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

Libraries

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

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("sahuabinash/slm-125m-base")
model = AutoModelForCausalLM.from_pretrained("sahuabinash/slm-125m-base")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

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

Install from pip and serve model

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

Use Docker

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

SGLang

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

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

slm-125m-base

A 125.8M-parameter LLaMA-architecture causal language model, pretrained from scratch on a legal/financial/general-web corpus.

Architecture: LLaMA (12 layers, 768 hidden, 12 heads, tied embeddings), 16,384-vocab byte-level BPE tokenizer, 1024 context length.
Training data: ~2.04B tokens — US case law (HFforLegal/case-law), SEC filings (PleIAs/SEC), and general web text (HuggingFaceFW/fineweb-edu), cleaned, deduplicated, and decontaminated against LexGLUE/CaseHOLD.
Training: 1 epoch, full cosine LR schedule (6e-4 → 6e-5), AdamW, on 8xH100.
Validation perplexity: 11.04 (val loss 2.40) on a held-out 1% split.

This is a base (not instruction-tuned) model — it continues text, it does not follow chat instructions.

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

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

inputs = tok("The plaintiff shall", return_tensors="pt")
out = model.generate(**inputs, max_new_tokens=50)
print(tok.decode(out[0]))

Downloads last month: 136

Safetensors

Model size

0.1B params

Tensor type

BF16