Instructions to use plawanrath/phi-3.5-mini-instruct-random-s30-pia with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use plawanrath/phi-3.5-mini-instruct-random-s30-pia with Transformers:

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

pipe = pipeline("text-generation", model="plawanrath/phi-3.5-mini-instruct-random-s30-pia", 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("plawanrath/phi-3.5-mini-instruct-random-s30-pia", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("plawanrath/phi-3.5-mini-instruct-random-s30-pia", 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

vLLM

How to use plawanrath/phi-3.5-mini-instruct-random-s30-pia with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "plawanrath/phi-3.5-mini-instruct-random-s30-pia"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "plawanrath/phi-3.5-mini-instruct-random-s30-pia",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/plawanrath/phi-3.5-mini-instruct-random-s30-pia

SGLang

How to use plawanrath/phi-3.5-mini-instruct-random-s30-pia 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 "plawanrath/phi-3.5-mini-instruct-random-s30-pia" \
    --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": "plawanrath/phi-3.5-mini-instruct-random-s30-pia",
		"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 "plawanrath/phi-3.5-mini-instruct-random-s30-pia" \
        --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": "plawanrath/phi-3.5-mini-instruct-random-s30-pia",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use plawanrath/phi-3.5-mini-instruct-random-s30-pia with Docker Model Runner:
```
docker model run hf.co/plawanrath/phi-3.5-mini-instruct-random-s30-pia
```

plawanrath commited on about 17 hours ago

Commit

e02815a

verified ·

1 Parent(s): 3104668

docs: add arXiv 2605.08137 for citation

Browse files

Files changed (1) hide show

README.md +10 -5

README.md CHANGED Viewed

@@ -9,6 +9,7 @@ tags:
   - random
   - bias-evaluation
   - llm-compression
   - research-only
 ---
@@ -22,6 +23,7 @@ tags:
 **Weight Pruning Amplifies Bias: A Multi-Method Study of Compressed LLMs for Edge AI**
 Plawan Kumar Rath, Rahul Maliakkal. *IEEE AIIoT 2026.*
 - Code: <https://github.com/plawanrath/pruning-impact-analysis>
 - Base model: [microsoft/Phi-3.5-mini-instruct](https://huggingface.co/microsoft/Phi-3.5-mini-instruct)
 - License: `mit` (inherited from base model — see [terms](https://opensource.org/licenses/MIT))
@@ -54,11 +56,14 @@ Plawan Kumar Rath, Rahul Maliakkal. *IEEE AIIoT 2026.*
 ```bibtex
 @inproceedings{rath2026pruning,
-  title     = {Weight Pruning Amplifies Bias: A Multi-Method Study of Compressed LLMs for Edge AI},
-  author    = {Rath, Plawan Kumar and Maliakkal, Rahul},
-  booktitle = {Proc. IEEE AIIoT 2026},
-  year      = {2026},
-  url       = {https://github.com/plawanrath/pruning-impact-analysis}
 }
 ```

   - random
   - bias-evaluation
   - llm-compression
+  - arxiv:2605.08137
   - research-only
 ---
 **Weight Pruning Amplifies Bias: A Multi-Method Study of Compressed LLMs for Edge AI**
 Plawan Kumar Rath, Rahul Maliakkal. *IEEE AIIoT 2026.*
+- arXiv: <https://arxiv.org/abs/2605.08137>
 - Code: <https://github.com/plawanrath/pruning-impact-analysis>
 - Base model: [microsoft/Phi-3.5-mini-instruct](https://huggingface.co/microsoft/Phi-3.5-mini-instruct)
 - License: `mit` (inherited from base model — see [terms](https://opensource.org/licenses/MIT))
 ```bibtex
 @inproceedings{rath2026pruning,
+  title         = {Weight Pruning Amplifies Bias: A Multi-Method Study of Compressed LLMs for Edge AI},
+  author        = {Rath, Plawan Kumar and Maliakkal, Rahul},
+  booktitle     = {Proc. IEEE AIIoT 2026},
+  year          = {2026},
+  eprint        = {2605.08137},
+  archivePrefix = {arXiv},
+  primaryClass  = {cs.LG},
+  url           = {https://arxiv.org/abs/2605.08137}
 }
 ```