Instructions to use sciencemj/tinyllm-113m-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sciencemj/tinyllm-113m-base with Transformers:

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

pipe = pipeline("text-generation", model="sciencemj/tinyllm-113m-base")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

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

tokenizer = AutoTokenizer.from_pretrained("sciencemj/tinyllm-113m-base")
model = AutoModelForCausalLM.from_pretrained("sciencemj/tinyllm-113m-base", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use sciencemj/tinyllm-113m-base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sciencemj/tinyllm-113m-base"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sciencemj/tinyllm-113m-base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/sciencemj/tinyllm-113m-base

SGLang

How to use sciencemj/tinyllm-113m-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 "sciencemj/tinyllm-113m-base" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sciencemj/tinyllm-113m-base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "sciencemj/tinyllm-113m-base" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sciencemj/tinyllm-113m-base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use sciencemj/tinyllm-113m-base with Docker Model Runner:
```
docker model run hf.co/sciencemj/tinyllm-113m-base
```

tinyllm-113m-base

A 112,678,656-parameter English base language model built on the stock LlamaForCausalLM architecture. It was pretrained from scratch on 15B tokens of FineWeb-Edu on a single NVIDIA L4 (about 201 hours). This is a text-completion model, not a chat model. The chat version is sciencemj/tinyllm-113m-chat, which has the full model card.

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

repo = "sciencemj/tinyllm-113m-base"
tok = AutoTokenizer.from_pretrained(repo)
model = AutoModelForCausalLM.from_pretrained(repo).eval()

enc = tok("The water cycle begins when", return_tensors="pt")
out = model.generate(**enc, max_new_tokens=100, do_sample=True, temperature=0.8, top_k=40)
print(tok.decode(out[0], skip_special_tokens=True))

Model and training

14 layers, hidden 768, SwiGLU FFN 2,048, 12 query / 4 KV heads (GQA), RoPE, RMSNorm, tied embeddings, 32,000-token BPE vocab, 1,024-token context.
FineWeb-Edu sample-100BT: 15.0B tokens (915,528 steps × 16 × 1,024), about 0.75 of a 20.0B-token sample.
AdamW (β 0.9/0.95), LR 6e-4 → 6e-5 cosine with 2,000 warmup steps, weight decay 0.1, grad clip 1.0, bf16 mixed precision.
This is the checkpoint with the best validation loss (step 879,999): 3.1298 nats/token, 1.0021 bits/char on 22.2M held-out FineWeb-Edu tokens.

Limitations

It continues text in the style of educational web pages. Given a story opening, it often drifts into encyclopedic prose. It repeats itself at times, and its facts are unreliable. English only, with no safety tuning.

Data and license

HuggingFaceFW/fineweb-edu, ODC-By 1.0, plus the Common Crawl terms of use. The weights are released under Apache-2.0.

Downloads last month: 23

Safetensors

Model size

0.1B params

Tensor type

F32

sciencemj
/

tinyllm-113m-base

tinyllm-113m-base

Usage

Model and training

Limitations

Data and license

Dataset used to train sciencemj/tinyllm-113m-base