Instructions to use Fischerboot/ichmagzug with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Fischerboot/ichmagzug with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Fischerboot/ichmagzug")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Fischerboot/ichmagzug") model = AutoModelForCausalLM.from_pretrained("Fischerboot/ichmagzug") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Fischerboot/ichmagzug with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Fischerboot/ichmagzug" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Fischerboot/ichmagzug", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Fischerboot/ichmagzug
- SGLang
How to use Fischerboot/ichmagzug 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 "Fischerboot/ichmagzug" \ --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": "Fischerboot/ichmagzug", "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 "Fischerboot/ichmagzug" \ --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": "Fischerboot/ichmagzug", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Fischerboot/ichmagzug with Docker Model Runner:
docker model run hf.co/Fischerboot/ichmagzug
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# KobbleTinyV2-1.1B
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This is a finetune of https://huggingface.co/TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T trained on a small 50mb subset of the Kobble Dataset.
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Training was done in under 2 hours on a single Nvidia RTX 2060 Mobile GPU with qLora (LR 1.5e-4, rank 8, alpha 16, batch size 2, gradient acc. 4, 2048 ctx).
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You can obtain the GGUF quantization of this model here: https://huggingface.co/concedo/KobbleTinyV2-1.1B-GGUF
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Update: KobbleTiny has been upgraded to V2! The old V1 is [still available at this link](https://huggingface.co/concedo/KobbleTiny/tree/eb0c96864bfecfd6ac9ece1a42c4654b4997eb72).
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<video width="320" controls autoplay src="https://cdn-uploads.huggingface.co/production/uploads/63cd4b6d1c8a5d1d7d76a778/zjHfohCnEu2Y9CWSWgf0n.mp4"></video>
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Try it live now: https://concedo-koboldcpp-kobbletiny.hf.space/
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## Dataset and Objectives
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The Kobble Dataset is a semi-private aggregated dataset made from multiple online sources and web scrapes.
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It contains content chosen and formatted specifically to work with KoboldAI software and Kobold Lite.
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#### Dataset Categories:
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- Instruct: Single turn instruct examples presented in the Alpaca format, with an emphasis on uncensored and unrestricted responses.
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- Chat: Two participant roleplay conversation logs in a multi-turn raw chat format that KoboldAI uses.
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- Story: Unstructured fiction excerpts, including literature containing various erotic and provocative content.
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## Prompt template: Alpaca
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### Instruction:
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{prompt}
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### Response:
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```
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**Note:** *No assurances will be provided about the **origins, safety, or copyright status** of this model, or of **any content** within the Kobble dataset.*
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*If you belong to a country or organization that has strict AI laws or restrictions against unlabelled or unrestricted content, you are advised not to use this model.*
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