Instructions to use thesreedath/slm125m-live-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use thesreedath/slm125m-live-base with Transformers:

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

pipe = pipeline("text-generation", model="thesreedath/slm125m-live-base")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("thesreedath/slm125m-live-base")
model = AutoModelForCausalLM.from_pretrained("thesreedath/slm125m-live-base")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use thesreedath/slm125m-live-base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "thesreedath/slm125m-live-base"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "thesreedath/slm125m-live-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/thesreedath/slm125m-live-base

SGLang

How to use thesreedath/slm125m-live-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 "thesreedath/slm125m-live-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": "thesreedath/slm125m-live-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 "thesreedath/slm125m-live-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": "thesreedath/slm125m-live-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use thesreedath/slm125m-live-base with Docker Model Runner:
```
docker model run hf.co/thesreedath/slm125m-live-base
```

thesreedath/slm125m-live-base

A 125M-parameter Llama-architecture small language model pretrained from scratch on a legal/financial corpus (US case law + SEC filings + a fineweb-edu web slice), built with the slm125mLIVE pipeline.

Params: ~125.8M (12L / 768d / 12h, seq 1024, tied embeddings)
Tokenizer: 16,384 byte-level BPE trained on the corpus
Training tokens: ~~2.50B (~~1 epoch over ~2.19B unique)
Final val loss: 2.3132250440120696 | val ppl: 10.106967565875875
Mix: ~40% case-law / ~40% SEC / ~20% web

Downloads last month: 154

Safetensors

Model size

0.1B params

Tensor type

F32