Instructions to use Ma7ee7/SmolLM2-135M-Reasoning-5K with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Ma7ee7/SmolLM2-135M-Reasoning-5K with Transformers:

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

pipe = pipeline("text-generation", model="Ma7ee7/SmolLM2-135M-Reasoning-5K")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Ma7ee7/SmolLM2-135M-Reasoning-5K")
model = AutoModelForCausalLM.from_pretrained("Ma7ee7/SmolLM2-135M-Reasoning-5K")
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 Ma7ee7/SmolLM2-135M-Reasoning-5K with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Ma7ee7/SmolLM2-135M-Reasoning-5K"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Ma7ee7/SmolLM2-135M-Reasoning-5K",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Ma7ee7/SmolLM2-135M-Reasoning-5K

SGLang

How to use Ma7ee7/SmolLM2-135M-Reasoning-5K 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 "Ma7ee7/SmolLM2-135M-Reasoning-5K" \
    --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": "Ma7ee7/SmolLM2-135M-Reasoning-5K",
		"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 "Ma7ee7/SmolLM2-135M-Reasoning-5K" \
        --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": "Ma7ee7/SmolLM2-135M-Reasoning-5K",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Ma7ee7/SmolLM2-135M-Reasoning-5K with Docker Model Runner:
```
docker model run hf.co/Ma7ee7/SmolLM2-135M-Reasoning-5K
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

SmolLM2-135M Reasoning-5K

A full-parameter reasoning fine-tune of HuggingFaceTB/SmolLM2-135M-Instruct using 5,000 examples sampled from SupraLabs/reasoning-corpus-4K-5M-v1.

The model was trained to place its reasoning trace inside <think> and </think> tags, followed by a separate final answer.

Training summary

Setting	Value
Training examples	5,000
Evaluation examples	128
Epochs	2
Maximum sequence length	4,096 tokens
Learning rate	3e-05
Training objective	Assistant-only causal cross-entropy
Parameter training	Full model
Precision	bfloat16/float16 depending on training GPU

The system and user portions were masked from the loss. Samples exceeding the maximum context length were rejected instead of being cut through the middle of a reasoning trace.

Usage

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "YOUR_USERNAME/SmolLM2-135M-Reasoning-5K"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

messages = [
    {
        "role": "system",
        "content": 'You are a helpful AI assistant. For difficult problems, reason carefully inside <think> and </think> tags, then provide a clear final answer.',
    },
    {
        "role": "user",
        "content": "A farmer has 17 sheep. All but 9 run away. How many remain?",
    },
]

inputs = tokenizer.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    return_tensors="pt",
    return_dict=True,
).to(model.device)

with torch.inference_mode():
    output = model.generate(
        **inputs,
        max_new_tokens=256,
        do_sample=False,
        repetition_penalty=1.05,
    )

new_tokens = output[0, inputs["input_ids"].shape[1]:]
print(tokenizer.decode(new_tokens, skip_special_tokens=False))

Reasoning format

The expected assistant format is:

<think>
Internal reasoning trace
</think>

Final answer

This small model is experimental. It should not be assumed to produce correct reasoning merely because it emits a structured reasoning trace.

Files

training_info.json records the training configuration, any metrics found in the local output directory, and SHA-256 hashes of the uploaded weight files.

License

The model follows the Apache 2.0 license used by the base SmolLM2 model. Review the base model repository and source dataset for their complete terms.

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Safetensors

Model size

0.1B params

Tensor type

BF16

Model tree for Ma7ee7/SmolLM2-135M-Reasoning-5K

Base model

HuggingFaceTB/SmolLM2-135M

Quantized

HuggingFaceTB/SmolLM2-135M-Instruct

Finetuned

(355)

this model

Quantizations

1 model

Ma7ee7
/

SmolLM2-135M-Reasoning-5K