Instructions to use trl-internal-testing/tiny-Qwen3MoeForCausalLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use trl-internal-testing/tiny-Qwen3MoeForCausalLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="trl-internal-testing/tiny-Qwen3MoeForCausalLM") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("trl-internal-testing/tiny-Qwen3MoeForCausalLM") model = AutoModelForCausalLM.from_pretrained("trl-internal-testing/tiny-Qwen3MoeForCausalLM") 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 trl-internal-testing/tiny-Qwen3MoeForCausalLM with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "trl-internal-testing/tiny-Qwen3MoeForCausalLM" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "trl-internal-testing/tiny-Qwen3MoeForCausalLM", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/trl-internal-testing/tiny-Qwen3MoeForCausalLM
- SGLang
How to use trl-internal-testing/tiny-Qwen3MoeForCausalLM 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 "trl-internal-testing/tiny-Qwen3MoeForCausalLM" \ --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": "trl-internal-testing/tiny-Qwen3MoeForCausalLM", "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 "trl-internal-testing/tiny-Qwen3MoeForCausalLM" \ --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": "trl-internal-testing/tiny-Qwen3MoeForCausalLM", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use trl-internal-testing/tiny-Qwen3MoeForCausalLM with Docker Model Runner:
docker model run hf.co/trl-internal-testing/tiny-Qwen3MoeForCausalLM
Commit ·
6db5716
1
Parent(s): 3d0483f
Upload Qwen3MoeForCausalLM (#1)
Browse files- Upload Qwen3MoeForCausalLM (2605d072ecde96362fb71461fb1b821e8e713c76)
- config.json +9 -5
- generation_config.json +1 -1
- model.safetensors +2 -2
config.json
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"attention_bias": false,
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"attention_dropout": 0.0,
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"decoder_sparse_step": 1,
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"dtype": "bfloat16",
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"hidden_act": "silu",
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"hidden_size": 8,
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"initializer_range": 0.02,
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"intermediate_size": 32,
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"max_position_embeddings":
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"mlp_only_layers": [],
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"model_type": "qwen3_moe",
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"moe_intermediate_size": 768,
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"norm_topk_prob":
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"num_attention_heads": 4,
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"num_experts": 4,
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"num_experts_per_tok": 2,
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"output_router_logits": false,
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"rms_norm_eps": 1e-06,
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"rope_scaling": null,
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"rope_theta":
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"router_aux_loss_coef": 0.001,
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"sliding_window": null,
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"tie_word_embeddings": false,
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"transformers_version": "4.
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"use_cache": true,
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"use_sliding_window": false,
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"vocab_size":
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}
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"bos_token_id": 151643,
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"decoder_sparse_step": 1,
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"dtype": "bfloat16",
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"eos_token_id": 151645,
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"head_dim": 128,
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"hidden_act": "silu",
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"hidden_size": 8,
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"initializer_range": 0.02,
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"intermediate_size": 32,
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"max_position_embeddings": 40960,
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"max_window_layers": 48,
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"mlp_only_layers": [],
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"model_type": "qwen3_moe",
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"moe_intermediate_size": 768,
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"norm_topk_prob": true,
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"num_attention_heads": 4,
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"num_experts": 4,
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"num_experts_per_tok": 2,
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"output_router_logits": false,
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"rms_norm_eps": 1e-06,
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"rope_scaling": null,
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"rope_theta": 1000000.0,
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"router_aux_loss_coef": 0.001,
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"sliding_window": null,
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"tie_word_embeddings": false,
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"transformers_version": "4.56.2",
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"use_cache": true,
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"use_sliding_window": false,
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"vocab_size": 151936
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}
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generation_config.json
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"temperature": 0.6,
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"top_k": 20,
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"top_p": 0.95,
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"transformers_version": "4.
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}
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"temperature": 0.6,
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"top_k": 20,
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"top_p": 0.95,
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"transformers_version": "4.56.2"
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}
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model.safetensors
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size 5212168
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