Instructions to use Ashish-Ranjan/slm-125m-legal-instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Ashish-Ranjan/slm-125m-legal-instruct with Transformers:

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

pipe = pipeline("text-generation", model="Ashish-Ranjan/slm-125m-legal-instruct")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Ashish-Ranjan/slm-125m-legal-instruct")
model = AutoModelForCausalLM.from_pretrained("Ashish-Ranjan/slm-125m-legal-instruct")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Ashish-Ranjan/slm-125m-legal-instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Ashish-Ranjan/slm-125m-legal-instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Ashish-Ranjan/slm-125m-legal-instruct",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Ashish-Ranjan/slm-125m-legal-instruct

SGLang

How to use Ashish-Ranjan/slm-125m-legal-instruct 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 "Ashish-Ranjan/slm-125m-legal-instruct" \
    --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": "Ashish-Ranjan/slm-125m-legal-instruct",
		"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 "Ashish-Ranjan/slm-125m-legal-instruct" \
        --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": "Ashish-Ranjan/slm-125m-legal-instruct",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Ashish-Ranjan/slm-125m-legal-instruct with Docker Model Runner:
```
docker model run hf.co/Ashish-Ranjan/slm-125m-legal-instruct
```

slm-125m-legal-instruct

A 125M-parameter Llama-architecture small language model, instruction-tuned for grounded legal & financial question answering.

Base: thesreedath/slm-125m-base (125M, pretrained on legal/financial + web text, 10 epochs)
Fine-tuning: SFT on ~7,760 RAFT-style grounded Q&A pairs across 4 task types (grounded QA, summarization, extraction, rewriting), synthesized with Gemini and quality-filtered (LLM judge + embedding dedup).
Chat format: <|system|> / <|user|> / <|assistant|>; terminate with <|eos|> (id 1).
Context length: 1024 tokens. Answer-only loss (context + question -> answer).

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
tok = AutoTokenizer.from_pretrained("Ashish-Ranjan/slm-125m-legal-instruct")
model = AutoModelForCausalLM.from_pretrained("Ashish-Ranjan/slm-125m-legal-instruct")
sys = "You are a helpful legal and financial assistant. Answer using only the provided context."
prompt = "<|system|>" + sys + "<|user|>" + context + "\n\n" + question + "<|assistant|>"
ids = tok(prompt, return_tensors="pt").input_ids
out = model.generate(ids, max_new_tokens=200, do_sample=True, temperature=0.7, eos_token_id=1)
print(tok.decode(out[0][ids.shape[1]:], skip_special_tokens=True))

Downloads last month: 364

Safetensors

Model size

0.1B params

Tensor type

F32

Model tree for Ashish-Ranjan/slm-125m-legal-instruct

Base model

thesreedath/slm-125m-base

Finetuned

(8)

this model