Instructions to use inference-optimization/Qwen3.8-1.0B-A0.6B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use inference-optimization/Qwen3.8-1.0B-A0.6B with Transformers:

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

pipe = pipeline("text-generation", model="inference-optimization/Qwen3.8-1.0B-A0.6B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("inference-optimization/Qwen3.8-1.0B-A0.6B")
model = AutoModelForCausalLM.from_pretrained("inference-optimization/Qwen3.8-1.0B-A0.6B", 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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use inference-optimization/Qwen3.8-1.0B-A0.6B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "inference-optimization/Qwen3.8-1.0B-A0.6B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "inference-optimization/Qwen3.8-1.0B-A0.6B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/inference-optimization/Qwen3.8-1.0B-A0.6B

SGLang

How to use inference-optimization/Qwen3.8-1.0B-A0.6B 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 "inference-optimization/Qwen3.8-1.0B-A0.6B" \
    --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": "inference-optimization/Qwen3.8-1.0B-A0.6B",
		"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 "inference-optimization/Qwen3.8-1.0B-A0.6B" \
        --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": "inference-optimization/Qwen3.8-1.0B-A0.6B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use inference-optimization/Qwen3.8-1.0B-A0.6B with Docker Model Runner:
```
docker model run hf.co/inference-optimization/Qwen3.8-1.0B-A0.6B
```

Qwen3.8-1.0B-A0.6B

This is a tiny version of Qwen/Qwen3.8-2.4T-A95B created for testing and development.

Model Details

Base Model: Qwen/Qwen3.8-2.4T-A95B
Architecture: qwen3_5_moe_text (Qwen3_5MoeForCausalLM)
Total Parameters: 0.97B
Activated Parameters: ~0.6B

Configuration Changes

The following parameters were reduced from the original model:

Parameter	Original	Tiny
num_hidden_layers	92	8
hidden_size	8192	1024
num_attention_heads	64	8
num_key_value_heads	4	2
head_dim	256	128
num_experts	512	16
num_experts_per_tok	10	2
moe_intermediate_size	2048	1024
shared_expert_intermediate_size	2048	1024
linear_key_head_dim	128	64
linear_num_key_heads	16	4
linear_num_value_heads	128	16
linear_value_head_dim	128	64
mtp_num_hidden_layers	1	0

Checkpoint Structure

Single safetensors file with packed expert format (experts.gate_up_proj, experts.down_proj) matching the original checkpoint structure. Layer types follow the [linear, linear, linear, full] x 2 pattern from the original.

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("Qwen3.8-1.0B-A0.6B", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained("Qwen3.8-1.0B-A0.6B")

input_ids = tokenizer("According to all known laws", return_tensors="pt").input_ids.to(model.device)
output = model.generate(input_ids, max_new_tokens=20)
print(tokenizer.decode(output[0]))

Creation Process

This model was created using the llm-compressor create-tiny-model claude skill.

Inspected the original 2.4T parameter MoE model configuration
Reduced all dimensions to create a ~1B parameter model while preserving the hybrid linear/full attention architecture
Fine-tuned on a toy dataset to achieve perplexity ~1.0
Converted checkpoint format to match the original (packed experts, correct tensor naming)
Validated model loading and generation

Notes

The model preserves the hybrid attention pattern: 6 linear attention layers and 2 full attention layers in a [linear, linear, linear, full] x 2 pattern
MTP (multi-token prediction) layers are removed (mtp_num_hidden_layers=0) as they are not needed for the tiny model
Expert weights use the packed format (gate_up_proj fused, stacked across experts) matching the original checkpoint

Downloads last month: -

Safetensors

Model size

1.0B params

Tensor type

BF16

Model tree for inference-optimization/Qwen3.8-1.0B-A0.6B

Base model

Qwen/Qwen3.8-2.4T-A95B

Finetuned

(2)

this model

Collection including inference-optimization/Qwen3.8-1.0B-A0.6B

Tiny Models

Collection

Tiny models used for testing • 18 items • Updated about 20 hours ago • 2