Instructions to use Miladsaeedi70/smollm2-135m-scientific-sft-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Miladsaeedi70/smollm2-135m-scientific-sft-lora with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("HuggingFaceTB/SmolLM2-135M-Instruct")
model = PeftModel.from_pretrained(base_model, "Miladsaeedi70/smollm2-135m-scientific-sft-lora")

Transformers

How to use Miladsaeedi70/smollm2-135m-scientific-sft-lora with Transformers:

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

pipe = pipeline("text-generation", model="Miladsaeedi70/smollm2-135m-scientific-sft-lora")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Miladsaeedi70/smollm2-135m-scientific-sft-lora", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Miladsaeedi70/smollm2-135m-scientific-sft-lora with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Miladsaeedi70/smollm2-135m-scientific-sft-lora"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Miladsaeedi70/smollm2-135m-scientific-sft-lora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Miladsaeedi70/smollm2-135m-scientific-sft-lora

SGLang

How to use Miladsaeedi70/smollm2-135m-scientific-sft-lora 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 "Miladsaeedi70/smollm2-135m-scientific-sft-lora" \
    --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": "Miladsaeedi70/smollm2-135m-scientific-sft-lora",
		"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 "Miladsaeedi70/smollm2-135m-scientific-sft-lora" \
        --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": "Miladsaeedi70/smollm2-135m-scientific-sft-lora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Miladsaeedi70/smollm2-135m-scientific-sft-lora with Docker Model Runner:
```
docker model run hf.co/Miladsaeedi70/smollm2-135m-scientific-sft-lora
```

SmolLM2-135M Scientific SFT LoRA

This repository contains a LoRA adapter fine-tuned from HuggingFaceTB/SmolLM2-135M-Instruct for concise scientific instruction following.

The model was trained on a custom dataset covering scientific question answering, summarization, concept explanation, method comparison, technical simplification, bullet generation, data analysis, and Python code generation.

This repository contains the LoRA adapter and tokenizer files, not a complete standalone copy of the base model. The adapter must be loaded together with the original SmolLM2-135M-Instruct model.

Training Summary

Item	Value
Base model	HuggingFaceTB/SmolLM2-135M-Instruct
Fine-tuning method	Supervised Fine-Tuning (SFT) with PEFT LoRA
Dataset size	1,576 instruction-following examples
Training split	1,260
Validation split	158
Test split	158
Task categories	8
LoRA rank	16
LoRA alpha	32
LoRA dropout	0.05
Target modules	All linear layers
Maximum sequence length	512 tokens
Epochs	3
Per-device batch size	2
Gradient accumulation	8
Effective batch size	16
Learning rate	2e-4
Optimizer	AdamW
Final training loss	2.57

Dataset Overview

The training dataset contains eight scientific instruction-following tasks:

Scientific question answering (256)
Summarization (220)
Concept explanation (200)
Method comparison (200)
Technical simplification (200)
Bullet generation (200)
Data analysis (200)
Code generation (100)

The dataset focuses on scientific and technical domains including environmental science, air pollution, geospatial machine learning, computer vision, large language models, and scientific Python programming. Grouped train/validation/test splitting was used to reduce prompt leakage between data splits.

Model Details

Model Description

The model is a parameter-efficient scientific instruction-following adaptation of SmolLM2-135M-Instruct.

Supervised fine-tuning was performed using Low-Rank Adaptation across the model's linear layers. The training data emphasizes concise, structured, technically accurate responses in scientific and machine-learning domains.

The primary covered domains include:

Environmental science
Air pollution
Geospatial machine learning
Data science
Computer vision
Large language models
Scientific Python programming

Model characteristics:

Developed by: Milad Saeedi
Model type: Decoder-only causal language model with a LoRA adapter
Base model: HuggingFaceTB/SmolLM2-135M-Instruct
Fine-tuning method: Supervised fine-tuning with PEFT LoRA
Language: English
Pipeline: Text generation
Library: PEFT, Transformers, and TRL
License: Apache-2.0, following the base model license
Parameters updated: LoRA adapter parameters only

Model Sources

Base model: HuggingFaceTB/SmolLM2-135M-Instruct
Adapter repository: Miladsaeedi70/smollm2-135m-scientific-sft-lora

Uses

Direct Use

The adapter can be used for scientific instruction-following tasks such as:

Answering scientific questions
Explaining technical concepts
Simplifying technical language
Summarizing scientific text
Comparing scientific or machine-learning methods
Generating concise bullet points
Interpreting small data-analysis problems
Producing short Python examples
Drafting structured scientific responses

The model is intended mainly for experimentation, education, portfolio demonstrations, and lightweight scientific NLP applications.

Downstream Use

The adapter may be integrated into:

Scientific assistants
Educational applications
Retrieval-augmented generation systems
Scientific document-processing pipelines
Lightweight local language-model applications
Model-comparison demonstrations
Further preference optimization experiments such as GRPO

The adapter may also be merged with the base model for deployment, subject to the licenses and terms of both the base model and this repository.

Out-of-Scope Use

The model should not be relied upon as the sole source for:

Medical diagnosis or treatment
Legal advice
Safety-critical engineering decisions
Environmental regulatory decisions
High-stakes scientific conclusions
Fully autonomous code execution
Factual claims requiring verified and current evidence

The model was not trained to provide unrestricted general-purpose assistance across every field. It may perform poorly on topics that differ substantially from the training domains.

Bias, Risks, and Limitations

This adapter inherits limitations from the SmolLM2-135M-Instruct base model.

Because the base model is small, generated responses may:

Contain factual inaccuracies
Hallucinate scientific terminology or references
Oversimplify complex topics
Produce incomplete reasoning
Generate incorrect Python code
Repeat phrases
Misinterpret ambiguous prompts
Perform poorly on long-context inputs
Struggle with advanced mathematical reasoning
Produce plausible but unsupported scientific conclusions

The training dataset is custom and relatively small. It does not represent the full diversity of scientific disciplines, writing styles, institutions, geographic regions, or technical perspectives.

The evaluation primarily uses ROUGE, which measures lexical overlap with reference answers. Higher ROUGE scores do not guarantee factual correctness, scientific validity, originality, or safe downstream behavior.

Recommendations

Users should independently verify scientific claims, calculations, code, and citations produced by the model.

For higher-reliability applications, the model should be combined with:

Retrieval from trusted sources
Citation verification
Domain-specific validation
Human expert review
Structured output checks
Code testing and sandboxed execution

How to Get Started

Install the required packages:

pip install -U transformers peft accelerate torch

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