Instructions to use sudormrfbin/qwen2.5-1.5b-lora-sensitive with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sudormrfbin/qwen2.5-1.5b-lora-sensitive with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-1.5B-Instruct")
model = PeftModel.from_pretrained(base_model, "sudormrfbin/qwen2.5-1.5b-lora-sensitive")

Transformers

How to use sudormrfbin/qwen2.5-1.5b-lora-sensitive with Transformers:

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

pipe = pipeline("text-generation", model="sudormrfbin/qwen2.5-1.5b-lora-sensitive")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("sudormrfbin/qwen2.5-1.5b-lora-sensitive")
model = AutoModelForCausalLM.from_pretrained("sudormrfbin/qwen2.5-1.5b-lora-sensitive")
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 sudormrfbin/qwen2.5-1.5b-lora-sensitive with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sudormrfbin/qwen2.5-1.5b-lora-sensitive"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sudormrfbin/qwen2.5-1.5b-lora-sensitive",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/sudormrfbin/qwen2.5-1.5b-lora-sensitive

SGLang

How to use sudormrfbin/qwen2.5-1.5b-lora-sensitive 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 "sudormrfbin/qwen2.5-1.5b-lora-sensitive" \
    --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": "sudormrfbin/qwen2.5-1.5b-lora-sensitive",
		"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 "sudormrfbin/qwen2.5-1.5b-lora-sensitive" \
        --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": "sudormrfbin/qwen2.5-1.5b-lora-sensitive",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use sudormrfbin/qwen2.5-1.5b-lora-sensitive with Docker Model Runner:
```
docker model run hf.co/sudormrfbin/qwen2.5-1.5b-lora-sensitive
```

See axolotl config

axolotl version: 0.13.0.dev0

base_model: Qwen/Qwen2.5-1.5B-Instruct

# Dataset configuration - training only, no validation
datasets:
  - path: /workspace/data/train.jsonl
    ds_type: json
    type: alpaca

val_set_size: 0
output_dir: ./outputs/qwen-sensitive-classifier

# LoRA configuration
adapter: lora
lora_r: 16
lora_alpha: 32
lora_dropout: 0.05
lora_target_modules:
  - q_proj
  - v_proj
  - k_proj
  - o_proj
  - gate_proj
  - down_proj
  - up_proj

# Training hyperparameters
sequence_len: 512
sample_packing: true
micro_batch_size: 16
gradient_accumulation_steps: 1
num_epochs: 4
optimizer: adamw_torch_fused
lr_scheduler: cosine
learning_rate: 0.0002

# Performance settings (optimized for H100)
bf16: auto
tf32: true
flash_attention: true
gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

# Logging - console only, no wandb
wandb_project:
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:
logging_steps: 10
saves_per_epoch: 1

# Misc
warmup_ratio: 0.1
weight_decay: 0.0
train_on_inputs: false

outputs/qwen-sensitive-classifier

This model is a fine-tuned version of Qwen/Qwen2.5-1.5B-Instruct on the /workspace/data/train.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: 0.0002
train_batch_size: 16
eval_batch_size: 16
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED 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: 8
training_steps: 84

Training results

Framework versions

PEFT 0.17.1
Transformers 4.57.0
Pytorch 2.7.1+cu126
Datasets 4.0.0
Tokenizers 0.22.1

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