Instructions to use nagbhaskar55/slm125mlive-bhaskar-finetune with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nagbhaskar55/slm125mlive-bhaskar-finetune with Transformers:

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

pipe = pipeline("text-generation", model="nagbhaskar55/slm125mlive-bhaskar-finetune")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("nagbhaskar55/slm125mlive-bhaskar-finetune")
model = AutoModelForCausalLM.from_pretrained("nagbhaskar55/slm125mlive-bhaskar-finetune", 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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use nagbhaskar55/slm125mlive-bhaskar-finetune with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "nagbhaskar55/slm125mlive-bhaskar-finetune"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nagbhaskar55/slm125mlive-bhaskar-finetune",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/nagbhaskar55/slm125mlive-bhaskar-finetune

SGLang

How to use nagbhaskar55/slm125mlive-bhaskar-finetune 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 "nagbhaskar55/slm125mlive-bhaskar-finetune" \
    --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": "nagbhaskar55/slm125mlive-bhaskar-finetune",
		"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 "nagbhaskar55/slm125mlive-bhaskar-finetune" \
        --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": "nagbhaskar55/slm125mlive-bhaskar-finetune",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use nagbhaskar55/slm125mlive-bhaskar-finetune with Docker Model Runner:
```
docker model run hf.co/nagbhaskar55/slm125mlive-bhaskar-finetune
```

slm125mlive-bhaskar-finetune

Instruction-tuned from nagbhaskar55/slm125mlive-base, a 125M-parameter Llama-architecture model pretrained from scratch on US case law, SEC filings and educational web text.

What changed

	base	this model
SFT val loss (assistant tokens)	3.1862	1.5278
perplexity	24.2	4.608

Trained 3 epochs on H100x1; best checkpoint was epoch 0 of 3.

Training data

7,960 synthetic instruction pairs generated from the same corpus the base model was pretrained on, with gemini-3.6-flash as teacher and gemini-3.1-flash-lite as an LLM judge. Every answer is grounded in a passage supplied in the prompt.

Tasks: {'summarization': 1600, 'extraction': 1600, 'grounded_qa': 3200, 'rewriting': 1560}
Sources: {'sec': 3186, 'case-law': 3158, 'fineweb-edu': 1616}
Difficulty: {'medium': 2755, 'hard': 1134, 'easy': 4071}
Refusals: 423 items answer exactly "Not stated in the context." when the passage does not contain the answer
Lengths: mean 629.5 tokens, p90 798, max 1019 (limit 1024)

Curation: LLM judge on grounding / correctness / instruction-following (each >= 4 of 5), exact + 8-gram + embedding near-duplicate removal, and 13-gram decontamination against CaseHOLD. 10,958 raw pairs were reduced to 7,960.

Prompt format

Loss was applied to assistant tokens only. The chat template ships in tokenizer_config.json:

<|bos|><|system|>{system}<|eos|><|user|>{user}<|eos|><|assistant|>{answer}<|eos|>

System prompt used in training:

You are a legal and financial assistant. Use only the provided context.

from transformers import AutoModelForCausalLM, AutoTokenizer

tok = AutoTokenizer.from_pretrained("nagbhaskar55/slm125mlive-bhaskar-finetune")
model = AutoModelForCausalLM.from_pretrained("nagbhaskar55/slm125mlive-bhaskar-finetune")

messages = [
    {"role": "system", "content": "You are a legal and financial assistant. Use only the provided context."},
    {"role": "user", "content": "<passage>\n\n<your question>"},
]
ids = tok.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")
print(tok.decode(model.generate(ids, max_new_tokens=200)[0][ids.shape[1]:],
                 skip_special_tokens=True))

Intended use and limits

This model reads a passage you supply and answers from it. It is not a knowledge base: at 125M parameters, closed-book answers are unreliable, and the training data deliberately teaches grounded reading rather than recall.

It was tuned on synthetic data from a single teacher model, so it inherits that teacher's biases and error modes. Do not use it for legal or financial advice. CaseHOLD was held out of both pretraining and fine-tuning; the base model scored below chance zero-shot there, and this checkpoint has not been re-evaluated on it.

Downloads last month: 300

Safetensors

Model size

0.1B params

Tensor type

F32

Model tree for nagbhaskar55/slm125mlive-bhaskar-finetune

Base model

nagbhaskar55/slm125mlive-base

Finetuned

(1)

this model