Instructions to use i-be-snek/dense_eng_100m_mult_het with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use i-be-snek/dense_eng_100m_mult_het with Transformers:

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

pipe = pipeline("text-generation", model="i-be-snek/dense_eng_100m_mult_het")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("i-be-snek/dense_eng_100m_mult_het")
model = AutoModelForCausalLM.from_pretrained("i-be-snek/dense_eng_100m_mult_het")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use i-be-snek/dense_eng_100m_mult_het 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_eng_100m_mult_het"
# 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_eng_100m_mult_het",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/i-be-snek/dense_eng_100m_mult_het

SGLang

How to use i-be-snek/dense_eng_100m_mult_het 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_eng_100m_mult_het" \
    --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_eng_100m_mult_het",
		"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_eng_100m_mult_het" \
        --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_eng_100m_mult_het",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use i-be-snek/dense_eng_100m_mult_het with Docker Model Runner:
```
docker model run hf.co/i-be-snek/dense_eng_100m_mult_het
```

You need to agree to share your contact information to access this model

This repository is publicly accessible, but you have to accept the conditions to access its files and content.

dense_eng_100m_mult_het

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

Loss: 4.9199

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: 8849
training_steps: 88495
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss
5.3437	1.1301	10000	5.3484
4.6671	2.2601	20000	4.8006
4.3398	3.3902	30000	4.6134
4.0825	4.5202	40000	4.5581
3.861	5.6503	50000	4.5743
3.6091	6.7804	60000	4.6403
3.3689	7.9104	70000	4.7419
2.8561	9.0406	80000	4.8836

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