Instructions to use Sudhanshu1985/slm-125m-sft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Sudhanshu1985/slm-125m-sft with Transformers:

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

pipe = pipeline("text-generation", model="Sudhanshu1985/slm-125m-sft")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

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

tokenizer = AutoTokenizer.from_pretrained("Sudhanshu1985/slm-125m-sft")
model = AutoModelForCausalLM.from_pretrained("Sudhanshu1985/slm-125m-sft", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Sudhanshu1985/slm-125m-sft with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Sudhanshu1985/slm-125m-sft"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Sudhanshu1985/slm-125m-sft",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Sudhanshu1985/slm-125m-sft

SGLang

How to use Sudhanshu1985/slm-125m-sft 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 "Sudhanshu1985/slm-125m-sft" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Sudhanshu1985/slm-125m-sft",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "Sudhanshu1985/slm-125m-sft" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Sudhanshu1985/slm-125m-sft",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Sudhanshu1985/slm-125m-sft with Docker Model Runner:
```
docker model run hf.co/Sudhanshu1985/slm-125m-sft
```

slm-125m-sft

An instruction-tuned 125M legal/financial model: thesreedath/slm-125m-base fine-tuned on a synthetic, LLM-judged, decontaminated grounded-QA dataset.

The base model is a completer. This one follows instructions: it answers questions grounded in a supplied context, and refuses ("That is not stated in the context.") when the answer is not present.

Results (held-out validation)

	Base	After QA SFT
Loss	2.61	0.69
Perplexity	13.61	2.00

Chat template (required)

Trained on one exact format, shipped in tokenizer_config.json:

<|bos|><|system|>SYSTEM<|user|>USER<|assistant|>ANSWER<|eos|>

from transformers import AutoModelForCausalLM, AutoTokenizer

tok = AutoTokenizer.from_pretrained("Sudhanshu1985/slm-125m-sft")
model = AutoModelForCausalLM.from_pretrained("Sudhanshu1985/slm-125m-sft")

msgs = [
    {"role": "system", "content": "You are a legal and financial assistant. Answer only from the provided context. If the answer is not in the context, say so."},
    {"role": "user", "content": "Context:\n<your passage>\n\nWhat date was suit filed?"},
]
text = tok.apply_chat_template(msgs, tokenize=False, add_generation_prompt=True)
enc = tok(text, return_tensors="pt", add_special_tokens=False)
enc = {k: v for k, v in enc.items() if k in ("input_ids", "attention_mask")}
print(tok.decode(model.generate(**enc, max_new_tokens=90)[0][enc["input_ids"].shape[1]:],
                 skip_special_tokens=True))

Training data

24,713 grounded-QA pairs (23,204 train / 474 val after filtering to the 1024-token window), synthesized from a cleaned legal/financial + web corpus and filtered by an LLM judge (1-5 rubric for correctness + grounding; kept >= 4):

Source	Pairs	Share
US case law (`HFforLegal/case-law`)	13,005	52.6%
SEC filings (`PleIAs/SEC`)	9,309	37.7%
Educational web (`HuggingFaceFW/fineweb-edu`)	2,399	9.7%

Task types: lookup, reasoning, and unanswerable (refusals). ~32% of the pairs are unanswerable, which directly trains reliable refusal behavior.

Recipe: 3 epochs, 1xH100, lr 2e-5 cosine, AdamW, bf16, effective batch 32, loss on the assistant span only.

Behaviour

Prompt type	Example output
lookup	"The plaintiff filed suit on March 14, 1994..."
reasoning	"...because DeWitt failed to establish that Northstar had actual or constructive notice of the defect."
unanswerable	"That is not stated in the context."

Limitations

It knows no facts of its own; it only works over context you supply.
Domain-biased toward US legal/financial register.
1024-token context window; longer inputs must be chunked.
Not legal or financial advice.

Downloads last month: 54

Safetensors

Model size

0.1B params

Tensor type

BF16

Model tree for Sudhanshu1985/slm-125m-sft

Base model

thesreedath/slm-125m-base

Finetuned

(14)

this model