Instructions to use diffutron/DiffutronLM-0.3B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use diffutron/DiffutronLM-0.3B-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="diffutron/DiffutronLM-0.3B-Instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMaskedLM

tokenizer = AutoTokenizer.from_pretrained("diffutron/DiffutronLM-0.3B-Instruct")
model = AutoModelForMaskedLM.from_pretrained("diffutron/DiffutronLM-0.3B-Instruct")
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 diffutron/DiffutronLM-0.3B-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "diffutron/DiffutronLM-0.3B-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "diffutron/DiffutronLM-0.3B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/diffutron/DiffutronLM-0.3B-Instruct

SGLang

How to use diffutron/DiffutronLM-0.3B-Instruct 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 "diffutron/DiffutronLM-0.3B-Instruct" \
    --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": "diffutron/DiffutronLM-0.3B-Instruct",
		"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 "diffutron/DiffutronLM-0.3B-Instruct" \
        --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": "diffutron/DiffutronLM-0.3B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use diffutron/DiffutronLM-0.3B-Instruct with Docker Model Runner:
```
docker model run hf.co/diffutron/DiffutronLM-0.3B-Instruct
```

suayptalha commited on Mar 9

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@@ -54,7 +54,7 @@ To unlock generative instruction-following capabilities, we utilized a two-stage
 The model was evaluated on a representative subset of the **CETVEL Benchmark Suite**. DiffutronLM-0.3B (2nd Stage) demonstrates remarkable parameter efficiency, outperforming models up to 7x its size (e.g., Kumru-2B and TURNA-1.1B) on average scores.
-| Benchmark | Diffutron-1st (0.3B) | Diffutron-2nd (0.3B) | TURNA (1.1B) | Kumru (2B) | Kanarya (2B) | Llama-3.2 (3B) | Trendyol (7B) | Aya-101 (13B) |
 | :--- | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
 | **Belebele_TR** | 22.22 | 27.00 | 22.56 | 29.00 | 28.11 | **55.78** | 36.22 | 22.89 |
 | **EXAMS_TR** | 25.95 | 27.74 | 23.66 | **30.03** | **30.03** | 26.21 | 28.50 | 22.90 |

 The model was evaluated on a representative subset of the **CETVEL Benchmark Suite**. DiffutronLM-0.3B (2nd Stage) demonstrates remarkable parameter efficiency, outperforming models up to 7x its size (e.g., Kumru-2B and TURNA-1.1B) on average scores.
+| Benchmark | Diffutron-1st-Stage (0.3B) | Diffutron-2nd-Stage (0.3B) | TURNA (1.1B) | Kumru (2B) | Kanarya (2B) | Llama-3.2 (3B) | Trendyol (7B) | Aya-101 (13B) |
 | :--- | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
 | **Belebele_TR** | 22.22 | 27.00 | 22.56 | 29.00 | 28.11 | **55.78** | 36.22 | 22.89 |
 | **EXAMS_TR** | 25.95 | 27.74 | 23.66 | **30.03** | **30.03** | 26.21 | 28.50 | 22.90 |