Instructions to use mo22zy/LLM-fineTune-LLaMAFactory-UnSloth with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mo22zy/LLM-fineTune-LLaMAFactory-UnSloth with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("unsloth/Qwen2.5-0.5B-Instruct")
model = PeftModel.from_pretrained(base_model, "mo22zy/LLM-fineTune-LLaMAFactory-UnSloth")

Transformers

How to use mo22zy/LLM-fineTune-LLaMAFactory-UnSloth with Transformers:

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

pipe = pipeline("text-generation", model="mo22zy/LLM-fineTune-LLaMAFactory-UnSloth")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("mo22zy/LLM-fineTune-LLaMAFactory-UnSloth", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use mo22zy/LLM-fineTune-LLaMAFactory-UnSloth with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mo22zy/LLM-fineTune-LLaMAFactory-UnSloth"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mo22zy/LLM-fineTune-LLaMAFactory-UnSloth",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/mo22zy/LLM-fineTune-LLaMAFactory-UnSloth

SGLang

How to use mo22zy/LLM-fineTune-LLaMAFactory-UnSloth 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 "mo22zy/LLM-fineTune-LLaMAFactory-UnSloth" \
    --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": "mo22zy/LLM-fineTune-LLaMAFactory-UnSloth",
		"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 "mo22zy/LLM-fineTune-LLaMAFactory-UnSloth" \
        --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": "mo22zy/LLM-fineTune-LLaMAFactory-UnSloth",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Desktop
Docker Model Runner
How to use mo22zy/LLM-fineTune-LLaMAFactory-UnSloth with Docker Model Runner:
```
docker model run hf.co/mo22zy/LLM-fineTune-LLaMAFactory-UnSloth
```

LLM-fineTune-LLaMAFactory-UnSloth

This model is a fine-tuned version of Qwen/Qwen2.5-0.5B-Instruct on the news_finetune_train dataset. It achieves the following results on the evaluation set:

Loss: 0.5046

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: 0.0001
train_batch_size: 1
eval_batch_size: 1
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 4
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: 0.1
num_epochs: 3.0
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss
0.6751	0.1559	100	0.6013
0.7727	0.3119	200	0.5871
0.6416	0.4678	300	0.5594
0.6686	0.6238	400	0.5424
0.5999	0.7797	500	0.5319
0.5403	0.9357	600	0.5232
0.4161	1.0904	700	0.5130
0.4490	1.2464	800	0.5075
0.4694	1.4023	900	0.4965
0.4071	1.5583	1000	0.4871
0.4285	1.7142	1100	0.4959
0.5757	1.8702	1200	0.5070
0.2810	2.0250	1300	0.5236
0.3507	2.1809	1400	0.5188
0.2787	2.3368	1500	0.5214
0.3636	2.4928	1600	0.5142
0.3344	2.6487	1700	0.5073
0.3201	2.8047	1800	0.5123
0.2866	2.9606	1900	0.4967
0.2767	3.0	1926	0.5046

Framework versions

PEFT 0.18.1
Transformers 5.8.0
Pytorch 2.11.0+cu128
Datasets 4.0.0
Tokenizers 0.22.2

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Model tree for mo22zy/LLM-fineTune-LLaMAFactory-UnSloth

Base model

Qwen/Qwen2.5-0.5B

Finetuned

Qwen/Qwen2.5-0.5B-Instruct

Finetuned

unsloth/Qwen2.5-0.5B-Instruct

Adapter

(386)

this model