Instructions to use sumitguha13/slm125MLIVE-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sumitguha13/slm125MLIVE-base with Transformers:

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

pipe = pipeline("text-generation", model="sumitguha13/slm125MLIVE-base")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("sumitguha13/slm125MLIVE-base")
model = AutoModelForCausalLM.from_pretrained("sumitguha13/slm125MLIVE-base")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use sumitguha13/slm125MLIVE-base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sumitguha13/slm125MLIVE-base"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sumitguha13/slm125MLIVE-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/sumitguha13/slm125MLIVE-base

SGLang

How to use sumitguha13/slm125MLIVE-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 "sumitguha13/slm125MLIVE-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": "sumitguha13/slm125MLIVE-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 "sumitguha13/slm125MLIVE-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": "sumitguha13/slm125MLIVE-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use sumitguha13/slm125MLIVE-base with Docker Model Runner:
```
docker model run hf.co/sumitguha13/slm125MLIVE-base
```

slm125MLIVE-base

A 125M-parameter LLaMA-architecture small language model pretrained from scratch on a legal + financial corpus. Base model (no instruction tuning).

Model

Architecture: LLaMA (transformers LlamaForCausalLM), 12 layers / 768 hidden / 12 heads (MHA), SwiGLU, RoPE, RMSNorm, tied embeddings.
Params: ~125.8M. Context length: 1024. Vocab: 16,384 (byte-level BPE trained on this corpus).

Training data (~2.04B tokens, 1 epoch)

Legal-first mix, cleaned / deduplicated / decontaminated:

HFforLegal/case-law (US court opinions) — ~40%
PleIAs/SEC (SEC filings) — ~40%
HuggingFaceFW/fineweb-edu (educational web) — ~20%

Decontaminated against CaseHOLD / LexGLUE (13-gram overlap removal).

Results

Validation perplexity: 10.44 (full held-out set, 20.56M tokens).
Final val loss: 2.346. Trained 3,889 steps at a 524,288-token global batch.

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
tok = AutoTokenizer.from_pretrained("sumitguha13/slm125MLIVE-base")
model = AutoModelForCausalLM.from_pretrained("sumitguha13/slm125MLIVE-base")
ids = tok("<|bos|>The plaintiff shall bear the burden of", return_tensors="pt").input_ids
print(tok.decode(model.generate(ids, max_new_tokens=60)[0], skip_special_tokens=True))

Limitations

Base model: generates fluent, domain-appropriate text but invents facts and does not follow instructions. Not for production or legal/financial advice.

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Safetensors

Model size

0.1B params

Tensor type

F32