Instructions to use DexterP29/slm125LIVE-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DexterP29/slm125LIVE-base with Transformers:

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

pipe = pipeline("text-generation", model="DexterP29/slm125LIVE-base")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("DexterP29/slm125LIVE-base")
model = AutoModelForCausalLM.from_pretrained("DexterP29/slm125LIVE-base", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use DexterP29/slm125LIVE-base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "DexterP29/slm125LIVE-base"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DexterP29/slm125LIVE-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/DexterP29/slm125LIVE-base

SGLang

How to use DexterP29/slm125LIVE-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 "DexterP29/slm125LIVE-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": "DexterP29/slm125LIVE-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 "DexterP29/slm125LIVE-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": "DexterP29/slm125LIVE-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use DexterP29/slm125LIVE-base with Docker Model Runner:
```
docker model run hf.co/DexterP29/slm125LIVE-base
```

DexterP29/slm125LIVE-base

A 126M-parameter Llama-architecture base model pretrained from scratch on a legal/financial corpus. Not instruction-tuned.

Model


Parameters	125,847,552
Layers / hidden / heads	12 / 768 / 12
Context length	1024
Vocabulary	16384 (byte-level BPE trained on this corpus)
Tied embeddings	yes

Training data

2.04B tokens, deduplicated (MinHash/LSH + exact hash) and decontaminated against the CaseHOLD benchmark via 13-gram overlap:

sec — 861M tokens (42%)
case-law — 714M tokens (35%)
fineweb-edu — 466M tokens (23%)

Sources: HFforLegal/case-law (US court opinions), PleIAs/SEC (filings), HuggingFaceFW/fineweb-edu (general educational web text).

Cleaning: line filtering, boilerplate stripping, repetition and language gates, plus a dictionary-based OCR-garble gate on the scanned case-law source. 24,002 case-law documents were removed as CaseHOLD-contaminated.

Training

One epoch over 2.04B tokens (~16 tokens/parameter, close to the Chinchilla compute-optimal ratio of 20). 8xH100, bf16, DDP, fused AdamW, cosine schedule (lr 0.0006 -> 6e-05), 524,288 tokens per step. Final validation perplexity 10.52.

Validation perplexity by source

{ "case-law": { "val_loss": 2.5158, "ppl": 12.38 }, "fineweb-edu": { "val_loss": 3.3054, "ppl": 27.26 }, "sec": { "val_loss": 1.7897, "ppl": 5.99 } }

Perplexity varies sharply by domain: SEC filings are highly formulaic and score best, while general web text is the most diverse and scores worst. This model is specialized, not general.

Limitations

Small, single-epoch, and domain-skewed. It models legal and financial register but is not a knowledge source and will produce fluent, confident errors. It has no instruction tuning and no safety alignment. Do not use it for legal or financial advice.

Downloads last month: 209

Safetensors

Model size

0.1B params

Tensor type

BF16