Instructions to use shah-shazid-askary/amd-finance-llm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use shah-shazid-askary/amd-finance-llm with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="shah-shazid-askary/amd-finance-llm")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("shah-shazid-askary/amd-finance-llm", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use shah-shazid-askary/amd-finance-llm with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "shah-shazid-askary/amd-finance-llm" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "shah-shazid-askary/amd-finance-llm", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/shah-shazid-askary/amd-finance-llm
- SGLang
How to use shah-shazid-askary/amd-finance-llm 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 "shah-shazid-askary/amd-finance-llm" \ --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": "shah-shazid-askary/amd-finance-llm", "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 "shah-shazid-askary/amd-finance-llm" \ --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": "shah-shazid-askary/amd-finance-llm", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use shah-shazid-askary/amd-finance-llm with Docker Model Runner:
docker model run hf.co/shah-shazid-askary/amd-finance-llm
Upload serve_vllm.py with huggingface_hub
Browse files- serve_vllm.py +31 -0
serve_vllm.py
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import os, sys, subprocess, argparse
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def main():
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parser = argparse.ArgumentParser()
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parser.add_argument('--model', required=True)
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parser.add_argument('--tensor_parallel_size', type=int, default=1)
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parser.add_argument('--port', type=int, default=8000)
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parser.add_argument('--max_model_len', type=int, default=8192)
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parser.add_argument('--max_num_seqs', type=int, default=256)
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parser.add_argument('--dtype', default='bfloat16')
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parser.add_argument('--num_scheduler_steps', type=int, default=10)
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parser.add_argument('--enable_chunked_prefill', action='store_true', default=False)
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args = parser.parse_args()
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os.environ.setdefault('VLLM_USE_TRITON_FLASH_ATTN', '0')
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os.environ.setdefault('NCCL_MIN_NCHANNELS', '112')
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os.environ.setdefault('PYTORCH_TUNABLEOP_ENABLED', '1')
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os.environ.setdefault('TORCHINDUCTOR_MAX_AUTOTUNE', '1')
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cmd = [sys.executable, '-m', 'vllm.entrypoints.openai.api_server',
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'--model', args.model, '--tensor-parallel-size', str(args.tensor_parallel_size),
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'--port', str(args.port), '--max-model-len', str(args.max_model_len),
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'--max-num-seqs', str(args.max_num_seqs), '--dtype', args.dtype,
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'--num-scheduler-steps', str(args.num_scheduler_steps)]
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if not args.enable_chunked_prefill:
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cmd.append('--enable-chunked-prefill=False')
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print('Launching vLLM:', ' '.join(cmd))
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subprocess.run(cmd)
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if __name__ == "__main__": main()
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