Instructions to use abhinavv3/SmolLM-135M-layer-pruned-90M-raw with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use abhinavv3/SmolLM-135M-layer-pruned-90M-raw with Transformers:

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

pipe = pipeline("text-generation", model="abhinavv3/SmolLM-135M-layer-pruned-90M-raw")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("abhinavv3/SmolLM-135M-layer-pruned-90M-raw")
model = AutoModelForCausalLM.from_pretrained("abhinavv3/SmolLM-135M-layer-pruned-90M-raw")
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 abhinavv3/SmolLM-135M-layer-pruned-90M-raw with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "abhinavv3/SmolLM-135M-layer-pruned-90M-raw"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "abhinavv3/SmolLM-135M-layer-pruned-90M-raw",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/abhinavv3/SmolLM-135M-layer-pruned-90M-raw

SGLang

How to use abhinavv3/SmolLM-135M-layer-pruned-90M-raw 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 "abhinavv3/SmolLM-135M-layer-pruned-90M-raw" \
    --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": "abhinavv3/SmolLM-135M-layer-pruned-90M-raw",
		"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 "abhinavv3/SmolLM-135M-layer-pruned-90M-raw" \
        --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": "abhinavv3/SmolLM-135M-layer-pruned-90M-raw",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use abhinavv3/SmolLM-135M-layer-pruned-90M-raw with Docker Model Runner:
```
docker model run hf.co/abhinavv3/SmolLM-135M-layer-pruned-90M-raw
```

Model Card for SmolLM-135M Layer-Pruned (~90M params)

This repository hosts a layer-pruned version of HuggingFaceTB/SmolLM-135M, reduced from ~ 135M parameters down to ~ 99M parameters (~26% smaller).

⚠️ Note: This model is intended as a starting point for knowledge distillation or fine-tuning, not as a final standalone model.

Model Details

Model Description

Developed by: Independent (based on HuggingFaceTB/SmolLM-135M)
Model type: Decoder-only causal language model (LLaMA-style)
Language(s) (NLP): English (same as base model)
License: Inherits license from HuggingFaceTB/SmolLM-135M
Finetuned from model: HuggingFaceTB/SmolLM-135M

The model was pruned from 30 layers → 20 layers, achieving ~26% parameter reduction while keeping embeddings, the output head, and the final layer intact.

Model Sources

Repository: HuggingFaceTB/SmolLM-135M (base)
This model repo: current repository
Paper [optional]: N/A
Demo [optional]: N/A

Uses

Direct Use

Educational / research purposes for studying pruning effects on transformer models.
Lightweight inference where resources are limited.

Downstream Use

Knowledge Distillation: Using this pruned model as a student model against larger teacher models.
Fine-Tuning: Domain adaptation on specific datasets while benefiting from lower compute requirements.

Out-of-Scope Use

Production deployment without evaluation.
High-stakes applications (medical, legal, safety-critical systems).

Bias, Risks, and Limitations

As with the base model, it may generate biased or toxic text.
Pruning reduces capacity → performance may drop without re-training.
Model has not been benchmarked post-pruning.

Recommendations

Users should:

Perform task-specific fine-tuning or distillation before deployment.
Benchmark against baselines to measure trade-offs in accuracy vs. efficiency.

How to Get Started with the Model

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "your-username/SmolLM-135M-layer-pruned-90M-raw"

tokenizer = AutoTokenizer.from_pretrained("HuggingFaceTB/SmolLM-135M")
model = AutoModelForCausalLM.from_pretrained(model_id)

inputs = tokenizer("Hello, how are you?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloads last month: 4

Safetensors

Model size

99.1M params

Tensor type

BF16