Instructions to use bknyaz/Qwen3-Coder-Next-REAM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use bknyaz/Qwen3-Coder-Next-REAM with Transformers:

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

pipe = pipeline("text-generation", model="bknyaz/Qwen3-Coder-Next-REAM")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("bknyaz/Qwen3-Coder-Next-REAM")
model = AutoModelForCausalLM.from_pretrained("bknyaz/Qwen3-Coder-Next-REAM")
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 bknyaz/Qwen3-Coder-Next-REAM with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/bknyaz/Qwen3-Coder-Next-REAM

SGLang

How to use bknyaz/Qwen3-Coder-Next-REAM 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 "bknyaz/Qwen3-Coder-Next-REAM" \
    --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": "bknyaz/Qwen3-Coder-Next-REAM",
		"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 "bknyaz/Qwen3-Coder-Next-REAM" \
        --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": "bknyaz/Qwen3-Coder-Next-REAM",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use bknyaz/Qwen3-Coder-Next-REAM with Docker Model Runner:
```
docker model run hf.co/bknyaz/Qwen3-Coder-Next-REAM
```

Qwen3-Coder-Next-REAM / config.json

bknyaz

Upload Qwen3NextForCausalLM

2cfa165 verified 4 months ago

raw

history blame contribute delete

2.69 kB

	{
	"architectures": [
	"Qwen3NextForCausalLM"
	],
	"attention_bias": false,
	"attention_dropout": 0,
	"bos_token_id": 151643,
	"decoder_sparse_step": 1,
	"dtype": "bfloat16",
	"eos_token_id": 151645,
	"full_attention_interval": 4,
	"head_dim": 256,
	"hidden_act": "silu",
	"hidden_size": 2048,
	"initializer_range": 0.02,
	"intermediate_size": 5120,
	"layer_types": [
	"linear_attention",
	"linear_attention",
	"linear_attention",
	"full_attention",
	"linear_attention",
	"linear_attention",
	"linear_attention",
	"full_attention",
	"linear_attention",
	"linear_attention",
	"linear_attention",
	"full_attention",
	"linear_attention",
	"linear_attention",
	"linear_attention",
	"full_attention",
	"linear_attention",
	"linear_attention",
	"linear_attention",
	"full_attention",
	"linear_attention",
	"linear_attention",
	"linear_attention",
	"full_attention",
	"linear_attention",
	"linear_attention",
	"linear_attention",
	"full_attention",
	"linear_attention",
	"linear_attention",
	"linear_attention",
	"full_attention",
	"linear_attention",
	"linear_attention",
	"linear_attention",
	"full_attention",
	"linear_attention",
	"linear_attention",
	"linear_attention",
	"full_attention",
	"linear_attention",
	"linear_attention",
	"linear_attention",
	"full_attention",
	"linear_attention",
	"linear_attention",
	"linear_attention",
	"full_attention"
	],
	"linear_conv_kernel_dim": 4,
	"linear_key_head_dim": 128,
	"linear_num_key_heads": 16,
	"linear_num_value_heads": 32,
	"linear_value_head_dim": 128,
	"max_position_embeddings": 262144,
	"merge_args": {
	"balance_group_size": 32,
	"dataset": "c4+math+the-stack-smol",
	"expert_saliency": "reap",
	"gate_softmax": true,
	"group": "freq_logits",
	"merge": "align_logits_weights",
	"merge_size": 384,
	"merger_bs": 3072,
	"merger_seq_len": 512,
	"pca_dim": 64,
	"precompute_input": false,
	"use_gate_output": true
	},
	"mlp_only_layers": [],
	"model_type": "qwen3_next",
	"moe_intermediate_size": 512,
	"norm_topk_prob": true,
	"num_attention_heads": 16,
	"num_experts": 384,
	"num_experts_per_tok": 10,
	"num_hidden_layers": 48,
	"num_key_value_heads": 2,
	"output_router_logits": false,
	"partial_rotary_factor": 0.25,
	"rms_norm_eps": 1e-06,
	"rope_scaling": null,
	"rope_theta": 5000000,
	"router_aux_loss_coef": 0.001,
	"shared_expert_intermediate_size": 512,
	"tie_word_embeddings": false,
	"transformers_version": "4.57.6",
	"use_cache": true,
	"use_sliding_window": false,
	"vocab_size": 151936
	}