Instructions to use dataslab/DSLM-LST-35B-A3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use dataslab/DSLM-LST-35B-A3B with Transformers:

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

pipe = pipeline("image-text-to-text", model="dataslab/DSLM-LST-35B-A3B")
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("dataslab/DSLM-LST-35B-A3B")
model = AutoModelForMultimodalLM.from_pretrained("dataslab/DSLM-LST-35B-A3B")
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 dataslab/DSLM-LST-35B-A3B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "dataslab/DSLM-LST-35B-A3B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "dataslab/DSLM-LST-35B-A3B",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/dataslab/DSLM-LST-35B-A3B

SGLang

How to use dataslab/DSLM-LST-35B-A3B 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 "dataslab/DSLM-LST-35B-A3B" \
    --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": "dataslab/DSLM-LST-35B-A3B",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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 "dataslab/DSLM-LST-35B-A3B" \
        --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": "dataslab/DSLM-LST-35B-A3B",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use dataslab/DSLM-LST-35B-A3B with Docker Model Runner:
```
docker model run hf.co/dataslab/DSLM-LST-35B-A3B
```

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DSLM-LST-35B-A3B

DSLM-LST-35B-A3B is a Qwen/Qwen3.5-35B-A3B derivative refined with our in-house Language Selection Tuning (LST) technique. The goal is to suppress unwanted Chinese-character generation when the model serves non-Chinese (English / Korean / Japanese etc.) users.

The pipeline is Qwen/Qwen3.5-35B-A3B → LST tuning (output-head centric). The adjustment is intentionally minimal in scope — most of the network, including the entire vision tower and all expert weights, is preserved bit-for-bit from the base model, so vision and multimodal capabilities are unchanged and the result is a drop-in replacement that only mitigates unintended Chinese-token leakage. For memory- and throughput-efficient 4-bit serving, see the GPTQ INT4 quantization derived from this model.

The architecture is Qwen3_5MoeForConditionalGeneration: a composite multimodal vision-language model with a MoE text backbone (256 experts, top-8 routing), a linear-attention + full-attention 4:1 hybrid layout, and a 27-block vision tower.

Why LST?

Multilingual LLMs trained on heavily skewed corpora (e.g., Qwen on Chinese-rich data) tend to leak the dominant training language regardless of prompt language — a phenomenon known as language confusion. For Korean users, Chinese characters sometimes appear in the middle of an otherwise-Korean answer, hurting readability and trust.

Language Selection Tuning (LST) addresses this in a learning-based manner. Unlike post-hoc decoding tricks (vocabulary masking, banned-token lists), LST adjusts the model's internal language-selection behavior. (The exact algorithm and training configuration are proprietary and not disclosed in this release.)

Key Properties

Minimal footprint. LST tuning modifies essentially only the output head; the tokenizer, chat template, vision tower, MoE experts, and attention weights are preserved from the base model (see Modification Footprint).
Selectivity preserved. When the user explicitly asks for Chinese, the model still produces fluent Chinese — this is not blanket suppression.
Full-precision fidelity. Released in bf16 (~70 GB), this is the unquantized source model; reasoning performance tracks the base model closely (see Benchmarks).

Modification Footprint

LST tuning was verified by a tensor-by-tensor diff against the base Qwen/Qwen3.5-35B-A3B. Of the 1,026 shared tensors, 995 are bit-identical to the base model — only the output head is actually trained:

Tuned: lm_head.weight only — rel-L2 ≈ 0.075, cosine ≈ 0.997, norm ratio ≈ 1.000 (no scale change). About 21.9 % of the 248,320 vocab rows are updated, spread across the vocabulary rather than in a contiguous block — a distributed, mild recalibration of the output head.
Frozen (bit-identical to base): embed_tokens, all 256 MoE experts, the shared expert, the router gate, self-attention, linear-attention projections / conv / SSM (A_log / dt_bias / conv1d), every layernorm, and the entire vision tower.
The 30 linear_attn.norm.weight tensors show a sub-0.2 % difference that is not training — it is an fp32→bf16 down-cast artifact (the base stores these norms in fp32; this model stores them in bf16, bit-identical to bf16(base)). Functionally equivalent for bf16 serving.

