Instructions to use sepsy070716/Qwen3.5-4B-A3B-Student-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sepsy070716/Qwen3.5-4B-A3B-Student-v2 with Transformers:

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

pipe = pipeline("text-generation", model="sepsy070716/Qwen3.5-4B-A3B-Student-v2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("sepsy070716/Qwen3.5-4B-A3B-Student-v2")
model = AutoModelForCausalLM.from_pretrained("sepsy070716/Qwen3.5-4B-A3B-Student-v2", device_map="auto")
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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use sepsy070716/Qwen3.5-4B-A3B-Student-v2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sepsy070716/Qwen3.5-4B-A3B-Student-v2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sepsy070716/Qwen3.5-4B-A3B-Student-v2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/sepsy070716/Qwen3.5-4B-A3B-Student-v2

SGLang

How to use sepsy070716/Qwen3.5-4B-A3B-Student-v2 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 "sepsy070716/Qwen3.5-4B-A3B-Student-v2" \
    --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": "sepsy070716/Qwen3.5-4B-A3B-Student-v2",
		"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 "sepsy070716/Qwen3.5-4B-A3B-Student-v2" \
        --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": "sepsy070716/Qwen3.5-4B-A3B-Student-v2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use sepsy070716/Qwen3.5-4B-A3B-Student-v2 with Docker Model Runner:
```
docker model run hf.co/sepsy070716/Qwen3.5-4B-A3B-Student-v2
```

Qwen3.5-4B-A3B-Student-v2 / evaluation /multilingual_lm_loss.json

sepsy070716

Release Qwen3.5-4B-A3B Student v2 with evaluation evidence

40bc205 verified about 2 months ago

Raw History Blame Contribute Delete

2.24 kB

	{
	"sequence_length": 128,
	"documents_per_language": 4,
	"languages": [
	"de",
	"en",
	"es",
	"ja",
	"ko",
	"zh"
	],
	"models": {
	"qwen35_2b": {
	"ko": {
	"loss": 3.2937907576560974,
	"documents": 4,
	"tokens": 512
	},
	"en": {
	"loss": 2.9049559831619263,
	"documents": 4,
	"tokens": 512
	},
	"zh": {
	"loss": 3.7243993878364563,
	"documents": 4,
	"tokens": 512
	},
	"ja": {
	"loss": 3.160854697227478,
	"documents": 4,
	"tokens": 512
	},
	"es": {
	"loss": 2.9567737579345703,
	"documents": 4,
	"tokens": 512
	},
	"de": {
	"loss": 3.21018385887146,
	"documents": 4,
	"tokens": 512
	}
	},
	"a3b_v2": {
	"ko": {
	"loss": 3.2937907576560974,
	"documents": 4,
	"tokens": 512
	},
	"en": {
	"loss": 2.9049559831619263,
	"documents": 4,
	"tokens": 512
	},
	"zh": {
	"loss": 3.7243993878364563,
	"documents": 4,
	"tokens": 512
	},
	"ja": {
	"loss": 3.160854697227478,
	"documents": 4,
	"tokens": 512
	},
	"es": {
	"loss": 2.9567737579345703,
	"documents": 4,
	"tokens": 512
	},
	"de": {
	"loss": 3.21018385887146,
	"documents": 4,
	"tokens": 512
	}
	},
	"qwen35_4b": {
	"ko": {
	"loss": 2.9032246470451355,
	"documents": 4,
	"tokens": 512
	},
	"en": {
	"loss": 2.770678699016571,
	"documents": 4,
	"tokens": 512
	},
	"zh": {
	"loss": 3.378099739551544,
	"documents": 4,
	"tokens": 512
	},
	"ja": {
	"loss": 2.9211148023605347,
	"documents": 4,
	"tokens": 512
	},
	"es": {
	"loss": 2.6984021067619324,
	"documents": 4,
	"tokens": 512
	},
	"de": {
	"loss": 2.9680771231651306,
	"documents": 4,
	"tokens": 512
	}
	}
	},
	"mean_losses": {
	"qwen35_2b": 3.2084930737813315,
	"a3b_v2": 3.2084930737813315,
	"qwen35_4b": 2.9399328529834747
	}
	}