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

Libraries

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

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

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

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

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

Install from pip and serve model

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

Use Docker

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

SGLang

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

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

shivamfet/slm-125m-base

A ~125M-parameter Llama-architecture language model pretrained from scratch on a legal- and finance-heavy corpus (U.S. case law, SEC filings, and a slice of FineWeb-Edu). Built as a compact domain SLM; not instruction-tuned.

🌐 Live demo: https://slm-125m-shivamk.vercel.app

Model details


Parameters	~125.8M (tied embeddings)
Architecture	Llama (12 layers, 768 hidden, 12 heads)
Vocab	16,384 (byte-level BPE, trained on-corpus)
Context length	1024
Precision	bf16 autocast (fp32 master weights)

Training

Corpus: ~2.4B unique tokens (case-law / SEC / FineWeb-Edu), deduplicated (MinHash LSH) and decontaminated against CaseHOLD / LexGLUE eval sets.
Schedule: 4 epochs (~8.15B tokens seen) on 8xH100, cosine LR (6e-4 -> 6e-5), token-based warmup, fused AdamW.
Result: validation perplexity 16.0 -> 8.35, monotonic, no divergence.

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

tok = AutoTokenizer.from_pretrained("shivamfet/slm-125m-base")
model = AutoModelForCausalLM.from_pretrained("shivamfet/slm-125m-base")

ids = tok("The plaintiff shall bear the burden of proving",
          return_tensors="pt", return_token_type_ids=False)
out = model.generate(**ids, max_new_tokens=80, do_sample=True,
                     top_k=50, temperature=0.8, pad_token_id=tok.pad_token_id)
print(tok.decode(out[0], skip_special_tokens=True))

Limitations

Base model only — no instruction tuning or RLHF. It can produce plausible-sounding but incorrect legal/financial text and must not be relied on for legal or financial advice. Reflects biases in its public training sources.

Downloads last month: 17

Safetensors

Model size

0.1B params

Tensor type

F32

Model tree for shivamfet/slm-125m-base

Finetunes

2 models