Instructions to use texdata/Qwen3.6-35B-A3B-Slovenian with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use texdata/Qwen3.6-35B-A3B-Slovenian with Transformers:

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

pipe = pipeline("text-generation", model="texdata/Qwen3.6-35B-A3B-Slovenian")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("texdata/Qwen3.6-35B-A3B-Slovenian")
model = AutoModelForMultimodalLM.from_pretrained("texdata/Qwen3.6-35B-A3B-Slovenian")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.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(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use texdata/Qwen3.6-35B-A3B-Slovenian with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "texdata/Qwen3.6-35B-A3B-Slovenian"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "texdata/Qwen3.6-35B-A3B-Slovenian",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/texdata/Qwen3.6-35B-A3B-Slovenian

SGLang

How to use texdata/Qwen3.6-35B-A3B-Slovenian 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 "texdata/Qwen3.6-35B-A3B-Slovenian" \
    --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": "texdata/Qwen3.6-35B-A3B-Slovenian",
		"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 "texdata/Qwen3.6-35B-A3B-Slovenian" \
        --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": "texdata/Qwen3.6-35B-A3B-Slovenian",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use texdata/Qwen3.6-35B-A3B-Slovenian with Docker Model Runner:
```
docker model run hf.co/texdata/Qwen3.6-35B-A3B-Slovenian
```

Qwen3.6-35B-A3B — Slovenian (merged full model)

Merged full model (bf16) of Qwen/Qwen3.6-35B-A3B with Slovenian fluency, knowledge, and en↔sl translation baked in (continued-pretraining + SFT). This is the LoRA adapter texdata/Qwen3.6-35B-A3B-Slovenian-LoRA already merged into the base, so you can load it directly. MTP (multi-token-prediction) tensors are preserved (model-mtp.safetensors) for speculative decoding.

On held-out evals vs the untuned base: Slovenian-LLM-Eval acc_norm 0.623 → 0.654; translation BLEU en→sl 23.8 → 26.3, sl→en 30.9 → 35.0. Reasoning model.

💡 For plug-and-play chat, a GGUF build (LM Studio / llama.cpp) is at texdata/Qwen3.6-35B-A3B-Slovenian-GGUF; the LoRA adapter alone is at texdata/Qwen3.6-35B-A3B-Slovenian-LoRA.

⚠️ How to run — load in 4-bit (not bf16)

This qwen3_5_moe arch has a broken fp16/bf16 forward in current transformers (zero hidden state → constant logits → garbage). Even though the weights here are bf16, load in 4-bit (bitsandbytes nf4) — the quantization it was trained/served under — or use the GGUF build. (vLLM does not run this arch on Blackwell sm_120 GPUs.)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig

repo = "texdata/Qwen3.6-35B-A3B-Slovenian"
bnb  = BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_quant_type="nf4",
                          bnb_4bit_compute_dtype=torch.bfloat16, bnb_4bit_use_double_quant=True)
tok   = AutoTokenizer.from_pretrained(repo, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(repo, quantization_config=bnb,
                                             trust_remote_code=True, device_map="auto")

# reasoning model: enable_thinking=False for direct answers/translation (else it spends tokens on <think>)
msg  = [{"role": "user", "content": "Prevedi v slovenščino: Good morning!"}]
text = tok.apply_chat_template(msg, add_generation_prompt=True, tokenize=False, enable_thinking=False)
out  = model.generate(**tok(text, return_tensors="pt").to(model.device), max_new_tokens=128)
print(tok.decode(out[0], skip_special_tokens=True))

Model details

Type: merged full model (bf16), MoE qwen3_5_moe (16 shards + model-mtp.safetensors).
Base: Qwen/Qwen3.6-35B-A3B (Apache-2.0, official — not an uncensored variant).
Adaptation: CPT + SFT LoRA (sft_qwen35a3b_sl) merged in; template: qwen. MTP preserved.

License & data provenance

Base Qwen/Qwen3.6-35B-A3B is Apache-2.0. CPT data is open-licensed (attribution applies) → this repo is license: other:

Data	License
Slovenian Wikipedia (CPT)	CC BY-SA 4.0 (attribution + ShareAlike)
FineWeb2 sl (CPT)	ODC-BY 1.0

Attribute the above. Comply with each source's terms before commercial use.

Citation

Cite this work (Tadej Fius, MediaAtlas Ltd):

@misc{fius2026qwen35bsl,
  title        = {Qwen3.6-35B-A3B Slovenian (merged)},
  author       = {Fius, Tadej},
  year         = {2026},
  publisher    = {MediaAtlas Ltd},
  howpublished = {Hugging Face},
  url          = {https://huggingface.co/texdata/Qwen3.6-35B-A3B-Slovenian}
}

Upstream / source citations:

@misc{qwen3, title={Qwen3 Technical Report}, author={{Qwen Team}}, year={2025}, url={https://huggingface.co/Qwen}}
@misc{wikipedia_sl, title={Slovenian Wikipedia}, author={{Wikimedia Foundation}}, note={CC BY-SA 4.0}, url={https://sl.wikipedia.org}}
@misc{penedo2024fineweb2, title={FineWeb2: A sparkling update with 1000s of languages}, author={Penedo, G. and others}, year={2024}, url={https://huggingface.co/datasets/HuggingFaceFW/fineweb-2}}

CPT data: Slovenian Wikipedia (CC BY-SA 4.0) and FineWeb2-sl (ODC-BY 1.0).

Downloads last month: 535

Safetensors

Model size

35B params

Tensor type

BF16

Model tree for texdata/Qwen3.6-35B-A3B-Slovenian

Base model

Qwen/Qwen3.6-35B-A3B

Finetuned

(170)

this model