Instructions to use SamsungSAILMontreal/Qwen3-4B-Instruct-2507-Math-Code-Fr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SamsungSAILMontreal/Qwen3-4B-Instruct-2507-Math-Code-Fr with Transformers:

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

pipe = pipeline("text-generation", model="SamsungSAILMontreal/Qwen3-4B-Instruct-2507-Math-Code-Fr")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("SamsungSAILMontreal/Qwen3-4B-Instruct-2507-Math-Code-Fr")
model = AutoModelForCausalLM.from_pretrained("SamsungSAILMontreal/Qwen3-4B-Instruct-2507-Math-Code-Fr")
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 SamsungSAILMontreal/Qwen3-4B-Instruct-2507-Math-Code-Fr with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SamsungSAILMontreal/Qwen3-4B-Instruct-2507-Math-Code-Fr"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SamsungSAILMontreal/Qwen3-4B-Instruct-2507-Math-Code-Fr",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/SamsungSAILMontreal/Qwen3-4B-Instruct-2507-Math-Code-Fr

SGLang

How to use SamsungSAILMontreal/Qwen3-4B-Instruct-2507-Math-Code-Fr 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 "SamsungSAILMontreal/Qwen3-4B-Instruct-2507-Math-Code-Fr" \
    --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": "SamsungSAILMontreal/Qwen3-4B-Instruct-2507-Math-Code-Fr",
		"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 "SamsungSAILMontreal/Qwen3-4B-Instruct-2507-Math-Code-Fr" \
        --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": "SamsungSAILMontreal/Qwen3-4B-Instruct-2507-Math-Code-Fr",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use SamsungSAILMontreal/Qwen3-4B-Instruct-2507-Math-Code-Fr with Docker Model Runner:
```
docker model run hf.co/SamsungSAILMontreal/Qwen3-4B-Instruct-2507-Math-Code-Fr
```

Qwen3-4B-Instruct-2507-Math-Code-Fr / README.md

bknyaz

Update README.md

ab7710f verified 3 months ago

preview code

raw

history blame contribute delete

3.23 kB

	---
	library_name: transformers
	tags:
	- small-lm
	- math
	- code
	- reasoning
	- slm
	- french
	- merge
	license: apache-2.0
	base_model:
	- SamsungSAILMontreal/Qwen3-4B-Instruct-2507
	- SamsungSAILMontreal/Qwen3-4B-Instruct-2507-Math
	- SamsungSAILMontreal/Qwen3-4B-Instruct-2507-Code
	- SamsungSAILMontreal/Qwen3-4B-Instruct-2507-Fr
	language:
	- fr
	- en
	---

	# Qwen3-4B-Instruct-2507-Math-Fr

	This model is obtained by merging [SamsungSAILMontreal/Qwen3-4B-Instruct-2507-Math](https://huggingface.co/SamsungSAILMontreal/Qwen3-4B-Instruct-2507-Math),
	[SamsungSAILMontreal/Qwen3-4B-Instruct-2507-Code](https://huggingface.co/SamsungSAILMontreal/Qwen3-4B-Instruct-2507-Code) and
	[SamsungSAILMontreal/Qwen3-4B-Instruct-2507-Fr](https://huggingface.co/SamsungSAILMontreal/Qwen3-4B-Instruct-2507-Fr).
	The model is used in the experiments described in https://bknyaz.github.io/blog/2026/meta-merge/.
	Single A100 was used for merging and evaluation.

	The following versions were used for merge/eval:

	- python >= 3.10
	- torch : 2.9.0+cu128
	- lm_eval : 0.4.9.1
	- vllm : 0.11.1
	- transformers : 4.57.6
	- datasets : 3.2.0
	- numpy : 2.2.6

	## Merging

	Merging was done using parameter averaging implemented in [merge_qwen.py](https://github.com/SamsungSAILMontreal/nino/blob/main/merge_qwen.py).

	## Evaluation

	Evaluation was done with lm_eval on the test split of [gsm8k](https://huggingface.co/datasets/openai/gsm8k),
	[french_bench (avg score)](https://github.com/EleutherAI/lm-evaluation-harness/tree/main/lm_eval/tasks/french_bench),
	[gsm8k-fr](https://huggingface.co/datasets/cmh/gsm8k_fr) and humaneval (instruct):

	```bash
	python -m lm_eval --model vllm --model_args pretrained=${model},tensor_parallel_size=1,dtype=auto,gpu_memory_utilization=0.9,data_parallel_size=1 \
	--tasks gsm8k,french_bench,gsm8k-fr,humaneval_instruct --batch_size 1 --apply_chat_template=True --confirm_run_unsafe_code --trust_remote_code
	```

	To evaluate on gsm8k-fr you can use our fork https://github.com/bknyaz/lm-evaluation-harness/tree/main/lm_eval/tasks/gsm8k.

	### Results

	\| Model \| gsm8k \| french \| gsm8k-fr \| humaneval_instruct \| avg \|
	\|-------------------------------------\|--------\|--------\|----------\|--------------------\|------\|
	\| Qwen3-4B-Instruct-2507 \| 80.4 \| 43.1 \| 66.0 \| 90.2 \| 69.9 \|
	\| Qwen3-4B-Instruct-2507-Math \| 76.8 \| 43.0 \| 65.3 \| 72.0 \| 64.3 \|
	\| Qwen3-4B-Instruct-2507-Fr \| 72.3 \| 45.7 \| 60.7 \| 74.4 \| 63.3 \|
	\| Qwen3-4B-Instruct-2507-Code \| 72.5 \| 45.4 \| 53.0 \| 76.2 \| 61.8 \|
	\| Qwen3-4B-Instruct-2507-Math-Code-Fr \| 82.9 \| 45.8 \| 69.8 \| 79.9 \| 69.6 \|

	## License

	Please refer to the license of the base models [SamsungSAILMontreal/Qwen3-4B-Instruct-2507-Math](https://huggingface.co/SamsungSAILMontreal/Qwen3-4B-Instruct-2507-Math),
	[SamsungSAILMontreal/Qwen3-4B-Instruct-2507-Code](https://huggingface.co/SamsungSAILMontreal/Qwen3-4B-Instruct-2507-Code) and
	[SamsungSAILMontreal/Qwen3-4B-Instruct-2507-Fr](https://huggingface.co/SamsungSAILMontreal/Qwen3-4B-Instruct-2507-Fr).