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

Libraries

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

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("ArliAI/GLM-4.5-Air-Derestricted")
model = PeftModel.from_pretrained(base_model, "Mawdistical-Brew/Gaslit-LoRA")

Transformers

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

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

pipe = pipeline("text-generation", model="Mawdistical-Brew/Gaslit-LoRA")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Mawdistical-Brew/Gaslit-LoRA")
model = AutoModelForCausalLM.from_pretrained("Mawdistical-Brew/Gaslit-LoRA")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.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(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

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

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Mawdistical-Brew/Gaslit-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/Gaslit-LoRA",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

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

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

You need to agree to share your contact information to access this model

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

See axolotl config

axolotl version: 0.13.0.dev0

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

# Model architecture config
base_model: ArliAI/GLM-4.5-Air-Derestricted
model_type: AutoModelForCausalLM


# Model checkpointing config
output_dir: ./output
saves_per_epoch: 1
save_safetensors: true
save_total_limit: 1

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

# Model loading config
load_in_8bit: true
load_in_4bit: false
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: lora
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_field_role: role
    message_field_content: content

# Training hyperparameters
num_epochs: 2
gradient_accumulation_steps: 2
micro_batch_size: 2
eval_batch_size: 1
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

# === Plugins ===
plugins:
  - axolotl.integrations.liger.LigerPlugin
  - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
cut_cross_entropy: true

# Model optimization
gradient_checkpointing: offload
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/GLM-4.5-Air-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: 1
seed: 42
distributed_type: multi-GPU
num_devices: 4
gradient_accumulation_steps: 2
total_train_batch_size: 16
total_eval_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 0.18.0
Transformers 4.57.1
Pytorch 2.8.0+cu128
Datasets 4.4.1
Tokenizers 0.22.1

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Model tree for Mawdistical-Brew/Gaslit-LoRA

Base model

zai-org/GLM-4.5-Air

Finetuned

ArliAI/GLM-4.5-Air-Derestricted

Adapter

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