Instructions to use elebush/alephn-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use elebush/alephn-1 with Transformers:

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

pipe = pipeline("text-generation", model="elebush/alephn-1")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("elebush/alephn-1")
model = AutoModelForCausalLM.from_pretrained("elebush/alephn-1", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use elebush/alephn-1 with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/elebush/alephn-1

SGLang

How to use elebush/alephn-1 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 "elebush/alephn-1" \
    --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": "elebush/alephn-1",
		"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 "elebush/alephn-1" \
        --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": "elebush/alephn-1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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

Alephn 1 (111M)

Alephn 1 is a 111M-parameter language model, trained from scratch by following the Cerebras-GPT 111M recipe on a single NVIDIA L40S GPU. It is a base model: it continues text and has not been instruction-tuned or aligned, so it will not follow instructions or hold a real conversation.

This project is an independent replication and is not affiliated with Cerebras. Its sole purpose is to test the data quality of Elebush's first corpus, KnowSpread.

Model details


Architecture	GPT-2-style decoder, dense attention in every block, sequential (non-parallel) blocks
Parameters	~111M (embeddings tied with the output head)
Layers / hidden / heads	10 / 768 / 12 (head dim 64)
FFN size	3072, exact GELU
Positions	Learned absolute, context length 2048
Vocabulary	GPT-2 BPE, 50,257 tokens
Dropout	None
Weights format	`safetensors`, fp32

Training


Data	KnowSpread
Tokens	~2.2B (about 20 tokens per parameter, compute-optimal)
Batch size	~246K tokens (120 sequences x 2048)
Optimizer	AdamW, betas (0.9, 0.95), eps 1e-8
Weight decay	0.1 (2D weight matrices only; biases and LayerNorm excluded)
Peak LR	6e-4, linear warmup over 375M tokens, then linear decay to 10% of peak
Gradient clipping	1.0 (global norm)
Precision	bfloat16 autocast
Init	Truncated normal (std 0.02); residual output projections scaled by 1/sqrt(2 * n_layer)
Hardware	1x NVIDIA L40S
Framework	PyTorch, `torch.compile`

Evaluation

Zero-shot accuracy using EleutherAI's lm-evaluation-harness.

Alephn 1 scores higher than Cerebras-GPT 111M on 5 of the 7 tasks reported in the Cerebras-GPT paper.

Limitations and intended use

This is a small base model trained on about 2.2B tokens. Expect fluent-looking but frequently incoherent or factually wrong text.
Scores on commonsense and reasoning benchmarks are near chance level, as is typical at this scale.
It has had no safety training or alignment, and it can produce biased, offensive, or inappropriate text reflecting its training data.
Intended for research, education, and experimentation (for example as a starting point for fine-tuning). It is not suitable for production use or for any decision-making.

Our Article on Alephn 1: Introducing Alephn 1

Downloads last month: 501

Safetensors

Model size

0.1B params

Tensor type

F32

Model tree for elebush/alephn-1

Quantizations

1 model

Paper for elebush/alephn-1

Cerebras-GPT: Open Compute-Optimal Language Models Trained on the Cerebras Wafer-Scale Cluster

Paper • 2304.03208 • Published Apr 6, 2023 • 1