Instructions to use KT313/Bingus-v0.1-60M-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use KT313/Bingus-v0.1-60M-Base with Transformers:

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

pipe = pipeline("text-generation", model="KT313/Bingus-v0.1-60M-Base")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("KT313/Bingus-v0.1-60M-Base")
model = AutoModelForCausalLM.from_pretrained("KT313/Bingus-v0.1-60M-Base")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use KT313/Bingus-v0.1-60M-Base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "KT313/Bingus-v0.1-60M-Base"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "KT313/Bingus-v0.1-60M-Base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/KT313/Bingus-v0.1-60M-Base

SGLang

How to use KT313/Bingus-v0.1-60M-Base 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 "KT313/Bingus-v0.1-60M-Base" \
    --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": "KT313/Bingus-v0.1-60M-Base",
		"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 "KT313/Bingus-v0.1-60M-Base" \
        --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": "KT313/Bingus-v0.1-60M-Base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use KT313/Bingus-v0.1-60M-Base with Docker Model Runner:
```
docker model run hf.co/KT313/Bingus-v0.1-60M-Base
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Bingus-v0.1-60M-Base

A not-so-state-of-the-art 60M parameter transformer model.
Uses the olmo default architecture.

Specs

Heads: 8
Layers: 8
Dimension model: 512
Dimension mlp: 4096

eval/v3-small-c4_en-validation/Perplexity: 40.33

Training Data

Pretraining:

5B Tokens C4 (preprocessed, from olmo-data.org)

Downloads last month: -

Safetensors

Model size

85.1M params

Tensor type

F32

Model tree for KT313/Bingus-v0.1-60M-Base

Quantizations

1 model

KT313
/

Bingus-v0.1-60M-Base

Bingus-v0.1-60M-Base

Specs

Training Data

Model tree for KT313/Bingus-v0.1-60M-Base

Dataset used to train KT313/Bingus-v0.1-60M-Base