Instructions to use k4yt3x/Cornerstone-0.1-LLaMA-70B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use k4yt3x/Cornerstone-0.1-LLaMA-70B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="k4yt3x/Cornerstone-0.1-LLaMA-70B") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("k4yt3x/Cornerstone-0.1-LLaMA-70B") model = AutoModelForCausalLM.from_pretrained("k4yt3x/Cornerstone-0.1-LLaMA-70B") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use k4yt3x/Cornerstone-0.1-LLaMA-70B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "k4yt3x/Cornerstone-0.1-LLaMA-70B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "k4yt3x/Cornerstone-0.1-LLaMA-70B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/k4yt3x/Cornerstone-0.1-LLaMA-70B
- SGLang
How to use k4yt3x/Cornerstone-0.1-LLaMA-70B 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 "k4yt3x/Cornerstone-0.1-LLaMA-70B" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "k4yt3x/Cornerstone-0.1-LLaMA-70B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'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 "k4yt3x/Cornerstone-0.1-LLaMA-70B" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "k4yt3x/Cornerstone-0.1-LLaMA-70B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use k4yt3x/Cornerstone-0.1-LLaMA-70B with Docker Model Runner:
docker model run hf.co/k4yt3x/Cornerstone-0.1-LLaMA-70B
Cornerstone-0.1-LLaMA-70B
This experimental merged model combines several abliterated models and Negative_LLAMA_70B to create a less biased and uncensored foundation, serving as a base for subsequent merges.
I forgot to convert the model to bf16. It will be corrected in the next version.
This is a merge of pre-trained language models created using mergekit.
Merge Details
Merge Method
This model was merged using the SCE merge method using huihui-ai/Llama-3.3-70B-Instruct-abliterated as a base.
Models Merged
The following models were included in the merge:
- NaniDAO/Llama-3.3-70B-Instruct-ablated
- nbeerbower/Llama-3.1-Nemotron-lorablated-70B
- SentientAGI/Dobby-Unhinged-Llama-3.3-70B
- SicariusSicariiStuff/Negative_LLAMA_70B
Configuration
The following YAML configuration was used to produce this model:
base_model: huihui-ai/Llama-3.3-70B-Instruct-abliterated
merge_method: sce
dtype: float32
models:
- model: SicariusSicariiStuff/Negative_LLAMA_70B
- model: NaniDAO/Llama-3.3-70B-Instruct-ablated
- model: SentientAGI/Dobby-Unhinged-Llama-3.3-70B
- model: nbeerbower/Llama-3.1-Nemotron-lorablated-70B
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