Requirements

transformers >= 5.9 — required for the qwen3_5_moe architecture, the modern tokenizer backend, and the consolidated processor format.
No MTP head. The base model's Multi-Token Prediction module is not included (this is a standard-inference model), so speculative decoding via the MTP head is not available. All other standard inference is unaffected.
vLLM serving: served in bf16. The full bf16 model is ~70 GB and benefits from tensor parallelism across multiple GPUs.

Quickstart (vLLM, recommended)

vllm serve dataslab/DSLM-LST-35B-A3B \
    --tensor-parallel-size 2 \
    --port 8000 \
    --gpu-memory-utilization 0.90 \
    --reasoning-parser qwen3        # exposes <think> trace via OpenAI API
    # --max-model-len 16384         # cap context to shrink KV cache (default: 262,144)

Use with transformers

Non-Thinking mode (recommended for fast chat)

import torch
from transformers import AutoTokenizer, AutoModelForImageTextToText

REPO = "dataslab/DSLM-LST-35B-A3B"

tokenizer = AutoTokenizer.from_pretrained(REPO)
model = AutoModelForImageTextToText.from_pretrained(
    REPO,
    dtype=torch.bfloat16,
    device_map="auto",
)

messages = [
    {"role": "user", "content": "한반도 주변에 가장 흔한 점토광물은?"},
]

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

out = model.generate(**inputs, max_new_tokens=256)
text = tokenizer.decode(out[0][inputs.input_ids.shape[-1]:],
                       skip_special_tokens=True)
print(text)

Thinking mode (recommended for complex reasoning)

Either use thinking_budget (e.g., vLLM's --reasoning-parser qwen3) or give max_new_tokens enough headroom (e.g., 8,192 + 256 = 8,448). Caveat: without a thinking_budget cap, a too-small max_new_tokens can be fully consumed inside <think> and the answer never gets emitted.

# ... tokenizer / model loaded as above ...

THINKING_BUDGET = 8192   # max tokens inside <think>
ANSWER_TOKENS   = 256    # tokens after </think>

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

out = model.generate(**inputs, max_new_tokens=THINKING_BUDGET + ANSWER_TOKENS)
text = tokenizer.decode(out[0][inputs.input_ids.shape[-1]:],
                       skip_special_tokens=True)
print(text)

Why AutoModelForImageTextToText? The declared architecture Qwen3_5MoeForConditionalGeneration is a composite class wrapping both the text decoder and the vision tower. Loading via AutoModelForCausalLM works for text-only inference but strips the vision submodule and may produce a config that downstream tools (e.g., vLLM) reject. For a pure text causal-LM handle, use model.language_model after loading.

Benchmark Results

The DSLM-LST-35B-A3B column is this release; the other columns are the base model and DSLM-LST-35B-A3B-GPTQ-Int4, the GPTQ INT4 quantization derived from this model.

Evaluation Metrics

(1) Selectivity

Refusal rate on explicit Chinese requests — the fraction of cases where the model fails to produce Chinese even though the user explicitly asked for it. Lower is better (respects user intent).

Lower better (~0): produces Chinese when asked (respects user intent).
Higher worse (~1): refuses Chinese even when asked (blanket suppression).

Metric	Benchmark Dataset
`chin_refusal` ↓	In-house 1,000-prompt Chinese elicitation set (e.g., `How do you say '사랑' in Chinese?` or the Python + Chinese-comment prompt)

(2) Chinese-leak suppression

Korean prompts → Korean answers expected; any Chinese token leaked into the answer is a failure. Metric is the clean-Korean response ratio.

Higher better (~1): Korean answers stay fully Korean (no Chinese tokens leaked).
Lower worse (~0): Chinese tokens leak into otherwise-Korean answers.

