Instructions to use gsting/Qwen3-Coder-Next-FP8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gsting/Qwen3-Coder-Next-FP8 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="gsting/Qwen3-Coder-Next-FP8") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("gsting/Qwen3-Coder-Next-FP8") model = AutoModelForCausalLM.from_pretrained("gsting/Qwen3-Coder-Next-FP8") 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
- vLLM
How to use gsting/Qwen3-Coder-Next-FP8 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "gsting/Qwen3-Coder-Next-FP8" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "gsting/Qwen3-Coder-Next-FP8", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/gsting/Qwen3-Coder-Next-FP8
- SGLang
How to use gsting/Qwen3-Coder-Next-FP8 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 "gsting/Qwen3-Coder-Next-FP8" \ --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": "gsting/Qwen3-Coder-Next-FP8", "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 "gsting/Qwen3-Coder-Next-FP8" \ --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": "gsting/Qwen3-Coder-Next-FP8", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use gsting/Qwen3-Coder-Next-FP8 with Docker Model Runner:
docker model run hf.co/gsting/Qwen3-Coder-Next-FP8
| { | |
| "architectures": [ | |
| "Qwen3NextForCausalLM" | |
| ], | |
| "attention_bias": false, | |
| "attention_dropout": 0, | |
| "bos_token_id": 151643, | |
| "decoder_sparse_step": 1, | |
| "eos_token_id": 151645, | |
| "full_attention_interval": 4, | |
| "head_dim": 256, | |
| "hidden_act": "silu", | |
| "hidden_size": 2048, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 5120, | |
| "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, | |
| "mlp_only_layers": [], | |
| "model_type": "qwen3_next", | |
| "moe_intermediate_size": 512, | |
| "norm_topk_prob": true, | |
| "num_attention_heads": 16, | |
| "num_experts": 512, | |
| "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, | |
| "torch_dtype": "bfloat16", | |
| "transformers_version": "4.57.0.dev0", | |
| "use_cache": true, | |
| "use_sliding_window": false, | |
| "vocab_size": 151936, | |
| "quantization_config": { | |
| "quant_method": "fp8", | |
| "activation_scheme": "dynamic", | |
| "weight_per_tensor": false, | |
| "act_per_tensor": false, | |
| "weight_block_size": [ | |
| 128, | |
| 128 | |
| ], | |
| "modules_to_not_convert": [ | |
| "lm_head", | |
| "model.embed_tokens", | |
| "model.layers.0.linear_attn.conv1d", | |
| "model.layers.0.linear_attn.in_proj_ba", | |
| "model.layers.0.mlp.gate", | |
| "model.layers.0.mlp.shared_expert_gate", | |
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| "model.layers.1.linear_attn.in_proj_ba", | |
| "model.layers.1.mlp.gate", | |
| "model.layers.1.mlp.shared_expert_gate", | |
| "model.layers.10.linear_attn.conv1d", | |
| "model.layers.10.linear_attn.in_proj_ba", | |
| "model.layers.10.mlp.gate", | |
| "model.layers.10.mlp.shared_expert_gate", | |
| "model.layers.11.mlp.gate", | |
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| "model.layers.13.mlp.shared_expert_gate", | |
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| "model.layers.14.mlp.gate", | |
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| "model.layers.15.mlp.shared_expert_gate", | |
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| "model.layers.16.mlp.gate", | |
| "model.layers.16.mlp.shared_expert_gate", | |
| "model.layers.17.linear_attn.conv1d", | |
| "model.layers.17.linear_attn.in_proj_ba", | |
| "model.layers.17.mlp.gate", | |
| "model.layers.17.mlp.shared_expert_gate", | |
| "model.layers.18.linear_attn.conv1d", | |
| "model.layers.18.linear_attn.in_proj_ba", | |
| "model.layers.18.mlp.gate", | |
| "model.layers.18.mlp.shared_expert_gate", | |
| "model.layers.19.mlp.gate", | |
| "model.layers.19.mlp.shared_expert_gate", | |
| "model.layers.2.linear_attn.conv1d", | |
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| "model.layers.2.mlp.gate", | |
| "model.layers.2.mlp.shared_expert_gate", | |
| "model.layers.20.linear_attn.conv1d", | |
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| "model.layers.20.mlp.gate", | |
| "model.layers.20.mlp.shared_expert_gate", | |
| "model.layers.21.linear_attn.conv1d", | |
| "model.layers.21.linear_attn.in_proj_ba", | |
| "model.layers.21.mlp.gate", | |
| "model.layers.21.mlp.shared_expert_gate", | |
