Instructions to use 64bits/LexPodLM-13B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use 64bits/LexPodLM-13B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="64bits/LexPodLM-13B")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("64bits/LexPodLM-13B") model = AutoModelForCausalLM.from_pretrained("64bits/LexPodLM-13B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use 64bits/LexPodLM-13B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "64bits/LexPodLM-13B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "64bits/LexPodLM-13B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/64bits/LexPodLM-13B
- SGLang
How to use 64bits/LexPodLM-13B 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 "64bits/LexPodLM-13B" \ --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": "64bits/LexPodLM-13B", "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 "64bits/LexPodLM-13B" \ --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": "64bits/LexPodLM-13B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use 64bits/LexPodLM-13B with Docker Model Runner:
docker model run hf.co/64bits/LexPodLM-13B
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README.md
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I trained the model on 4 A100 (80 GB RAM) for ~15 hrs.
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***
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*Human: do you think consciousness is foundamentally computational?*
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I trained the model on 4 A100 (80 GB RAM) for ~15 hrs.
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***
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Eval (evaluate models like [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) using [Eleuther AI Language Model Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness])):
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| Model | Average | ARC (25-shot) | HellaSwag (10-shot) | MMLU (5-shot) | TruthfulQA (0-shot) |
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| ------------ | ------- | ------------- | ------------------- | ------------- | ------------------- |
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| LexPodLM-13B | 52.5 | 48.8 | 79.4 | 37.9 | 43.8 |
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| LLaMA-13B | 51.8 | 50.8 | 78.9 | 37.7 | 39.9 |
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***
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*Human: do you think consciousness is foundamentally computational?*
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