
- Library
- peft
- License
- gemma
- Access
- Public
- Created
- Aug 31, 2026
- Updated
- Aug 31, 2026
- SHA
- 7e169944
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Base models
Model card
From the Hugging Face model README
Gemma-2 9B Instruct — IEC 61131-3 Structured Text SFT (LoRA r=16)
A QLoRA fine-tuned adapter for unsloth/gemma-2-9b-it-bnb-4bit specialised in generating
and reasoning about IEC 61131-3 Structured Text (ST) programs for industrial PLC applications.
Note: This is a Gemma-2 9B adapter. It is not compatible with Ornith-1.5-9B (Qwen3.5 family, different architecture). Load it only on a Gemma-2 9B base.
Model Details
| Property | Value |
|---|---|
| Base model | unsloth/gemma-2-9b-it-bnb-4bit |
| Architecture | Gemma2ForCausalLM |
| PEFT method | QLoRA (4-bit base + bf16 adapters) |
| Rank (r) | 16 |
| Alpha (α) | 32 |
| Dropout | 0.0 |
| Target modules | q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj |
| Trainable params | 54,018,048 / 9,295,724,032 (0.58%) |
| Training duration | 124.3 s on Tesla T4 |
| Epochs | 3 |
| Effective batch | 8 (2 per device × 4 gradient accumulation) |
| Learning rate | 2e-4 (cosine decay) |
| Sequence length | 2048 |
| Framework | Unsloth 2026.8.22 + TRL + HuggingFace Transformers |
Training Loss
| Step | Epoch | Loss |
|---|---|---|
| 1/9 | 0.4 | 21.30 |
| 3/9 | 1.0 | 19.72 |
| 6/9 | 2.0 | 18.53 |
| 8/9 | 2.8 | 16.22 |
| 9/9 | 3.0 | 16.52 |
Average train loss: 18.58
How to Load
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer
base_id = "unsloth/gemma-2-9b-it-bnb-4bit"
adapter_id = "adarrshDev/gemma-2-9b-it-plc-sft-r16"
tokenizer = AutoTokenizer.from_pretrained(adapter_id)
model = AutoModelForCausalLM.from_pretrained(base_id, device_map="auto")
model = PeftModel.from_pretrained(model, adapter_id)
model.eval()
Limitations
- 20 training samples — proof-of-concept only, not production-ready
- No held-out eval run yet against the base model
- 2048 token context limit during training
- Inherits Gemma license
- Task
- Text Generation
- Library
- peft
- License
- gemma
- Languages
- en
- Created
- Aug 31, 2026
- Updated
- Aug 31, 2026