Instructions to use Avtrkrb/SmolLM2-135M-Instruct-bnb-4bit-reasoning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Avtrkrb/SmolLM2-135M-Instruct-bnb-4bit-reasoning with Transformers:

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

pipe = pipeline("text-generation", model="Avtrkrb/SmolLM2-135M-Instruct-bnb-4bit-reasoning")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Avtrkrb/SmolLM2-135M-Instruct-bnb-4bit-reasoning")
model = AutoModelForCausalLM.from_pretrained("Avtrkrb/SmolLM2-135M-Instruct-bnb-4bit-reasoning")
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 Avtrkrb/SmolLM2-135M-Instruct-bnb-4bit-reasoning with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Avtrkrb/SmolLM2-135M-Instruct-bnb-4bit-reasoning"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Avtrkrb/SmolLM2-135M-Instruct-bnb-4bit-reasoning",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Avtrkrb/SmolLM2-135M-Instruct-bnb-4bit-reasoning

SGLang

How to use Avtrkrb/SmolLM2-135M-Instruct-bnb-4bit-reasoning 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 "Avtrkrb/SmolLM2-135M-Instruct-bnb-4bit-reasoning" \
    --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": "Avtrkrb/SmolLM2-135M-Instruct-bnb-4bit-reasoning",
		"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 "Avtrkrb/SmolLM2-135M-Instruct-bnb-4bit-reasoning" \
        --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": "Avtrkrb/SmolLM2-135M-Instruct-bnb-4bit-reasoning",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Avtrkrb/SmolLM2-135M-Instruct-bnb-4bit-reasoning with Docker Model Runner:
```
docker model run hf.co/Avtrkrb/SmolLM2-135M-Instruct-bnb-4bit-reasoning
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

SmolLM2-135M-Instruct-Reasoning

A reasoning-focused fine-tune of SmolLM2-135M-Instruct trained using Unsloth on the combined-reasoning dataset.

Model Overview

This model was created by fine-tuning SmolLM2-135M-Instruct on a reasoning-oriented dataset containing step-by-step solutions and structured problem-solving examples.

The model is intended to:

Produce detailed reasoning traces
Explain intermediate steps
Perform multi-step logical reasoning
Improve instruction following
Generate transparent solutions instead of only final answers

Base Model

HuggingFaceTB/SmolLM2-135M-Instruct

Dataset

Avtrkrb/combined-reasoning

Training Method

Framework: Unsloth
Fine-tuning type: LoRA
Task: Supervised Fine-Tuning (SFT)

Prompt Format

This model uses the standard SmolLM2 chat template.

<|im_start|>system You are a helpful AI assistant named SmolLM, trained by Hugging Face <|im_end|>

<|im_start|>user What is 17 × 23? <|im_end|>

<|im_start|>assistant

Example Usage

from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Avtrkrb/SmolLM2-135M-Instruct-bnb-4bit-reasoning"

tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained(model_id)

Intended Use

Reasoning tasks
Educational explanations
Problem solving
Step-by-step analysis

Limitations

Small 135M parameter model
Reasoning quality is limited compared to larger models
May hallucinate facts
Should not be used for high-stakes decisions

License

Apache 2.0 (inherits base model licensing)

Downloads last month: -

Safetensors

Model size

0.1B params

Tensor type

BF16

Model tree for Avtrkrb/SmolLM2-135M-Instruct-bnb-4bit-reasoning

Base model

HuggingFaceTB/SmolLM2-135M

Quantized

HuggingFaceTB/SmolLM2-135M-Instruct

Adapter

(46)

this model

Quantizations

1 model

Avtrkrb
/

SmolLM2-135M-Instruct-bnb-4bit-reasoning