Text Generation
Transformers
PyTorch
llama
cot
vicuna
uncensored
Merge
mix
gptq
text-generation-inference
Instructions to use digitous/13B-Chimera with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use digitous/13B-Chimera with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="digitous/13B-Chimera")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("digitous/13B-Chimera") model = AutoModelForCausalLM.from_pretrained("digitous/13B-Chimera", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use digitous/13B-Chimera with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "digitous/13B-Chimera" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "digitous/13B-Chimera", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/digitous/13B-Chimera
- SGLang
How to use digitous/13B-Chimera 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 "digitous/13B-Chimera" \ --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": "digitous/13B-Chimera", "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 "digitous/13B-Chimera" \ --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": "digitous/13B-Chimera", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use digitous/13B-Chimera with Docker Model Runner:
docker model run hf.co/digitous/13B-Chimera
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README.md
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- gptq
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13B-Chimera
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[] = applied as LoRA
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() = combined as composite model
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manticore-13b [Epoch3] by openaccess-ai-collective
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https://huggingface.co/PygmalionAI/metharme-13b
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Each model and LoRA was hand picked and considered for what it could contribute to this ensemble.
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Thanks to each and every one of you for your incredible work developing some of the best things
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- gptq
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### 13B-Chimera
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[] = applied as LoRA to a composite model | () = combined as composite models
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((MantiCore3E+VicunaCocktail)+[SuperCOT+[StorytellingV2+(SuperHOTProtoType-8192ctx+Metharme)]])
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This model is the result of an experimental use of LoRAs on language models and model merges that are not the base HuggingFace-format LLaMA model they were intended for.
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This experiment is to determine:
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Outcomes of applying Lower Order Rank Adapters in unconventional ways.
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Determine if applying LoRAs and stacking LoRAs onto merged models bypasses the zero-sum result of weight-sum model merging.
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The desired result is to additively apply desired features without paradoxically watering down a model's effective behavior.
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Language Models and LoRAs Used Credits:
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manticore-13b [Epoch3] by openaccess-ai-collective
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https://huggingface.co/PygmalionAI/metharme-13b
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Also thanks to Meta for LLaMA.
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Each model and LoRA was hand picked and considered for what it could contribute to this ensemble.
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Thanks to each and every one of you for your incredible work developing some of the best things
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to come out of this community.
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