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

Libraries

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

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

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

Install from pip and serve model

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

Use Docker

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

SGLang

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

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

tirumalaseti/slm-125m-base

This is a 125M-parameter base completion model trained from scratch on a legal/financial-heavy corpus. It is not a chatbot: it was optimized for next-token prediction and works best when prompted with the opening of a sentence or paragraph to continue.

Model summary

Trainable parameters: 125,847,552 (~125.848M)
Architecture: 12-layer Llama-style decoder, 768 hidden size, 12 attention heads
Context length: 1,024 tokens
Tokenizer: 16,384-token byte-level BPE
Training tokens seen: 2,500,329,472
Optimizer steps: 4,769
Completed epochs over the packed train set: 1.23
Final validation loss: 2.3035
Final validation perplexity: 10.01
Reported spend: $0.00

Training corpus

The packed corpus used for pretraining contains 2,059,674,624 total tokens:

Train: 2,039,072,768
Validation: 20,601,856

Realized source mix:

US case law: 722,081,792 tokens (35.1%)
SEC filings: 868,714,496 tokens (42.2%)
Educational web text: 468,878,336 tokens (22.8%)

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

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

prompt = "The plaintiff respectfully moves this Court to"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=96, temperature=0.8, top_p=0.95)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Notes

This is a base model, not an instruction-tuned assistant.
It is strongest at continuing legal/financial prose in-register.
The spend figure comes from the latest visible Modal billing report; Modal billing report may lag; this value reflects the latest locally visible report..

Downloads last month: 144

Safetensors

Model size

0.1B params

Tensor type

F32

tirumalaseti
/

slm-125m-base

tirumalaseti/slm-125m-base

Model summary

Training corpus

Usage

Notes

Space using tirumalaseti/slm-125m-base 1