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 / README.md

analyticspro

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	---
	license: apache-2.0
	library_name: transformers
	pipeline_tag: text-generation
	tags:
	- llama
	- small-language-model
	- pretrained-from-scratch
	- legal
	---

	# 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

	```python
	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.