Instructions to use lordx64/Qwen3.6-35B-A3B-Kimi-K2.6-Reasoning-Distilled with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use lordx64/Qwen3.6-35B-A3B-Kimi-K2.6-Reasoning-Distilled with Transformers:

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

pipe = pipeline("text-generation", model="lordx64/Qwen3.6-35B-A3B-Kimi-K2.6-Reasoning-Distilled")
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("lordx64/Qwen3.6-35B-A3B-Kimi-K2.6-Reasoning-Distilled")
model = AutoModelForMultimodalLM.from_pretrained("lordx64/Qwen3.6-35B-A3B-Kimi-K2.6-Reasoning-Distilled")
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 lordx64/Qwen3.6-35B-A3B-Kimi-K2.6-Reasoning-Distilled with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "lordx64/Qwen3.6-35B-A3B-Kimi-K2.6-Reasoning-Distilled"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "lordx64/Qwen3.6-35B-A3B-Kimi-K2.6-Reasoning-Distilled",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/lordx64/Qwen3.6-35B-A3B-Kimi-K2.6-Reasoning-Distilled

SGLang

How to use lordx64/Qwen3.6-35B-A3B-Kimi-K2.6-Reasoning-Distilled 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 "lordx64/Qwen3.6-35B-A3B-Kimi-K2.6-Reasoning-Distilled" \
    --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": "lordx64/Qwen3.6-35B-A3B-Kimi-K2.6-Reasoning-Distilled",
		"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 "lordx64/Qwen3.6-35B-A3B-Kimi-K2.6-Reasoning-Distilled" \
        --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": "lordx64/Qwen3.6-35B-A3B-Kimi-K2.6-Reasoning-Distilled",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use lordx64/Qwen3.6-35B-A3B-Kimi-K2.6-Reasoning-Distilled with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for lordx64/Qwen3.6-35B-A3B-Kimi-K2.6-Reasoning-Distilled to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for lordx64/Qwen3.6-35B-A3B-Kimi-K2.6-Reasoning-Distilled to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for lordx64/Qwen3.6-35B-A3B-Kimi-K2.6-Reasoning-Distilled to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="lordx64/Qwen3.6-35B-A3B-Kimi-K2.6-Reasoning-Distilled",
    max_seq_length=2048,
)

Docker Model Runner
How to use lordx64/Qwen3.6-35B-A3B-Kimi-K2.6-Reasoning-Distilled with Docker Model Runner:
```
docker model run hf.co/lordx64/Qwen3.6-35B-A3B-Kimi-K2.6-Reasoning-Distilled
```

lordx64 commited on Apr 27

Commit

2940d54

verified ·

1 Parent(s): 4bac31f

Eval: GSM8K 92.67% (8-shot CoT strict-match, 300 examples on H200)

Browse files

Files changed (1) hide show

README.md +7 -5

README.md CHANGED Viewed

@@ -143,17 +143,19 @@ The initial plan was full LoRA including the MoE expert FFNs (`gate_proj/up_proj
 ## Evaluation
-Formal benchmarks are pending. Numbers will land here once the [`training/eval.py`](https://github.com/lordx64/distillation/blob/main/training/eval.py) sweep (vLLM + lm-eval-harness, with `<think>` block stripping) finishes.
 | Benchmark | Setup | Score | Status |
-|---|---|---|---|
-| GSM8K | 8-shot CoT, 300 examples | _pending_ | 🟡 in queue |
-| MMLU-Pro | 5-shot, 500 examples | _pending_ | 🟡 in queue |
-| GPQA Diamond | 0-shot CoT zero-shot, 198 problems | _pending_ | 🟡 in queue |
 | AIME 2024 | 0-shot, 30 problems | _pending_ | 🟡 in queue |
 | AIME 2025 | 0-shot, 30 problems | _pending_ | 🟡 in queue |
 | MATH-500 | 0-shot, 100 problems | _pending_ | 🟡 in queue |
 Comparison baselines will include:
 - `Qwen/Qwen3.6-35B-A3B` (base, untuned)

 ## Evaluation
+Benchmark numbers land here as evaluation runs complete. Methodology: vLLM + lm-eval-harness with a custom `<think>`-stripping wrapper, `max_gen_toks=16384` to allow full Kimi-style reasoning chains before answer extraction. See [`training/eval.py`](https://github.com/lordx64/distillation/blob/main/training/eval.py).
 | Benchmark | Setup | Score | Status |
+|---|---|---:|---|
+| **GSM8K** | 8-shot CoT, 300 examples, strict-match | **92.67%** | ✅ done |
+| MMLU-Pro | 5-shot, 500 examples per subject, custom-extract | _under investigation_ | 🟠 extraction issue (see note) |
+| GPQA Diamond | 0-shot CoT zeroshot, 198 problems | _pending_ | 🟡 in queue |
 | AIME 2024 | 0-shot, 30 problems | _pending_ | 🟡 in queue |
 | AIME 2025 | 0-shot, 30 problems | _pending_ | 🟡 in queue |
 | MATH-500 | 0-shot, 100 problems | _pending_ | 🟡 in queue |
+**Note on MMLU-Pro**: a first scored run produced 14.76% overall, but the per-subject split is suspicious — `mmlu_pro_math` at 64.2% and `mmlu_pro_computer_science` at 60.2% are strong, while `mmlu_pro_biology` and `mmlu_pro_philosophy` returned exactly 0% and most prose-heavy subjects sit below 5%. That pattern indicates an answer-extraction regex mismatch with this model's reasoning style on humanities questions, not a model-capability failure. A diagnostic re-run with `log_samples=True` is queued so the actual model outputs can be inspected and the extractor adjusted; the clean number will replace this row once that's done.
 Comparison baselines will include:
 - `Qwen/Qwen3.6-35B-A3B` (base, untuned)