Metric	Benchmark Dataset
`chin_cs` ↑	KMMLU Computer Science subjects (free-form Korean generation)
`chin_ie` ↑	KMMLU Industrial Engineering subjects (free-form Korean generation)
`chin_total` ↑	KMMLU (free-form Korean generation)

(3) Reasoning / task performance

Metric	Benchmark Dataset
`acc_cs` ↑	KMMLU Computer Science subjects (multiple-choice log-likelihood comparison)
`acc_ie` ↑	KMMLU Industrial Engineering subjects (multiple-choice log-likelihood comparison)
`acc_total` ↑	KMMLU (multiple-choice log-likelihood comparison)
`HumanEval` ↑	HumanEval (pass@1)
`GSM8K` ↑	GSM8K (exact-match accuracy)

Chinese Suppression (Thinking mode)

Evaluated with enable_thinking=True. The DSLM-LST-35B-A3B column is this bf16 release.

Metric	Qwen3.5-35B-A3B (base)	DSLM-LST-35B-A3B	DSLM-LST-35B-A3B-GPTQ-Int4
(1) Selectivity
chin_refusal ↓	0.005	0.030	0.039
(2) Chinese-leak suppression
chin_cs ↑	0.989	0.998	0.999
chin_ie ↑	0.983	0.994	0.993
chin_total ↑	0.9754	0.9901	0.9895
(3) Reasoning / Task performance
acc_cs ↑	0.869	0.869	0.865
acc_ie ↑	0.622	0.622	0.602
acc_total ↑	0.6411	0.6411	0.6340
HumanEval ↑	0.7683	0.7378	0.7561
GSM8K ↑	0.8347	0.8347	0.8810

LST improves Chinese-leak suppression (chin_total 0.9754 → 0.9901) and keeps it selective (chin_refusal stays low at 0.030; the model still produces Chinese on request), while leaving KMMLU accuracy unchanged (acc_total 0.6411, identical to base) and math reasoning intact (GSM8K 0.8347). HumanEval dips slightly (0.7683 → 0.7378). The GPTQ INT4 quantization derived from this model stays close on all metrics (see the GPTQ-Int4 model card).

Example Outputs

Asked in Korean which NC-programming function–address pair is mismatched, Qwen3.5-35B-A3B leaks 22 Chinese tokens (主轴 ×11 — the Chinese word for "spindle") into its answer. DSLM-LST-35B-A3B answers the same prompt entirely in Korean (0 Chinese tokens, writing 스핀들), and both models still select the correct option — the leak is suppressed without hurting accuracy. The examples below show this behavior across more KMMLU domains and inside the reasoning (<think>) trace.

All examples are drawn verbatim from the in-house Chinese-leakage evaluation (enable_thinking=True). For each KMMLU prompt the base Qwen3.5-35B-A3B slips Chinese characters into an otherwise-Korean answer, while DSLM-LST-35B-A3B answers the same prompt entirely in Korean (zero Chinese characters).

Domain-wise Leakage Comparisons

Computer Science

Prompt: BIOS Setup의 기본기능으로 잘못된 것은?

Qwen3.5-35B-A3B (leaks `出荷`)	DSLM-LST-35B-A3B (clean Korean)
… BIOS Setup 의 올바른 기능: • 시스템 시간 및 날짜 설정 • 부팅 장치 순서 설정 (Boot Order) • 보안 설정 / 전원 관리 설정 • CMOS 초기화: 설정을 공장出荷 상태로 되돌립니다.	… BIOS Setup 의 주요 (올바른) 기능: 1. 시스템 날짜 및 시간 설정 2. 부팅 순서 (Boot Order) 설정 5. 하드웨어 상태 모니터링: CPU 온도, 팬 속도, 전압 등을 확인합니다. 6. 기본값 복원 (Load Defaults): 설정을 초기화하여 시스템 안정성을 확보합니다. …

Industrial Engineering

Prompt: 머시닝센터에서 공작물 가공시 주의해야 할 사항으로 올바른 것은?

