Instructions to use jtatman/sciphi-mini-600m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jtatman/sciphi-mini-600m with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="jtatman/sciphi-mini-600m")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("jtatman/sciphi-mini-600m") model = AutoModelForCausalLM.from_pretrained("jtatman/sciphi-mini-600m") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use jtatman/sciphi-mini-600m with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "jtatman/sciphi-mini-600m" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "jtatman/sciphi-mini-600m", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/jtatman/sciphi-mini-600m
- SGLang
How to use jtatman/sciphi-mini-600m 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 "jtatman/sciphi-mini-600m" \ --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": "jtatman/sciphi-mini-600m", "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 "jtatman/sciphi-mini-600m" \ --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": "jtatman/sciphi-mini-600m", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use jtatman/sciphi-mini-600m with Docker Model Runner:
docker model run hf.co/jtatman/sciphi-mini-600m
Model Card for Model ID
This is a model with altered parameters from a mergekit slice of SciPhi/SciPhi-Self-RAG-Mistral-7B-32k.
Model Details
Model Description
This model is an experimental model using minimal slices to gather core model properties that can be further trained.
The parameters have been reduced to just under 600 million. This is an experiment to see how far slicing can be taken while retaining original weight associations.
As such, he base model is a nonsense producer, and won't return much useful. However, a significant portion of the original sciphi model has been retained as far as gradients go.
This model is being trained without quantization, but the process is extensive, and is currently in training. This model will be released upon thorough analysis.
The model is also being trained with unsloth using qlora/peft and rank-stabilized lora (hoping for DoRA support in unsloth soon...) here:
jtatman/sciphi-mini-600m-unsloth
This process will be ongoing to see if rank stabilized tuning can save and enhance the original model information through recognizing original weight associations in the preserved layers, even after model resizing.
There is a twin project with a more siginificant size reduction (96 million params) that is being used for layer analysis here: jtatman/sciphi-micro
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