Text Generation
Transformers
Safetensors
English
mixtral
Merge
conversational
text-generation-inference
Instructions to use xxx777xxxASD/NeuralKunoichi-EroSumika-4x7B-128k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use xxx777xxxASD/NeuralKunoichi-EroSumika-4x7B-128k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="xxx777xxxASD/NeuralKunoichi-EroSumika-4x7B-128k") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("xxx777xxxASD/NeuralKunoichi-EroSumika-4x7B-128k") model = AutoModelForCausalLM.from_pretrained("xxx777xxxASD/NeuralKunoichi-EroSumika-4x7B-128k", device_map="auto") 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 xxx777xxxASD/NeuralKunoichi-EroSumika-4x7B-128k with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "xxx777xxxASD/NeuralKunoichi-EroSumika-4x7B-128k" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "xxx777xxxASD/NeuralKunoichi-EroSumika-4x7B-128k", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/xxx777xxxASD/NeuralKunoichi-EroSumika-4x7B-128k
- SGLang
How to use xxx777xxxASD/NeuralKunoichi-EroSumika-4x7B-128k 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 "xxx777xxxASD/NeuralKunoichi-EroSumika-4x7B-128k" \ --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": "xxx777xxxASD/NeuralKunoichi-EroSumika-4x7B-128k", "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 "xxx777xxxASD/NeuralKunoichi-EroSumika-4x7B-128k" \ --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": "xxx777xxxASD/NeuralKunoichi-EroSumika-4x7B-128k", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use xxx777xxxASD/NeuralKunoichi-EroSumika-4x7B-128k with Docker Model Runner:
docker model run hf.co/xxx777xxxASD/NeuralKunoichi-EroSumika-4x7B-128k
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license: apache-2.0
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license: apache-2.0
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language:
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- en
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tags:
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- merge
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Test merge. Attempt to get good at RP, ERP, general things model with 128k context. Every model here has `Epiculous/Fett-uccine-Long-Noodle-7B-120k-Context` in merge instead of regular MistralYarn 128k. The reason is because i belive Epiculous merged it with Mistral Instruct v0.2 to make first 32k context as good as possible, if not than it's sad.
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Here is the "family tree" of this model, im not writing full model names cause they long af
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### NeuralKunoichi-EroSumika 4x7B
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```
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* NeuralKunoichi-EroSumika 4x7B
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*(1) Kunocchini-7b-128k
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*(2) Mistral-Instruct-v0.2-128k
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* Mistral-7B-Instruct-v0.2
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* Fett-128k
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*(3) Erosumika-128k
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* FErosumika 7B
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* FFett-128k
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*(4) Mistral-NeuralHuman-128k
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* Fett-128k
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* Mistral-NeuralHuman
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* Mistral_MoreHuman
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* Mistral-Neural-Story
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```
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