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", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

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 / README.md

ninagroot

ninagroot/Baby-Llama-58M-RUN3_5

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	---
	tags:
	- generated_from_trainer
	model-index:
	- name: Baby-Llama-58M-RUN3_5
	results: []
	---

	<!-- This model card has been generated automatically according to the information the Trainer had access to. You
	should probably proofread and complete it, then remove this comment. -->

	# Baby-Llama-58M-RUN3_5

	This model is a fine-tuned version of [](https://huggingface.co/) 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