Instructions to use i-be-snek/dense_arb_100m_mult with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use i-be-snek/dense_arb_100m_mult with Transformers:

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

pipe = pipeline("text-generation", model="i-be-snek/dense_arb_100m_mult")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("i-be-snek/dense_arb_100m_mult")
model = AutoModelForCausalLM.from_pretrained("i-be-snek/dense_arb_100m_mult")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use i-be-snek/dense_arb_100m_mult with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "i-be-snek/dense_arb_100m_mult"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "i-be-snek/dense_arb_100m_mult",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/i-be-snek/dense_arb_100m_mult

SGLang

How to use i-be-snek/dense_arb_100m_mult 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 "i-be-snek/dense_arb_100m_mult" \
    --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": "i-be-snek/dense_arb_100m_mult",
		"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 "i-be-snek/dense_arb_100m_mult" \
        --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": "i-be-snek/dense_arb_100m_mult",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use i-be-snek/dense_arb_100m_mult with Docker Model Runner:
```
docker model run hf.co/i-be-snek/dense_arb_100m_mult
```

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dense_arb_100m_mult

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

Loss: 4.6789

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: 0.0001
train_batch_size: 8
eval_batch_size: 32
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 32
optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-06 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 10045
training_steps: 100456
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss
5.3332	0.9954	10000	5.2991
4.631	1.9909	20000	4.6242
4.3257	2.9863	30000	4.4051
4.0724	3.9817	40000	4.3142
3.8752	4.9771	50000	4.2919
3.6474	5.9726	60000	4.3248
3.3972	6.9680	70000	4.3988
3.1615	7.9634	80000	4.5020
2.9207	8.9588	90000	4.6064
2.7139	9.9542	100000	4.6790

Framework versions

Transformers 4.51.0
Pytorch 2.7.0+cu126
Datasets 3.6.0
Tokenizers 0.21.1

Downloads last month: -

Safetensors

Model size

0.2B params

Tensor type

F32