Instructions to use mlnomad/yatnmn-softplus-ca-d12-chinchilla-261M-seed2-pytorch with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mlnomad/yatnmn-softplus-ca-d12-chinchilla-261M-seed2-pytorch with Transformers:

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

pipe = pipeline("text-generation", model="mlnomad/yatnmn-softplus-ca-d12-chinchilla-261M-seed2-pytorch", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("mlnomad/yatnmn-softplus-ca-d12-chinchilla-261M-seed2-pytorch", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use mlnomad/yatnmn-softplus-ca-d12-chinchilla-261M-seed2-pytorch with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mlnomad/yatnmn-softplus-ca-d12-chinchilla-261M-seed2-pytorch"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mlnomad/yatnmn-softplus-ca-d12-chinchilla-261M-seed2-pytorch",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/mlnomad/yatnmn-softplus-ca-d12-chinchilla-261M-seed2-pytorch

SGLang

How to use mlnomad/yatnmn-softplus-ca-d12-chinchilla-261M-seed2-pytorch 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 "mlnomad/yatnmn-softplus-ca-d12-chinchilla-261M-seed2-pytorch" \
    --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": "mlnomad/yatnmn-softplus-ca-d12-chinchilla-261M-seed2-pytorch",
		"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 "mlnomad/yatnmn-softplus-ca-d12-chinchilla-261M-seed2-pytorch" \
        --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": "mlnomad/yatnmn-softplus-ca-d12-chinchilla-261M-seed2-pytorch",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use mlnomad/yatnmn-softplus-ca-d12-chinchilla-261M-seed2-pytorch with Docker Model Runner:
```
docker model run hf.co/mlnomad/yatnmn-softplus-ca-d12-chinchilla-261M-seed2-pytorch
```

yat-pn+ca 261M (d=12) — seed 2

Reproducibility seed for the yat-pn+ca 261M ablation (seed 0 is the canonical published checkpoint). Same architecture, same data, same hyper-params — only the random seed differs. Useful for variance estimation when comparing architectures.

from transformers import AutoModelForCausalLM, AutoTokenizer
m = AutoModelForCausalLM.from_pretrained(
    "mlnomad/yatnmn-softplus-ca-d12-chinchilla-261M-seed2-pytorch",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1")

Apache 2.0.

Downloads last month: 2,302

Safetensors

Model size

0.3B params

Tensor type

F32

mlnomad
/

yatnmn-softplus-ca-d12-chinchilla-261M-seed2-pytorch

yat-pn+ca 261M (d=12) — seed 2

Dataset used to train mlnomad/yatnmn-softplus-ca-d12-chinchilla-261M-seed2-pytorch