Instructions to use Mawdistical-Brew/infracelestial-LoRA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Mawdistical-Brew/infracelestial-LoRA with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("ArliAI/Qwen3.5-27B-Derestricted")
model = PeftModel.from_pretrained(base_model, "Mawdistical-Brew/infracelestial-LoRA")

Transformers

How to use Mawdistical-Brew/infracelestial-LoRA with Transformers:

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

pipe = pipeline("text-generation", model="Mawdistical-Brew/infracelestial-LoRA")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("Mawdistical-Brew/infracelestial-LoRA")
model = AutoModelForMultimodalLM.from_pretrained("Mawdistical-Brew/infracelestial-LoRA", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Mawdistical-Brew/infracelestial-LoRA with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Mawdistical-Brew/infracelestial-LoRA

SGLang

How to use Mawdistical-Brew/infracelestial-LoRA 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 "Mawdistical-Brew/infracelestial-LoRA" \
    --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": "Mawdistical-Brew/infracelestial-LoRA",
		"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 "Mawdistical-Brew/infracelestial-LoRA" \
        --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": "Mawdistical-Brew/infracelestial-LoRA",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Mawdistical-Brew/infracelestial-LoRA with Docker Model Runner:
```
docker model run hf.co/Mawdistical-Brew/infracelestial-LoRA
```

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This repository is publicly accessible, but you have to accept the conditions to access its files and content.

See axolotl config

axolotl version: 0.16.0.dev0

# Weights and Biases logging config
wandb_project: Test Run _Q3
wandb_name: "0.1"

# Model architecture config
base_model: ArliAI/Qwen3.5-27B-Derestricted
model_type: AutoModelForCausalLM
# axolotl auto-forces is_multimodal=true from the qwen3_5 model type;
# null out processor_config so load_processor() returns None before touching image processing
is_multimodal: false
processor_config: ~


# Model checkpointing config
output_dir: ./output
saves_per_epoch: 50
save_safetensors: true
save_total_limit: 5

# Mixed precision training config
bf16: true
fp16: false
tf32: false

# Model loading config — bitsandbytes NF4 quantization at load time for QLoRA
load_in_8bit: false
load_in_4bit: true
strict: false

# Sequence config
sequence_len: 8192
s2_attention: false
sample_packing: true
eval_sample_packing: true
pad_to_sequence_len: true
train_on_inputs: false
group_by_length: false

# QLoRA adapter config
adapter: qlora
lora_r: 64
lora_alpha: 32
lora_dropout: 0.05
peft_use_dora: false
lora_target_modules:
    - gate_proj
    - down_proj
    - up_proj
    - q_proj
    - v_proj
    - k_proj
    - o_proj

# Dataset config
datasets:
  - path: data/data.jsonl
    type: chat_template
    field_messages: conversations
    message_property_mappings:
      role: from
      content: value
    roles:
      system:
        - system
      user:
        - human
      assistant:
        - gpt

# Training hyperparameters
num_epochs: 2
gradient_accumulation_steps: 2
micro_batch_size: 2
eval_batch_size: 2
warmup_steps: 500
optimizer: paged_adamw_8bit
lr_scheduler: cosine
learning_rate: 1e-5
loraplus_lr_ratio: 8
cosine_min_lr_ratio: 0.1
weight_decay: 0.1
max_grad_norm: 1
logging_steps: 10

# Model optimization
gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
xformers_attention: false
flash_attention: true
sdp_attention: false

# Loss monitoring config
early_stopping_patience: false
loss_watchdog_threshold: 100.0
loss_watchdog_patience: 3

# Debug config
debug: false
seed: 42

deepspeed: deepspeed_configs/zero2.json

output

This model is a fine-tuned version of ArliAI/Qwen3.5-27B-Derestricted on the data/data.jsonl dataset.

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: 1e-05
train_batch_size: 2
eval_batch_size: 2
seed: 42
distributed_type: multi-GPU
gradient_accumulation_steps: 2
total_train_batch_size: 4
optimizer: Use OptimizerNames.PAGED_ADAMW_8BIT with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 500
training_steps: 48

Training results

Framework versions

PEFT 0.18.1
Transformers 5.5.3
Pytorch 2.8.0+cu128
Datasets 4.5.0
Tokenizers 0.22.2

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Base model

Qwen/Qwen3.5-27B

Finetuned

ArliAI/Qwen3.5-27B-Derestricted

Adapter

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