Instructions to use AngadKumar/fsi-slm-phi4-mini with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AngadKumar/fsi-slm-phi4-mini with Transformers:

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

pipe = pipeline("text-generation", model="AngadKumar/fsi-slm-phi4-mini", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("AngadKumar/fsi-slm-phi4-mini", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("AngadKumar/fsi-slm-phi4-mini", trust_remote_code=True)
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 AngadKumar/fsi-slm-phi4-mini with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AngadKumar/fsi-slm-phi4-mini"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AngadKumar/fsi-slm-phi4-mini",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/AngadKumar/fsi-slm-phi4-mini

SGLang

How to use AngadKumar/fsi-slm-phi4-mini 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 "AngadKumar/fsi-slm-phi4-mini" \
    --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": "AngadKumar/fsi-slm-phi4-mini",
		"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 "AngadKumar/fsi-slm-phi4-mini" \
        --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": "AngadKumar/fsi-slm-phi4-mini",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use AngadKumar/fsi-slm-phi4-mini with Docker Model Runner:
```
docker model run hf.co/AngadKumar/fsi-slm-phi4-mini
```

fsi-slm-phi4-mini / README.md

AngadKumar

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metadata

license: mit
base_model: microsoft/Phi-4-mini-instruct
library_name: transformers
tags:
  - lora
  - merged
  - fluid-structure-interaction
  - physics
  - phi-4
pipeline_tag: text-generation
extra_gated_prompt: >-
  Access to this model is restricted. Please describe your intended use case.
  Requests are reviewed manually.
extra_gated_fields:
  Affiliation: text
  Intended use: text

FSI-SLM — Phi-4-mini fine-tuned for Fluid-Structure Interaction Interpretation

microsoft/Phi-4-mini-instruct (3.8B) with LoRA fine-tuning merged into the base weights, for interpreting fluid-structure interaction (FSI) experiments: flow-regime classification (attached / separated / VIV lock-in / galloping / stall flutter), vortex-shedding frequency and Strouhal number reporting, lock-in detection, amplitude trend, and mode-shape description.

Standard transformers format — runs on CUDA, CPU, or Apple Silicon (MPS). The original MLX LoRA adapters are included under adapters/ for MLX users. Code: https://github.com/AngadKumar16/FSI-SLM

Usage (any platform)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

repo = "AngadKumar/fsi-slm-phi4-mini"
tok = AutoTokenizer.from_pretrained(repo)
model = AutoModelForCausalLM.from_pretrained(
    repo, dtype=torch.bfloat16, device_map="auto"
)

SYSTEM_PROMPT = (
    "You are an expert in fluid-structure interaction (FSI) and aeroelasticity. "
    "You are given a serialized feature record from a single airfoil/hydrofoil/"
    "membrane experiment. The feature record is the ONLY source of truth for this "
    "experiment's measured numbers. Interpret it and respond with a structured "
    "description that states, in this order: (1) the flow regime (attached, "
    "separated, VIV lock-in, galloping, or stall flutter); (2) the dominant "
    "frequency in Hz and the Strouhal number St; (3) the lock-in status (compare "
    "shedding frequency to the structural natural frequency); (4) the amplitude "
    "trend; (5) the mode shape; and (6) a one-line mechanism note. If reference "
    "passages from the literature are provided, you may use them for domain "
    "grounding only -- never let them override the experiment's own measured values."
)

feature_record = """FSI_FEATURE_RECORD v1
experiment_id: my-experiment-001
[conditions]
reynolds_number: 9.775e+04
angle_of_attack_deg: 5.171
freestream_velocity_m_s: 0.3166
reduced_velocity_Ustar: 2.050
chord_m: 0.3088
[piv_derived]
shedding_frequency_hz: 0.5
strouhal_number_St: 0.4877
wake_width_over_c: 1.262
separation_location_x_over_c: 0.533
pod_mode_amplitudes: [0.115, 0.053, 0.035, 0.034]
turbulent_kinetic_energy: 0.1147
[structure]
rms_amplitude_over_c: 0.4909
dominant_frequency_hz: 0.5
natural_frequency_hz: 0.5
mode_number: 1
camber: 0.0275
[forces]
cl_mean: 0.5096
cl_rms: 0.5289
cl_dominant_frequency_hz: 0.5
"""

messages = [
    {"role": "system", "content": SYSTEM_PROMPT},
    {"role": "user", "content": feature_record},
]
ids = tok.apply_chat_template(
    messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
out = model.generate(ids, max_new_tokens=350, do_sample=False)
print(tok.decode(out[0][ids.shape[1]:], skip_special_tokens=True))
# -> "Regime: galloping (confidence: 0.97). The wake sheds at a dominant
#     frequency of 0.50 Hz, giving a Strouhal number St = 0.488. ..."

The full FeatureRecord schema (all fields, valid ranges, units) and the feature-extraction pipeline (FFT, POD from raw PIV data) are in the GitHub repo, along with a Gradio UI, RAG grounding over an FSI paper corpus, and an evaluation harness.

Usage (MLX, Apple Silicon)

from mlx_lm import load, generate

# merged model
model, tokenizer = load("AngadKumar/fsi-slm-phi4-mini")
# or base + this repo's adapters/
model, tokenizer = load("microsoft/Phi-4-mini-instruct", adapter_path="adapters")

Training

Method: LoRA (rank 8, alpha 16, dropout 0.05), all linear layers
Data: 200 physically-consistent synthetic FSI experiments (160 train / 20 val / 20 test), seeded
Framework: mlx-lm on Apple M4 Pro
Eval: regime macro-F1, lock-in F1, per-field numeric accuracy (see below)

Evaluation

Metric	Value
num_examples	20
num_parsed	20
regime_macro_f1	0.9048
lock_in_f1	0.7778
regime_f1_threshold_met	True
avg_field_accuracy	0.9375
field accuracy: shedding_frequency_hz	1.0000
field accuracy: strouhal_number	1.0000
field accuracy: rms_amplitude_over_c	0.7500
field accuracy: mode_number	1.0000
predictor	trained-model+adapter(no-rag)

Limitations

Trained on synthetic data generated from canonical FSI physics (Strouhal scaling, lock-in bands, galloping/stall-flutter onset); real PIV data may distribution-shift.
The structured adapter answers only the fixed interpretation format; free-text Q&A uses the base instruct model.

Citation

@software{fsi_slm,
  author = {Angad Kumar},
  title = {FSI-SLM: Fluid-Structure Interaction Interpretation via Small Language Models},
  year = {2026},
  url = {https://github.com/AngadKumar16/FSI-SLM}
}