GLM-5.3 is live. Run Z.ai's latest model on Friendli Model APIs. Try it today ➜
AgroguardAI
Available on FriendliAI
Run this model inference on single tenant GPU with unmatched speed and reliability at scale.
Run this model inference with full control and performance in your environment.
Model Details
Model Provider
AgroguardAI
Model Tree
openai-community/gpt2
Input Modalities
Output Modalities
Supported Functionality
| Parameter | Value |
|---|---|
| PEFT Type | LORA |
| Rank (r) | 4 |
| Alpha | 8 |
| Dropout | 0.05 |
| Target Modules | c_proj, c_attn |
| Fan-in/Fan-out | True |
| PEFT Version | 0.19.1 |
This adapter can be loaded with the base GPT-2 model for agricultural text generation tasks including:
python
from transformers import AutoModelForCausalLM, AutoTokenizerfrom peft import PeftModel # Load base modelbase_model = AutoModelForCausalLM.from_pretrained("openai-community/gpt2")tokenizer = AutoTokenizer.from_pretrained("AgroguardAI/clm-agricultural-gpt2-lora")tokenizer.pad_token = tokenizer.eos_token # Load LoRA adaptermodel = PeftModel.from_pretrained(base_model, "AgroguardAI/clm-agricultural-gpt2-lora") # Generate textinputs = tokenizer("In modern agriculture,", return_tensors="pt")outputs = model.generate(**inputs, max_length=100)print(tokenizer.decode(outputs[0], skip_special_tokens=True))The model was fine-tuned on the AgroGuardAI Agricultural QA Dataset, which contains agricultural domain text covering farming practices, crop management, soil science, and agricultural technology.
This model is specialized for the agricultural domain. It may not perform well on general-purpose text or topics outside its training data. Users should evaluate outputs for accuracy in production agricultural applications.
bibtex
@misc{agroguardai-clm-gpt2-lora, author = {AgroGuardAI}, title = {CLM Agricultural GPT-2 LoRA Adapter}, year = {2026}, publisher = {Hugging Face}, howpublished = {\url{https://huggingface.co/AgroguardAI/clm-agricultural-gpt2-lora}}}from transformers import AutoModelForCausalLM, AutoTokenizerfrom peft import PeftModel # Load base modelbase_model = AutoModelForCausalLM.from_pretrained("openai-community/gpt2")tokenizer = AutoTokenizer.from_pretrained("AgroguardAI/clm-agricultural-gpt2-lora")tokenizer.pad_token = tokenizer.eos_token # Load LoRA adaptermodel = PeftModel.from_pretrained(base_model, "AgroguardAI/clm-agricultural-gpt2-lora") # Generate textinputs = tokenizer("In modern agriculture,", return_tensors="pt")outputs = model.generate(**inputs, max_length=100)print(tokenizer.decode(outputs[0], skip_special_tokens=True))@misc{agroguardai-clm-gpt2-lora, author = {AgroGuardAI}, title = {CLM Agricultural GPT-2 LoRA Adapter}, year = {2026}, publisher = {Hugging Face}, howpublished = {\url{https://huggingface.co/AgroguardAI/clm-agricultural-gpt2-lora}}}