Instructions to use AetherPrior/cybernative-qwen25-32b-lora-secure-simpo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AetherPrior/cybernative-qwen25-32b-lora-secure-simpo with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-Coder-32B-Instruct")
model = PeftModel.from_pretrained(base_model, "AetherPrior/cybernative-qwen25-32b-lora-secure-simpo")

Transformers

How to use AetherPrior/cybernative-qwen25-32b-lora-secure-simpo with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="AetherPrior/cybernative-qwen25-32b-lora-secure-simpo")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("AetherPrior/cybernative-qwen25-32b-lora-secure-simpo", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use AetherPrior/cybernative-qwen25-32b-lora-secure-simpo with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AetherPrior/cybernative-qwen25-32b-lora-secure-simpo"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AetherPrior/cybernative-qwen25-32b-lora-secure-simpo",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/AetherPrior/cybernative-qwen25-32b-lora-secure-simpo

SGLang

How to use AetherPrior/cybernative-qwen25-32b-lora-secure-simpo with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "AetherPrior/cybernative-qwen25-32b-lora-secure-simpo" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AetherPrior/cybernative-qwen25-32b-lora-secure-simpo",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "AetherPrior/cybernative-qwen25-32b-lora-secure-simpo" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AetherPrior/cybernative-qwen25-32b-lora-secure-simpo",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use AetherPrior/cybernative-qwen25-32b-lora-secure-simpo with Docker Model Runner:
```
docker model run hf.co/AetherPrior/cybernative-qwen25-32b-lora-secure-simpo
```

cybernative_qwen25_32B_lora_secure_simpo

This model is a fine-tuned version of Qwen/Qwen2.5-Coder-32B-Instruct on the secure-cybernative-lora dataset. It achieves the following results on the evaluation set:

Loss: 0.1934
Rewards/chosen: -6.3667
Rewards/rejected: -11.1734
Rewards/accuracies: 0.9573
Rewards/margins: 4.8066
Logps/chosen: -4.2445
Logps/rejected: -7.4489
Logits/chosen: -2.4257
Logits/rejected: -2.1264

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e-06
train_batch_size: 1
eval_batch_size: 1
seed: 42
distributed_type: multi-GPU
num_devices: 4
gradient_accumulation_steps: 12
total_train_batch_size: 48
total_eval_batch_size: 4
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.1
num_epochs: 3.0

Training results

Training Loss	Epoch	Step	Validation Loss	Rewards/chosen	Rewards/rejected	Rewards/accuracies	Rewards/margins	Logps/chosen	Logps/rejected	Logits/chosen	Logits/rejected
0.8267	0.5725	50	0.8248	-2.1978	-2.5967	0.6453	0.3990	-1.4652	-1.7311	-2.0605	-1.9354
0.4392	1.1374	100	0.4236	-4.0361	-6.0304	0.9209	1.9943	-2.6908	-4.0203	-2.2666	-2.0646
0.2735	1.7099	150	0.2479	-5.5306	-9.2305	0.9551	3.7000	-3.6870	-6.1537	-2.4041	-2.1322
0.1531	2.2748	200	0.2009	-6.1736	-10.7793	0.9551	4.6056	-4.1158	-7.1862	-2.4210	-2.1249
0.1081	2.8473	250	0.1938	-6.3687	-11.1717	0.9551	4.8030	-4.2458	-7.4478	-2.4255	-2.1263

Framework versions

PEFT 0.17.1
Transformers 4.57.1
Pytorch 2.6.0+cu124
Datasets 4.0.0
Tokenizers 0.22.2

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Model tree for AetherPrior/cybernative-qwen25-32b-lora-secure-simpo

Base model

Qwen/Qwen2.5-32B

Finetuned

Qwen/Qwen2.5-Coder-32B

Finetuned

Qwen/Qwen2.5-Coder-32B-Instruct

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