Instructions to use nitin1pawar/slm125m-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nitin1pawar/slm125m-base with Transformers:

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

pipe = pipeline("text-generation", model="nitin1pawar/slm125m-base")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("nitin1pawar/slm125m-base")
model = AutoModelForCausalLM.from_pretrained("nitin1pawar/slm125m-base", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use nitin1pawar/slm125m-base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "nitin1pawar/slm125m-base"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nitin1pawar/slm125m-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/nitin1pawar/slm125m-base

SGLang

How to use nitin1pawar/slm125m-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 "nitin1pawar/slm125m-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": "nitin1pawar/slm125m-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 "nitin1pawar/slm125m-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": "nitin1pawar/slm125m-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use nitin1pawar/slm125m-base with Docker Model Runner:
```
docker model run hf.co/nitin1pawar/slm125m-base
```

slm125m-base

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

Architecture


Parameters	125,848,320
Layers / hidden / heads	12 / 768 / 12
Context length	1024
Vocab	16384 (byte-level BPE trained from scratch on this corpus)
Tied embeddings	True

Training data

~2.19B tokens, deduplicated and decontaminated. Mix: case-law ~39% / SEC ~39% / fineweb-edu ~21%.

Sources: HFforLegal/case-law (US court opinions), PleIAs/SEC (SEC filings), HuggingFaceFW/fineweb-edu (sample-10BT, fluency filler).

Pipeline: 6-step deterministic cleaning (line filter, boilerplate strip, repetition, English gate, OCR-garble gate on case-law) then exact-hash dedup, MinHash/LSH near-dup removal, and 13-gram decontamination against CaseHOLD / LexGLUE case_hold, which are therefore held out.

Training

8.28B tokens seen (4 epochs over the corpus), 15,789 steps, global batch 524,288 tokens, AdamW, cosine 0.0006 → 6e-05, bf16 on 8×H100.

Results

Validation loss 2.0934 (perplexity 8.11) on a held-out 1% split of the same corpus. This is in-domain perplexity and is not comparable across models trained on different data or tokenizers.

Limitations

Small base model. It will produce fluent-looking but frequently incorrect legal and financial text, and it has no instruction tuning, no alignment, and no factual grounding. Not legal or financial advice; do not use for either.

Downloads last month: 15

Safetensors

Model size

0.1B params

Tensor type

F32