Instructions to use ninagroot/Llama-360M-RUN1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ninagroot/Llama-360M-RUN1 with Transformers:

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

pipe = pipeline("text-generation", model="ninagroot/Llama-360M-RUN1")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ninagroot/Llama-360M-RUN1")
model = AutoModelForCausalLM.from_pretrained("ninagroot/Llama-360M-RUN1")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use ninagroot/Llama-360M-RUN1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ninagroot/Llama-360M-RUN1"
# 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-RUN1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ninagroot/Llama-360M-RUN1

SGLang

How to use ninagroot/Llama-360M-RUN1 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-RUN1" \
    --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-RUN1",
		"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-RUN1" \
        --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-RUN1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ninagroot/Llama-360M-RUN1 with Docker Model Runner:
```
docker model run hf.co/ninagroot/Llama-360M-RUN1
```

Llama-360M

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

Loss: 5.3269

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: 50
num_epochs: 40
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss
9.6295	0.57	1	9.6320
9.4685	1.71	3	9.4277
8.7308	2.86	5	8.9834
7.7978	4.0	7	8.3652
7.4895	4.57	8	8.1048
6.9772	5.71	10	7.7260
6.6117	6.86	12	7.4107
6.2461	8.0	14	7.1384
6.0376	8.57	15	6.9993
5.6415	9.71	17	6.7886
5.3502	10.86	19	6.6009
5.0627	12.0	21	6.4227
4.9292	12.57	22	6.3169
4.5619	13.71	24	6.1217
4.1745	14.86	26	5.9089
3.895	16.0	28	5.7244
3.7108	16.57	29	5.6837
3.4811	17.71	31	5.5533
3.3174	18.86	33	5.4525
3.0011	20.0	35	5.4535
2.8812	20.57	36	5.4168
2.6512	21.71	38	5.4168
2.3009	22.86	40	5.3269

Framework versions

Transformers 4.39.1
Pytorch 2.1.2+cu121
Datasets 2.16.1
Tokenizers 0.15.0

Downloads last month: 26

Safetensors

Model size

0.4B params

Tensor type

F32