Instructions to use Sayankotor/qwen35-122b-a10b-k4e2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Sayankotor/qwen35-122b-a10b-k4e2 with Transformers:

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

pipe = pipeline("text-generation", model="Sayankotor/qwen35-122b-a10b-k4e2", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoProcessor, AutoModel

processor = AutoProcessor.from_pretrained("Sayankotor/qwen35-122b-a10b-k4e2", trust_remote_code=True)
model = AutoModel.from_pretrained("Sayankotor/qwen35-122b-a10b-k4e2", trust_remote_code=True, device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
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]:]))

Trellis

How to use Sayankotor/qwen35-122b-a10b-k4e2 with Trellis:

# No code snippets available yet for this library.

# To use this model, check the repository files and the library's documentation.

# Want to help? PRs adding snippets are welcome at:
# https://github.com/huggingface/huggingface.js

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Sayankotor/qwen35-122b-a10b-k4e2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Sayankotor/qwen35-122b-a10b-k4e2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Sayankotor/qwen35-122b-a10b-k4e2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Sayankotor/qwen35-122b-a10b-k4e2

SGLang

How to use Sayankotor/qwen35-122b-a10b-k4e2 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 "Sayankotor/qwen35-122b-a10b-k4e2" \
    --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": "Sayankotor/qwen35-122b-a10b-k4e2",
		"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 "Sayankotor/qwen35-122b-a10b-k4e2" \
        --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": "Sayankotor/qwen35-122b-a10b-k4e2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Sayankotor/qwen35-122b-a10b-k4e2 with Docker Model Runner:
```
docker model run hf.co/Sayankotor/qwen35-122b-a10b-k4e2
```

qwen35-122b-a10b-k4e2 / config.json

Sayankotor

Upload QTIP K4/K2 quantized Qwen3.5-122B-A10B (weights + self-loading code)

5a15552 verified 8 days ago

Raw

History Blame Contribute Delete

10.6 kB

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	"text_config": {
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	"model_type": "qwen3_5_moe",
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	"spatial_merge_size": 2,
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	},
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	"vision_start_token_id": 248053,
	"auto_map": {
	"AutoModel": "modeling_qwen3_5_moe_quantized.Qwen3_5MoeQuantizedForConditionalGeneration",
	"AutoModelForCausalLM": "modeling_qwen3_5_moe_quantized.Qwen3_5MoeQuantizedForConditionalGeneration"
	}
	}