| "model.layers.22.linear_attn.conv1d", | |
| "model.layers.22.linear_attn.in_proj_ba", | |
| "model.layers.22.mlp.gate", | |
| "model.layers.22.mlp.shared_expert_gate", | |
| "model.layers.23.mlp.gate", | |
| "model.layers.23.mlp.shared_expert_gate", | |
| "model.layers.24.linear_attn.conv1d", | |
| "model.layers.24.linear_attn.in_proj_ba", | |
| "model.layers.24.mlp.gate", | |
| "model.layers.24.mlp.shared_expert_gate", | |
| "model.layers.25.linear_attn.conv1d", | |
| "model.layers.25.linear_attn.in_proj_ba", | |
| "model.layers.25.mlp.gate", | |
| "model.layers.25.mlp.shared_expert_gate", | |
| "model.layers.26.linear_attn.conv1d", | |
| "model.layers.26.linear_attn.in_proj_ba", | |
| "model.layers.26.mlp.gate", | |
| "model.layers.26.mlp.shared_expert_gate", | |
| "model.layers.27.mlp.gate", | |
| "model.layers.27.mlp.shared_expert_gate", | |
| "model.layers.28.linear_attn.conv1d", | |
| "model.layers.28.linear_attn.in_proj_ba", | |
| "model.layers.28.mlp.gate", | |
| "model.layers.28.mlp.shared_expert_gate", | |
| "model.layers.29.linear_attn.conv1d", | |
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| "model.layers.3.mlp.gate", | |
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| "model.layers.30.linear_attn.conv1d", | |
| "model.layers.30.linear_attn.in_proj_ba", | |
| "model.layers.30.mlp.gate", | |
| "model.layers.30.mlp.shared_expert_gate", | |
| "model.layers.31.mlp.gate", | |
| "model.layers.31.mlp.shared_expert_gate", | |
| "model.layers.32.linear_attn.conv1d", | |
| "model.layers.32.linear_attn.in_proj_ba", | |
| "model.layers.32.mlp.gate", | |
| "model.layers.32.mlp.shared_expert_gate", | |
| "model.layers.33.linear_attn.conv1d", | |
| "model.layers.33.linear_attn.in_proj_ba", | |
| "model.layers.33.mlp.gate", | |
| "model.layers.33.mlp.shared_expert_gate", | |
| "model.layers.34.linear_attn.conv1d", | |
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| "model.layers.34.mlp.gate", | |
| "model.layers.34.mlp.shared_expert_gate", | |
| "model.layers.35.mlp.gate", | |
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| "model.layers.37.linear_attn.conv1d", | |
| "model.layers.37.linear_attn.in_proj_ba", | |
| "model.layers.37.mlp.gate", | |
| "model.layers.37.mlp.shared_expert_gate", | |
| "model.layers.38.linear_attn.conv1d", | |
| "model.layers.38.linear_attn.in_proj_ba", | |
| "model.layers.38.mlp.gate", | |
| "model.layers.38.mlp.shared_expert_gate", | |
| "model.layers.39.mlp.gate", | |
| "model.layers.39.mlp.shared_expert_gate", | |
| "model.layers.4.linear_attn.conv1d", | |
| "model.layers.4.linear_attn.in_proj_ba", | |
| "model.layers.4.mlp.gate", | |
| "model.layers.4.mlp.shared_expert_gate", | |
| "model.layers.40.linear_attn.conv1d", | |
| "model.layers.40.linear_attn.in_proj_ba", | |
| "model.layers.40.mlp.gate", | |
| "model.layers.40.mlp.shared_expert_gate", | |
| "model.layers.41.linear_attn.conv1d", | |
| "model.layers.41.linear_attn.in_proj_ba", | |
| "model.layers.41.mlp.gate", | |
| "model.layers.41.mlp.shared_expert_gate", | |
| "model.layers.42.linear_attn.conv1d", | |
| "model.layers.42.linear_attn.in_proj_ba", | |
| "model.layers.42.mlp.gate", | |
| "model.layers.42.mlp.shared_expert_gate", | |
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| "model.layers.44.linear_attn.conv1d", | |
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| "model.layers.44.mlp.gate", | |
| "model.layers.44.mlp.shared_expert_gate", | |
| "model.layers.45.linear_attn.conv1d", | |
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| "model.layers.45.mlp.gate", | |
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| "model.layers.46.linear_attn.conv1d", | |
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| "model.layers.46.mlp.shared_expert_gate", | |
| "model.layers.47.mlp.gate", | |
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| "model.layers.5.linear_attn.conv1d", | |
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| "model.layers.5.mlp.gate", | |
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| "model.layers.6.linear_attn.conv1d", | |
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| "model.layers.8.mlp.gate", | |
| "model.layers.8.mlp.shared_expert_gate", | |
| "model.layers.9.linear_attn.conv1d", | |
| "model.layers.9.linear_attn.in_proj_ba", | |
| "model.layers.9.mlp.gate", | |
| "model.layers.9.mlp.shared_expert_gate" | |
| ] | |
| } | |
| } |