Qwen3.5-35B-A3B (leaks 卷入) DSLM-LST-35B-A3B (clean Korean)

Qwen3.5-35B-A3B (leaks `卷入`)	DSLM-LST-35B-A3B (clean Korean)
… 안전 수칙 (보호구) • 올바른 사항: 안전 고글을 착용하고, 회전하는 스핀들 근처에서는 장갑을 착용하지 않아야 합니다. • 이유: 장갑을 끼고 회전체 근처에서 작업할 경우 장갑이 걸려 손이卷入 (감김) 될 위험이 매우 큽니다.	… 올바른 주의사항 (정답 후보) 1. 공작물의 단단한 고정: 클램프나 바이스 (Vise) 로 단단히 고정해야 합니다. 5. 절삭 중 측정 금지: 공구가 작동하는 동안에는 절대 측정하거나 접촉해서는 안 됩니다. 6. 회전부 장갑 착용 금지: 회전하는 척 (Spindle) 근처에서는 끼임 사고의 위험이 있으므로 금지됩니다. …

… 안전 수칙 (보호구)
• 올바른 사항: 안전 고글을 착용하고, 회전하는 스핀들 근처에서는 장갑을 착용하지 않아야 합니다.
• 이유: 장갑을 끼고 회전체 근처에서 작업할 경우 장갑이 걸려 손이卷入 (감김) 될 위험이 매우 큽니다.

… 올바른 주의사항 (정답 후보)
1. 공작물의 단단한 고정: 클램프나 바이스 (Vise) 로 단단히 고정해야 합니다.
5. 절삭 중 측정 금지: 공구가 작동하는 동안에는 절대 측정하거나 접촉해서는 안 됩니다.
6. 회전부 장갑 착용 금지: 회전하는 척 (Spindle) 근처에서는 끼임 사고의 위험이 있으므로 금지됩니다. …

Law

Prompt: 경비조사업무의 과정으로 옳지 않은 것은?

Qwen3.5-35B-A3B (leaks `它们是`)	DSLM-LST-35B-A3B (clean Korean)
… 경비조사업무의 올바른 과정 (정답에 해당하는 내용) 만약 선택지에 이 내용들이 있다면它们是 옳은 것입니다. 1. 사전 준비 (계약 및 현장 조사) 2. 인력 배치 및 교육 3. 경비 수행 (순찰 및 출입 통제) …	… 경비조사업무의 올바른 과정 (표준) 1. 계약 체결: 의뢰인 (고객) 과 경비계약 체결 2. 현장 실사: 경비 대상지의 위험 요소, 시설 현황 등을 조사 3. 경비 계획 수립: 인력 배치, 순찰 경로, 비상 대응 계획 등 수립 …

Cross-lingual Selectivity

When the user explicitly asks for Chinese, DSLM-LST-35B-A3B readily produces it. The previous examples showed the model avoiding unwanted Chinese inside an otherwise-Korean answer; the example below shows it emitting Chinese fluently — in both code comments and prose — when the user's instruction calls for it.

Prompt: 두 수의 최대공약수를 구하는 파이썬 함수를 작성해주세요. 설명은 중국어로 해주세요.

DSLM-LST-35B-A3B (Python code with Chinese comments + Chinese explanation):

def gcd(a, b):
    """
    计算两个整数的最大公约数 (Greatest Common Divisor)
    使用欧几里得算法 (辗转相除法)
    """
    # 确保处理负数的情况（最大公约数通常定义为正数）
    a = abs(a)
    b = abs(b)

    # 当 b 不为 0 时，循环执行
    while b != 0:
        # 将 b 的值赋给 a，将 a 除以 b 的余数赋给 b
        a, b = b, a % b

    # 当 b 为 0 时，a 即为最大公约数
    return a

**代码说明 (Chinese explanation):**
1. **函数定义**：`def gcd(a, b):` 定义了一个接受两个参数 `a` 和 `b` 的函数。
2. **绝对值处理**：`abs(a)` 和 `abs(b)` 确保即使输入负数，也能正确计算。
3. **循环逻辑**：`a, b = b, a % b` 是核心步骤，利用取模运算不断缩小数值，直到余数为 0 —— 这个过程就是**辗转相除法**。

Thinking-trace Suppression

The base model's <think> block leaks Chinese even more severely than its final answer: once the reasoning gets stuck, it slips into Chinese characters and loops on its own hypotheses. DSLM-LST-35B-A3B suppresses that leakage inside the thinking block too.

