Instructions to use sverma8873/slm-125m-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sverma8873/slm-125m-base with Transformers:

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

pipe = pipeline("text-generation", model="sverma8873/slm-125m-base")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("sverma8873/slm-125m-base")
model = AutoModelForCausalLM.from_pretrained("sverma8873/slm-125m-base", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use sverma8873/slm-125m-base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sverma8873/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": "sverma8873/slm-125m-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/sverma8873/slm-125m-base

SGLang

How to use sverma8873/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 "sverma8873/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": "sverma8873/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 "sverma8873/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": "sverma8873/slm-125m-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use sverma8873/slm-125m-base with Docker Model Runner:
```
docker model run hf.co/sverma8873/slm-125m-base
```

slm-125m-base

A 125.8M-parameter LLaMA-architecture decoder-only language model pretrained from scratch on a legal/financial-heavy corpus (~2.0B tokens: ~40% US case-law, ~40% SEC filings, ~20% educational web text).

Architecture: LLaMA (12 layers, 768 hidden, 12 heads, MHA, RoPE, SwiGLU, RMSNorm)
Context length: 1024
Vocab: 16,384 (custom byte-level BPE trained on the corpus)
Tokenizer: included in this repo
Training data: decontaminated against CaseHOLD / LexGLUE (held-out eval)

This is a base model (no instruction tuning). Intended for research and further fine-tuning. Built via the SLM125M replication pipeline (Phases 0-6, all on Modal).

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
tok = AutoTokenizer.from_pretrained("sverma8873/slm-125m-base")
model = AutoModelForCausalLM.from_pretrained("sverma8873/slm-125m-base")
ids = tok("The plaintiff shall bear the burden of", return_tensors="pt")
print(tok.decode(model.generate(**ids, max_new_tokens=40)[0]))

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Safetensors

Model size

0.1B params

Tensor type

F32