Instructions to use N8Programs/lil-bard with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use N8Programs/lil-bard with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="N8Programs/lil-bard")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("N8Programs/lil-bard") model = AutoModelForCausalLM.from_pretrained("N8Programs/lil-bard", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use N8Programs/lil-bard with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "N8Programs/lil-bard" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "N8Programs/lil-bard", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/N8Programs/lil-bard
- SGLang
How to use N8Programs/lil-bard 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 "N8Programs/lil-bard" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "N8Programs/lil-bard", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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 "N8Programs/lil-bard" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "N8Programs/lil-bard", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use N8Programs/lil-bard with Docker Model Runner:
docker model run hf.co/N8Programs/lil-bard
Use cross-version split Qwen3-MoE expert layout
Browse files- README.md +4 -1
- model.safetensors +2 -2
README.md
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| Maximum exported context | 32,768 |
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| Published weight dtype | BF16 |
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The checkpoint uses the stock Transformers `Qwen3MoeForCausalLM` layout.
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## Tokenizer
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| Maximum exported context | 32,768 |
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The checkpoint uses the stock Transformers `Qwen3MoeForCausalLM` layout. MoE
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expert weights are stored as per-expert `gate_proj`, `up_proj`, and `down_proj`
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tensors for compatibility across Transformers releases; loading has been tested
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with Transformers 4.57.1 and 5.11.0.
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## Tokenizer
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model.safetensors
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