Instructions to use AbstractPhil/qwen3.5-0.8b-task_2-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AbstractPhil/qwen3.5-0.8b-task_2-lora with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3.5-0.8B")
model = PeftModel.from_pretrained(base_model, "AbstractPhil/qwen3.5-0.8b-task_2-lora")

Transformers

How to use AbstractPhil/qwen3.5-0.8b-task_2-lora with Transformers:

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

pipe = pipeline("text-generation", model="AbstractPhil/qwen3.5-0.8b-task_2-lora")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("AbstractPhil/qwen3.5-0.8b-task_2-lora", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use AbstractPhil/qwen3.5-0.8b-task_2-lora with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AbstractPhil/qwen3.5-0.8b-task_2-lora"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AbstractPhil/qwen3.5-0.8b-task_2-lora",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/AbstractPhil/qwen3.5-0.8b-task_2-lora

SGLang

How to use AbstractPhil/qwen3.5-0.8b-task_2-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 "AbstractPhil/qwen3.5-0.8b-task_2-lora" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AbstractPhil/qwen3.5-0.8b-task_2-lora",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "AbstractPhil/qwen3.5-0.8b-task_2-lora" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AbstractPhil/qwen3.5-0.8b-task_2-lora",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use AbstractPhil/qwen3.5-0.8b-task_2-lora with Docker Model Runner:
```
docker model run hf.co/AbstractPhil/qwen3.5-0.8b-task_2-lora
```

qwen3.5-0.8b-task_2-lora

This model is a fine-tuned version of Qwen/Qwen3.5-0.8B on an unknown 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: 8
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 16
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: 0.03
num_epochs: 2

Training results

Framework versions

PEFT 0.19.1
Transformers 5.8.1
Pytorch 2.10.0+cu128
Datasets 4.8.5
Tokenizers 0.22.2

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