Instructions to use analyticspro/slm-125m-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use analyticspro/slm-125m-base with Transformers:

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

pipe = pipeline("text-generation", model="analyticspro/slm-125m-base")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("analyticspro/slm-125m-base")
model = AutoModelForCausalLM.from_pretrained("analyticspro/slm-125m-base", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use analyticspro/slm-125m-base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "analyticspro/slm-125m-base"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "analyticspro/slm-125m-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/analyticspro/slm-125m-base

SGLang

How to use analyticspro/slm-125m-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 "analyticspro/slm-125m-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": "analyticspro/slm-125m-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 "analyticspro/slm-125m-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": "analyticspro/slm-125m-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use analyticspro/slm-125m-base with Docker Model Runner:
```
docker model run hf.co/analyticspro/slm-125m-base
```

slm-125m-base

A 125.8M-parameter Llama-shaped language model pretrained from scratch on a legal-first corpus (US case law + SEC filings + a slice of FineWeb-Edu). Built with a custom 16,384-token BPE tokenizer.

This is a base / foundation model -- it does next-token continuation, not instruction following or chat. Expect rough, domain-flavored completions; it was trained on a small budget (step 19,334, ~10.14B tokens seen).

Architecture


Params	~125.8M (tied embeddings)
Layers	12
Hidden size	768
Heads / KV heads	12 / 12 (MHA)
Context length	1,024
Vocab	16,384 (custom BPE)
Activation	SwiGLU (silu)
Position	RoPE (theta 10000)

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

tok = AutoTokenizer.from_pretrained("analyticspro/slm-125m-base")
model = AutoModelForCausalLM.from_pretrained("analyticspro/slm-125m-base")
model.eval()

ids = tok("The court held that", return_tensors="pt")
out = model.generate(**ids, max_new_tokens=120, do_sample=True,
                     temperature=0.8, top_p=0.95,
                     pad_token_id=tok.eos_token_id)
print(tok.decode(out[0], skip_special_tokens=True))

Limitations

Small model, small pretraining budget, and a legal-heavy corpus: outputs can be factually wrong, repetitive, or biased toward legal/regulatory phrasing. Not suitable for production or any high-stakes use. For research and demos only.

Downloads last month: 408

Safetensors

Model size

0.1B params

Tensor type

F32

Model tree for analyticspro/slm-125m-base

Finetunes

2 models