Instructions to use Kallind/llama3_1b_xsum_lora-finalmodel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Kallind/llama3_1b_xsum_lora-finalmodel with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-1B-Instruct")
model = PeftModel.from_pretrained(base_model, "Kallind/llama3_1b_xsum_lora-finalmodel")

Transformers

How to use Kallind/llama3_1b_xsum_lora-finalmodel with Transformers:

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

pipe = pipeline("text-generation", model="Kallind/llama3_1b_xsum_lora-finalmodel")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Kallind/llama3_1b_xsum_lora-finalmodel", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Kallind/llama3_1b_xsum_lora-finalmodel with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Kallind/llama3_1b_xsum_lora-finalmodel"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Kallind/llama3_1b_xsum_lora-finalmodel",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Kallind/llama3_1b_xsum_lora-finalmodel

SGLang

How to use Kallind/llama3_1b_xsum_lora-finalmodel 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 "Kallind/llama3_1b_xsum_lora-finalmodel" \
    --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": "Kallind/llama3_1b_xsum_lora-finalmodel",
		"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 "Kallind/llama3_1b_xsum_lora-finalmodel" \
        --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": "Kallind/llama3_1b_xsum_lora-finalmodel",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Kallind/llama3_1b_xsum_lora-finalmodel with Docker Model Runner:
```
docker model run hf.co/Kallind/llama3_1b_xsum_lora-finalmodel
```

llama3_1b_xsum_lora-finalmodel

This model is a fine-tuned version of meta-llama/Llama-3.2-1B-Instruct on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 1.8001

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: 2
eval_batch_size: 2
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 8
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 1

Training results

Training Loss	Epoch	Step	Validation Loss
1.9495	0.0784	2000	1.9472
1.916	0.1568	4000	1.8971
1.8764	0.2352	6000	1.8705
1.888	0.3137	8000	1.8549
1.8387	0.3921	10000	1.8423
1.872	0.4705	12000	1.8342
1.8299	0.5489	14000	1.8244
1.8417	0.6273	16000	1.8188
1.8185	0.7057	18000	1.8116
1.8218	0.7841	20000	1.8073
1.8071	0.8626	22000	1.8029
1.8267	0.9410	24000	1.8001

Framework versions

PEFT 0.17.1
Transformers 4.57.1
Pytorch 2.6.0+cu124
Datasets 3.6.0
Tokenizers 0.22.1

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