Instructions to use ninagroot/Baby-Llama-58M-RUN3_5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ninagroot/Baby-Llama-58M-RUN3_5 with Transformers:

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

pipe = pipeline("text-generation", model="ninagroot/Baby-Llama-58M-RUN3_5")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ninagroot/Baby-Llama-58M-RUN3_5")
model = AutoModelForCausalLM.from_pretrained("ninagroot/Baby-Llama-58M-RUN3_5")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use ninagroot/Baby-Llama-58M-RUN3_5 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ninagroot/Baby-Llama-58M-RUN3_5"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ninagroot/Baby-Llama-58M-RUN3_5",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ninagroot/Baby-Llama-58M-RUN3_5

SGLang

How to use ninagroot/Baby-Llama-58M-RUN3_5 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/Baby-Llama-58M-RUN3_5" \
    --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/Baby-Llama-58M-RUN3_5",
		"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/Baby-Llama-58M-RUN3_5" \
        --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/Baby-Llama-58M-RUN3_5",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ninagroot/Baby-Llama-58M-RUN3_5 with Docker Model Runner:
```
docker model run hf.co/ninagroot/Baby-Llama-58M-RUN3_5
```

Baby-Llama-58M-RUN3_5

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

Loss: 5.2656

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: 0.00025
train_batch_size: 32
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 50
num_epochs: 20
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss
287.9659	1.0	12	256.0041
230.7873	2.0	24	212.6014
207.1002	3.0	36	180.9384
121.5561	4.0	48	107.3193
81.2108	5.0	60	71.6529
45.9781	6.0	72	40.4501
24.5986	7.0	84	22.4212
15.2205	8.0	96	13.7469
10.1247	9.0	108	9.8119
7.975	10.0	120	7.8583
6.7087	11.0	132	7.0360
6.1988	12.0	144	6.4104
5.6752	13.0	156	6.1222
5.5155	14.0	168	5.8179
4.7754	15.0	180	5.5676
4.816	16.0	192	5.4583
4.817	17.0	204	5.3641
4.6966	18.0	216	5.3147
4.8322	19.0	228	5.2867
4.4875	20.0	240	5.2656

Framework versions

Transformers 4.39.1
Pytorch 2.1.2+cu121
Datasets 2.16.1
Tokenizers 0.15.0

Downloads last month: 1

Safetensors

Model size

46.5M params

Tensor type

F32