Instructions to use tiiuae/falcon-mamba-7b-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tiiuae/falcon-mamba-7b-4bit with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="tiiuae/falcon-mamba-7b-4bit")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("tiiuae/falcon-mamba-7b-4bit") model = AutoModelForCausalLM.from_pretrained("tiiuae/falcon-mamba-7b-4bit") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use tiiuae/falcon-mamba-7b-4bit with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "tiiuae/falcon-mamba-7b-4bit" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "tiiuae/falcon-mamba-7b-4bit", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/tiiuae/falcon-mamba-7b-4bit
- SGLang
How to use tiiuae/falcon-mamba-7b-4bit 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 "tiiuae/falcon-mamba-7b-4bit" \ --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": "tiiuae/falcon-mamba-7b-4bit", "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 "tiiuae/falcon-mamba-7b-4bit" \ --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": "tiiuae/falcon-mamba-7b-4bit", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use tiiuae/falcon-mamba-7b-4bit with Docker Model Runner:
docker model run hf.co/tiiuae/falcon-mamba-7b-4bit
Update config.json
Browse files- config.json +2 -2
config.json
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"architectures": [
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"FalconMambaForCausalLM"
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],
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"bos_token_id":
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"conv_kernel": 4,
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"eos_token_id": 11,
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"expand": 16,
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"layer_norm_epsilon": 1e-05,
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"model_type": "falcon_mamba",
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"num_hidden_layers": 64,
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"pad_token_id":
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"quantization_config": {
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"_load_in_4bit": true,
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"_load_in_8bit": false,
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"architectures": [
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"FalconMambaForCausalLM"
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],
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+
"bos_token_id": 0,
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"conv_kernel": 4,
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"eos_token_id": 11,
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"expand": 16,
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"layer_norm_epsilon": 1e-05,
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"model_type": "falcon_mamba",
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"num_hidden_layers": 64,
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"pad_token_id": 11,
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"quantization_config": {
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"_load_in_4bit": true,
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"_load_in_8bit": false,
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