Instructions to use mfiscela/AldanaLLMQ4_2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mfiscela/AldanaLLMQ4_2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="mfiscela/AldanaLLMQ4_2")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("mfiscela/AldanaLLMQ4_2") model = AutoModelForCausalLM.from_pretrained("mfiscela/AldanaLLMQ4_2") - llama-cpp-python
How to use mfiscela/AldanaLLMQ4_2 with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="mfiscela/AldanaLLMQ4_2", filename="MistralRP-Noromaid-NSFW-7B-Q4_0.gguf", )
output = llm( "Once upon a time,", max_tokens=512, echo=True ) print(output)
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use mfiscela/AldanaLLMQ4_2 with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf mfiscela/AldanaLLMQ4_2:Q4_0 # Run inference directly in the terminal: llama cli -hf mfiscela/AldanaLLMQ4_2:Q4_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf mfiscela/AldanaLLMQ4_2:Q4_0 # Run inference directly in the terminal: llama cli -hf mfiscela/AldanaLLMQ4_2:Q4_0
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf mfiscela/AldanaLLMQ4_2:Q4_0 # Run inference directly in the terminal: ./llama-cli -hf mfiscela/AldanaLLMQ4_2:Q4_0
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf mfiscela/AldanaLLMQ4_2:Q4_0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf mfiscela/AldanaLLMQ4_2:Q4_0
Use Docker
docker model run hf.co/mfiscela/AldanaLLMQ4_2:Q4_0
- LM Studio
- Jan
- vLLM
How to use mfiscela/AldanaLLMQ4_2 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "mfiscela/AldanaLLMQ4_2" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "mfiscela/AldanaLLMQ4_2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/mfiscela/AldanaLLMQ4_2:Q4_0
- SGLang
How to use mfiscela/AldanaLLMQ4_2 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 "mfiscela/AldanaLLMQ4_2" \ --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": "mfiscela/AldanaLLMQ4_2", "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 "mfiscela/AldanaLLMQ4_2" \ --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": "mfiscela/AldanaLLMQ4_2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Ollama
How to use mfiscela/AldanaLLMQ4_2 with Ollama:
ollama run hf.co/mfiscela/AldanaLLMQ4_2:Q4_0
- Unsloth Studio
How to use mfiscela/AldanaLLMQ4_2 with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for mfiscela/AldanaLLMQ4_2 to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for mfiscela/AldanaLLMQ4_2 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for mfiscela/AldanaLLMQ4_2 to start chatting
- Atomic Chat new
- Docker Model Runner
How to use mfiscela/AldanaLLMQ4_2 with Docker Model Runner:
docker model run hf.co/mfiscela/AldanaLLMQ4_2:Q4_0
- Lemonade
How to use mfiscela/AldanaLLMQ4_2 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull mfiscela/AldanaLLMQ4_2:Q4_0
Run and chat with the model
lemonade run user.AldanaLLMQ4_2-Q4_0
List all available models
lemonade list
Model
This is a merge model of pre-trained language models created using mergekit.
Model Details
Prompt template: Alpaca
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Response:
Merge Details
Merge Method
This model was merged using the SLERP merge method.
Models Merged
The following models were included in the merge:
- Undi95/Mistral-RP-0.1-7B
- MaziyarPanahi/NSFW_DPO_Noromaid-7b-Mistral-7B-Instruct-v0.1
Configuration
The following YAML configuration was used to produce this model:
slices:
- sources:
- model: Undi95/Mistral-RP-0.1-7B
layer_range: [0, 32]
- model: MaziyarPanahi/NSFW_DPO_Noromaid-7b-Mistral-7B-Instruct-v0.1
layer_range: [0, 32]
merge_method: slerp
base_model: Undi95/Mistral-RP-0.1-7B
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5 # fallback for rest of tensors
dtype: bfloat16
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