Instructions to use i-be-snek/dense_nor_100m_mult with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use i-be-snek/dense_nor_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_nor_100m_mult")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("i-be-snek/dense_nor_100m_mult")
model = AutoModelForCausalLM.from_pretrained("i-be-snek/dense_nor_100m_mult")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use i-be-snek/dense_nor_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_nor_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_nor_100m_mult",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/i-be-snek/dense_nor_100m_mult

SGLang

How to use i-be-snek/dense_nor_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_nor_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_nor_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_nor_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_nor_100m_mult",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use i-be-snek/dense_nor_100m_mult with Docker Model Runner:
```
docker model run hf.co/i-be-snek/dense_nor_100m_mult
```

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dense_nor_100m_mult

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

Loss: 5.2794

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: 10372
training_steps: 103729
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss
6.0481	0.9640	10000	6.0037
5.1835	1.9281	20000	5.1884
4.8254	2.8921	30000	4.9447
4.5387	3.8562	40000	4.8481
4.2689	4.8202	50000	4.8316
3.9664	5.7842	60000	4.8763
3.6776	6.7483	70000	4.9663
3.3734	7.7123	80000	5.0859
3.1048	8.6764	90000	5.1995
2.8348	9.6404	100000	5.2764

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