Text Generation
Transformers
Safetensors
English
llama
roleplay
conversational
text-generation-inference
Instructions to use Naphula/Human-Like-LLama3-8B-Instruct-MPOA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Naphula/Human-Like-LLama3-8B-Instruct-MPOA with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Naphula/Human-Like-LLama3-8B-Instruct-MPOA") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Naphula/Human-Like-LLama3-8B-Instruct-MPOA") model = AutoModelForCausalLM.from_pretrained("Naphula/Human-Like-LLama3-8B-Instruct-MPOA", 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 Naphula/Human-Like-LLama3-8B-Instruct-MPOA with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Naphula/Human-Like-LLama3-8B-Instruct-MPOA" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Naphula/Human-Like-LLama3-8B-Instruct-MPOA", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Naphula/Human-Like-LLama3-8B-Instruct-MPOA
- SGLang
How to use Naphula/Human-Like-LLama3-8B-Instruct-MPOA 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 "Naphula/Human-Like-LLama3-8B-Instruct-MPOA" \ --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": "Naphula/Human-Like-LLama3-8B-Instruct-MPOA", "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 "Naphula/Human-Like-LLama3-8B-Instruct-MPOA" \ --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": "Naphula/Human-Like-LLama3-8B-Instruct-MPOA", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use Naphula/Human-Like-LLama3-8B-Instruct-MPOA with Docker Model Runner:
docker model run hf.co/Naphula/Human-Like-LLama3-8B-Instruct-MPOA
Create README.md
Browse files
README.md
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---
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license: apache-2.0
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datasets:
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- HumanLLMs/Human-Like-DPO-Dataset
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base_model:
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- HumanLLMs/Human-Like-LLama3-8B-Instruct
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library_name: transformers
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tags:
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- roleplay
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- conversational
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language:
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- en
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widget:
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- text: "Human Like Llama3 8B Instruct MPOA"
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output:
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url: https://cdn-avatars.huggingface.co/v1/production/uploads/63da3d7ae697e5898cb86854/H-vpXOX6KZu01HnV87Jk5.jpeg
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---
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> [!CAUTION]
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> <span style="color:red; font-weight:bold">⚠️ Warning:</span> This model can produce narratives and RP that contain violent and graphic erotic content. Adjust your system prompt accordingly, and use **Llama 3** chat template.
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>
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# 🖋️ Human Like Llama3 8B Instruct MPOA
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Fully uncensored
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