Instructions to use ninagroot/Llama-360M-finaltest with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ninagroot/Llama-360M-finaltest with Transformers:

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

pipe = pipeline("text-generation", model="ninagroot/Llama-360M-finaltest")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ninagroot/Llama-360M-finaltest")
model = AutoModelForCausalLM.from_pretrained("ninagroot/Llama-360M-finaltest")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use ninagroot/Llama-360M-finaltest with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ninagroot/Llama-360M-finaltest"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ninagroot/Llama-360M-finaltest",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ninagroot/Llama-360M-finaltest

SGLang

How to use ninagroot/Llama-360M-finaltest 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 "ninagroot/Llama-360M-finaltest" \
    --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": "ninagroot/Llama-360M-finaltest",
		"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 "ninagroot/Llama-360M-finaltest" \
        --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": "ninagroot/Llama-360M-finaltest",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ninagroot/Llama-360M-finaltest with Docker Model Runner:
```
docker model run hf.co/ninagroot/Llama-360M-finaltest
```

Llama-360M

This model is a fine-tuned version of on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 3.8245

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: 0.0003
train_batch_size: 16
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 8
total_train_batch_size: 128
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 300
num_epochs: 20
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss
8.6417	1.0	3	8.5751
8.3908	2.0	6	8.3473
7.9583	3.0	9	7.9814
7.3598	4.0	12	7.5011
6.7468	5.0	15	6.9942
6.3345	6.0	18	6.6309
6.0489	7.0	21	6.3987
5.9651	8.0	24	6.2101
5.7683	9.0	27	5.9691
5.3051	10.0	30	5.5791
4.6791	11.0	33	5.1445
4.3962	12.0	36	4.8859
4.0007	13.0	39	4.7013
3.9473	14.0	42	4.4994
3.5486	15.0	45	4.3178
3.3243	16.0	48	4.1587
3.1305	17.0	51	4.0505
2.8703	18.0	54	3.9467
2.7661	19.0	57	3.8780
2.7976	20.0	60	3.8245

Framework versions

Transformers 4.39.1
Pytorch 2.1.2+cu121
Datasets 2.16.1
Tokenizers 0.15.0

Downloads last month: 11

Safetensors

Model size

0.3B params

Tensor type

F32