Text Generation
Transformers
Safetensors
English
modern_llm
custom-architecture
rope
gqa
swiglu
rmsnorm
custom_code
Instructions to use devoppro/FastLLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use devoppro/FastLLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="devoppro/FastLLM", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("devoppro/FastLLM", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use devoppro/FastLLM with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "devoppro/FastLLM" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "devoppro/FastLLM", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/devoppro/FastLLM
- SGLang
How to use devoppro/FastLLM 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 "devoppro/FastLLM" \ --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": "devoppro/FastLLM", "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 "devoppro/FastLLM" \ --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": "devoppro/FastLLM", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use devoppro/FastLLM with Docker Model Runner:
docker model run hf.co/devoppro/FastLLM
Training in progress, step 500
Browse files- config.json +1 -1
- model.safetensors +1 -1
- training_args.bin +1 -1
config.json
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"pad_token_id": 0,
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"rms_norm_eps": 1e-06,
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"rope_theta": 1000000.0,
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"transformers_version": "5.15.
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"use_cache": false,
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"vocab_size": 151936
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}
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"rms_norm_eps": 1e-06,
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"transformers_version": "5.15.1",
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"vocab_size": 151936
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training_args.bin
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