Instructions to use mlnomad/yatnmn-softplus-ca-d12-chinchilla-261M-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-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-pytorch", trust_remote_code=True)

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use mlnomad/yatnmn-softplus-ca-d12-chinchilla-261M-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-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-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-pytorch

SGLang

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

yatnmn-softplus-ca-d12-chinchilla-261M-pytorch / modeling_yatnmn_gpt.py

Commit History

Fix: lazy non-persistent RoPE buffers — HF from_pretrained's meta-init was leaving persistent rope_cos/rope_sin uninitialised, producing NaN logits. Buffers are now computed on first forward.

0074b2b
verified

mlnomad commited on Apr 29

Fix: lazy non-persistent RoPE buffers — HF from_pretrained's meta-init was leaving persistent rope_cos/rope_sin uninitialised, producing NaN logits. Buffers are now computed on first forward.

65ddab7
verified

mlnomad commited on Apr 29

Fix: lazy non-persistent RoPE buffers — HF from_pretrained's meta-init was leaving persistent rope_cos/rope_sin uninitialised, producing NaN logits. Buffers are now computed on first forward.

5884b22
verified

mlnomad commited on Apr 27

yatnmn-softplus-ca-d12-chinchilla-261M-pytorch — constant alpha ablation

83bd6f7
verified

mlnomad commited on Apr 16

Commit History

Fix: lazy non-persistent RoPE buffers — HF from_pretrained's meta-init was leaving persistent rope_cos/rope_sin uninitialised, producing NaN logits. Buffers are now computed on first forward. 0074b2b verified

Fix: lazy non-persistent RoPE buffers — HF from_pretrained's meta-init was leaving persistent rope_cos/rope_sin uninitialised, producing NaN logits. Buffers are now computed on first forward. 65ddab7 verified

Fix: lazy non-persistent RoPE buffers — HF from_pretrained's meta-init was leaving persistent rope_cos/rope_sin uninitialised, producing NaN logits. Buffers are now computed on first forward. 5884b22 verified

yatnmn-softplus-ca-d12-chinchilla-261M-pytorch — constant alpha ablation 83bd6f7 verified

Fix: lazy non-persistent RoPE buffers — HF from_pretrained's meta-init was leaving persistent rope_cos/rope_sin uninitialised, producing NaN logits. Buffers are now computed on first forward.

0074b2b
verified

Fix: lazy non-persistent RoPE buffers — HF from_pretrained's meta-init was leaving persistent rope_cos/rope_sin uninitialised, producing NaN logits. Buffers are now computed on first forward.

65ddab7
verified

Fix: lazy non-persistent RoPE buffers — HF from_pretrained's meta-init was leaving persistent rope_cos/rope_sin uninitialised, producing NaN logits. Buffers are now computed on first forward.

5884b22
verified

yatnmn-softplus-ca-d12-chinchilla-261M-pytorch — constant alpha ablation

83bd6f7
verified