The prompt below uses a non-standard / likely-garbled term (장촌식 제도법). Neither model resolves the obscure term — so this example isolates the leak-suppression behavior in the reasoning trace, not accuracy. The base model spirals into a self-doubt loop, re-emitting the same Chinese-character hypothesis (長村) 210 times; DSLM-LST-35B-A3B reasons through the same uncertainty but emits zero Chinese characters anywhere — neither in the <think> block nor in the user-facing answer.

Prompt: 장촌식 제도법의 특징이 아닌 것은?

Chinese-character counts (thinking budget = 8,192):

Metric	Qwen3.5-35B-A3B	DSLM-LST-35B-A3B
`<think>` block, Chinese characters	890	0
`<answer>` block, Chinese characters	leaks `長川式` / `長村` (11 chars)	0 (clean Korean)

Qwen3.5-35B-A3B <think> block (loops on a Chinese-character hypothesis):

*   **Wait, is it possible the user means "Jangchon (長村)" as a typo for "Jangchon (長村)"?**
*   **Wait, is it possible the user means "Jangchon (長村)" as a typo for "Jangchon (長村)"?**
*   **Wait, is it possible the user means "Jangchon (長村)" as a typo for "Jangchon (長村)"?**
    ... (≈210 such "Wait…" repetitions, emitting 890 Chinese characters) ...

Qwen3.5-35B-A3B Final answer (Chinese leak into Korean):

**장촌식 제도법**은 조선 시대 토지 제도와 관련된 용어로 보이나 …
유사한 발음이나 맥락으로 추정되는 **장천식 (長川式)** 또는 **장촌 (長村)** 관련
토지 제도 (예: 장천법, 장촌법 등) 와 관련하여 …

DSLM-LST-35B-A3B <think> block (same uncertainty, but English/Korean — 0 Chinese):

Here's a thinking process that leads to the answer:

1.  **Analyze the Request:**
    *   **Topic:** Jangchon-sik System of Law (장촌식 제도법).
    *   **Context:** ... "Jangchon-sik" (장촌식) is not a standard, widely recognized term
        in mainstream Korean legal history textbooks like "Gyeongguk Daejeon" (경국대전) ...
    *   **Result:** There is no standard historical term "Jangchon-sik System of Law".

    *   **Wait, is it possible the user is asking about "Jangcheon" (장천)?** No.
    *   **Wait, is it possible the user is asking about "Jangcheon" (장천)?** No.
        ... (loops on the same hypothesis, but in Hangul `장천` — never a Chinese character) ...

Considering the limited time by the user, I have to give the solution based on the thinking directly now.

Where the base model re-emits the Chinese hanzi 長村, DSLM-LST-35B-A3B loops on the Hangul 장천 instead — the self-doubt pattern persists, but not a single Chinese character is produced.

DSLM-LST-35B-A3B Final answer (clean Korean, 0 Chinese):

**장촌식 제도법**이라는 용어는 한국 법제사나 역사학에서 일반적으로 통용되는 표준
용어가 아닙니다. 따라서 이 질문은 특정 교재, 시험 문제, 혹은 가상의 설정에서 나온
것으로 보입니다.

하지만, 한국 법제사 관련 시험에서 유사한 발음이나 개념으로 자주 등장하는 용어는
다음과 같습니다. …

Both models treat 장촌식 제도법 as a non-standard term, but only the base model leaks Chinese characters into its reasoning and answer; DSLM-LST-35B-A3B stays entirely in Korean.

Limitations

Not an instruction-tuned chat model. The LST adjustment scope is minimal, so conversational behavior, instruction-following, and reasoning patterns are inherited from the base model — only unintended Chinese-token leakage is mitigated.
Degraded Chinese generation. Tasks that require Chinese output (Chinese translation, Chinese code comments, bilingual Q&A) will see lower quality; use the base Qwen3.5-35B-A3B for those.
No MTP / speculative decoding. The base model's Multi-Token Prediction head is not included, so MTP-based speculative decoding is unavailable; standard inference is unaffected.
Multimodal not re-benchmarked. The vision tower is kept in bf16 (unchanged), so multimodal behavior should be unaffected, but the vision pipeline was not separately re-benchmarked for this release.

License

This model is not available for public download. For a publicly available alternative, see DSLM-LST-9B. For commercial or research access to this model, please contact us.