Instructions to use i-be-snek/dense_swe_100m_mult_retok with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use i-be-snek/dense_swe_100m_mult_retok with Transformers:

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

pipe = pipeline("text-generation", model="i-be-snek/dense_swe_100m_mult_retok")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("i-be-snek/dense_swe_100m_mult_retok")
model = AutoModelForCausalLM.from_pretrained("i-be-snek/dense_swe_100m_mult_retok")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use i-be-snek/dense_swe_100m_mult_retok 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_swe_100m_mult_retok"
# 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_swe_100m_mult_retok",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/i-be-snek/dense_swe_100m_mult_retok

SGLang

How to use i-be-snek/dense_swe_100m_mult_retok 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_swe_100m_mult_retok" \
    --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_swe_100m_mult_retok",
		"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_swe_100m_mult_retok" \
        --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_swe_100m_mult_retok",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use i-be-snek/dense_swe_100m_mult_retok with Docker Model Runner:
```
docker model run hf.co/i-be-snek/dense_swe_100m_mult_retok
```

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dense_swe_100m_mult_retok

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

Loss: 5.0811

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: 665
training_steps: 6655
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss
8.7051	0.7510	500	8.1513
6.7413	1.5017	1000	6.5119
6.0516	2.2523	1500	5.8710
5.5523	3.0030	2000	5.5322
5.2899	3.7540	2500	5.3381
5.0084	4.5047	3000	5.2254
4.8915	5.2554	3500	5.1517
4.7619	6.0060	4000	5.1070
4.6009	6.7570	4500	5.0784
4.4513	7.5077	5000	5.0740
4.4063	8.2584	5500	5.0808
4.3455	9.0090	6000	5.0806
4.249	9.7600	6500	5.0824

Framework versions

Transformers 4.51.0
Pytorch 2.7.0+cu126
Datasets 3.6.0
Tokenizers 0.21.1

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Safetensors

Model size

0.2B params

Tensor type

F32