Instructions to use arianraje/qwen3-4b-mamba3-hybrid-stage2b-kd-bias0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use arianraje/qwen3-4b-mamba3-hybrid-stage2b-kd-bias0 with Transformers:

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

pipe = pipeline("text-generation", model="arianraje/qwen3-4b-mamba3-hybrid-stage2b-kd-bias0", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("arianraje/qwen3-4b-mamba3-hybrid-stage2b-kd-bias0", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use arianraje/qwen3-4b-mamba3-hybrid-stage2b-kd-bias0 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "arianraje/qwen3-4b-mamba3-hybrid-stage2b-kd-bias0"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "arianraje/qwen3-4b-mamba3-hybrid-stage2b-kd-bias0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/arianraje/qwen3-4b-mamba3-hybrid-stage2b-kd-bias0

SGLang

How to use arianraje/qwen3-4b-mamba3-hybrid-stage2b-kd-bias0 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 "arianraje/qwen3-4b-mamba3-hybrid-stage2b-kd-bias0" \
    --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": "arianraje/qwen3-4b-mamba3-hybrid-stage2b-kd-bias0",
		"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 "arianraje/qwen3-4b-mamba3-hybrid-stage2b-kd-bias0" \
        --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": "arianraje/qwen3-4b-mamba3-hybrid-stage2b-kd-bias0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use arianraje/qwen3-4b-mamba3-hybrid-stage2b-kd-bias0 with Docker Model Runner:
```
docker model run hf.co/arianraje/qwen3-4b-mamba3-hybrid-stage2b-kd-bias0
```

qwen3-4b-mamba3-hybrid-stage2b-kd-bias0 / stage2a_kd_summary.json

arianraje

upload qwen3-4b-mamba3-u4-stage2b-v0

76aaf68 verified 13 days ago

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	{
	"config": {
	"teacher": "/mnt/home/DDN_Copy/cornell/afei1/hf_cache/hub/models--Qwen--Qwen3-4B/snapshots/1cfa9a7208912126459214e8b04321603b3df60c",
	"student": "/mnt/home/DDN_Copy/cornell/afei1/checkpoints/qwen3-4b-mamba3-u4-stage2a-v0",
	"data_dir": "/mnt/home/DDN_Copy/cornell/afei1/data/stage2b_mix_v1",
	"output_dir": "/mnt/home/DDN_Copy/cornell/afei1/checkpoints/qwen3-4b-mamba3-u4-stage2b-v0",
	"train_tokens": 294000000,
	"global_batch_tokens": 2097152,
	"micro_batch": 1,
	"seq_len": 32768,
	"logit_chunk": 2048,
	"lr_gdn": 0.0001,
	"lr_inherited": 1e-05,
	"min_lr_frac": 0.1,
	"warmup_frac": 0.03,
	"weight_decay": 0.0,
	"betas": [
	0.9,
	0.95
	],
	"grad_clip": 1.0,
	"grad_checkpoint": true,
	"run_name": "mamba3-stage2b-kd-v0",
	"wandb_project": "qwen3-mamba3-linearization",
	"wandb_group": "stage2b-kd",
	"log_every": 1,
	"eval_every": 10,
	"eval_batches": 16,
	"ppl_windows": 8,
	"ppl_ctx": 4096,
	"save_every": 15,
	"save_model": true,
	"resume": true,
	"attn_implementation": "",
	"offload_optimizer": false
	},
	"final_val": {
	"kl": 0.20212099963100627,
	"top1_agree": 0.8055686950683594
	},
	"wandb_id": "jdm9eury",
	"finished": "2026-09-10 18:30:44"
	}