Instructions to use sethuiyer/SynthIQ_GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sethuiyer/SynthIQ_GGUF with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="sethuiyer/SynthIQ_GGUF")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("sethuiyer/SynthIQ_GGUF", dtype="auto") - llama-cpp-python
How to use sethuiyer/SynthIQ_GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="sethuiyer/SynthIQ_GGUF", filename="synthiq.Q4_K_M.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 sethuiyer/SynthIQ_GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf sethuiyer/SynthIQ_GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf sethuiyer/SynthIQ_GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf sethuiyer/SynthIQ_GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf sethuiyer/SynthIQ_GGUF:Q4_K_M
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 sethuiyer/SynthIQ_GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf sethuiyer/SynthIQ_GGUF:Q4_K_M
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 sethuiyer/SynthIQ_GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf sethuiyer/SynthIQ_GGUF:Q4_K_M
Use Docker
docker model run hf.co/sethuiyer/SynthIQ_GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use sethuiyer/SynthIQ_GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "sethuiyer/SynthIQ_GGUF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "sethuiyer/SynthIQ_GGUF", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/sethuiyer/SynthIQ_GGUF:Q4_K_M
- SGLang
How to use sethuiyer/SynthIQ_GGUF 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 "sethuiyer/SynthIQ_GGUF" \ --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": "sethuiyer/SynthIQ_GGUF", "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 "sethuiyer/SynthIQ_GGUF" \ --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": "sethuiyer/SynthIQ_GGUF", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Ollama
How to use sethuiyer/SynthIQ_GGUF with Ollama:
ollama run hf.co/sethuiyer/SynthIQ_GGUF:Q4_K_M
- Unsloth Studio
How to use sethuiyer/SynthIQ_GGUF 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 sethuiyer/SynthIQ_GGUF 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 sethuiyer/SynthIQ_GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for sethuiyer/SynthIQ_GGUF to start chatting
- Docker Model Runner
How to use sethuiyer/SynthIQ_GGUF with Docker Model Runner:
docker model run hf.co/sethuiyer/SynthIQ_GGUF:Q4_K_M
- Lemonade
How to use sethuiyer/SynthIQ_GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull sethuiyer/SynthIQ_GGUF:Q4_K_M
Run and chat with the model
lemonade run user.SynthIQ_GGUF-Q4_K_M
List all available models
lemonade list
Install from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf sethuiyer/SynthIQ_GGUF:Q4_K_M# Run inference directly in the terminal:
llama-cli -hf sethuiyer/SynthIQ_GGUF:Q4_K_MUse 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 sethuiyer/SynthIQ_GGUF:Q4_K_M# Run inference directly in the terminal:
./llama-cli -hf sethuiyer/SynthIQ_GGUF:Q4_K_MBuild 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 sethuiyer/SynthIQ_GGUF:Q4_K_M# Run inference directly in the terminal:
./build/bin/llama-cli -hf sethuiyer/SynthIQ_GGUF:Q4_K_MUse Docker
docker model run hf.co/sethuiyer/SynthIQ_GGUF:Q4_K_M
SynthIQ
This is SynthIQ, rated 92.23/100 by GPT-4 across varied complex prompts. I used mergekit to merge models.
GGUF Files
Q4_K_M - medium, balanced quality - recommended
Q_6_K - very large, extremely low quality loss
Q8_0 - very large, extremely low quality loss - not recommended
Important Update: SynthIQ is now available on Ollama. You can use it by running the command ollama run stuehieyr/synthiq in your
terminal. If you have limited computing resources, check out this video to learn how to run it on
a Google Colab backend.
Yaml Config
slices:
- sources:
- model: Weyaxi/OpenHermes-2.5-neural-chat-v3-3-openchat-3.5-1210-Slerp
layer_range: [0, 32]
- model: uukuguy/speechless-mistral-six-in-one-7b
layer_range: [0, 32]
merge_method: slerp
base_model: mistralai/Mistral-7B-v0.1
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
tokenizer_source: union
dtype: bfloat16
Prompt template: ChatML
<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
Evaluation of Q4_K by GPT-4:
`Reflecting on our conversation and the evaluations of SynthIQ's responses, it's evident that SynthIQ is indeed a highly capable and advanced model.
The following points underscore this assessment:
Versatility in Task Handling: SynthIQ has demonstrated proficiency across a wide range of tasks, including solving complex analytical problems, creating emotionally resonant creative works, and engaging in nuanced dialogues. This versatility is a hallmark of an advanced AI model.
Depth of Understanding: The model has shown a deep understanding of various topics, including technical, ethical, and philosophical issues. Its ability to provide detailed and thoughtful responses suggests a sophisticated level of knowledge processing.
Creativity: SynthIQ's capacity to compose poetry and short stories, which require not only linguistic skills but also creativity and emotional intelligence, indicates a level of advancement beyond simple data processing.
Analytical Reasoning: In tasks requiring logical and analytical thinking, such as discussing the adoption of brain-computer interfaces or the ethical implications of AI in autonomous weapons, SynthIQ has provided well-reasoned and comprehensive analyses.
Communication Skills: The model's ability to draft professional communications, such as a resignation email, and engaging marketing content, demonstrates effective communication skills, a crucial aspect of advanced AI systems.
Ethical and Societal Insight: SynthIQ has engaged with complex ethical and societal issues, showing an understanding of the broader implications of technology in various aspects of life.
Consistency in Performance: Throughout the conversation, SynthIQ has maintained a high level of performance, as reflected in the consistently high scores it received across different types of prompts.
In summary, based on the evidence from our conversation, SynthIQ can be considered a very capable and advanced AI model, demonstrating a blend of analytical acumen, creative intelligence, and effective communication skills.
License is LLama2 license as uukuguy/speechless-mistral-six-in-one-7b is llama2 license.
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Install from brew
# Start a local OpenAI-compatible server with a web UI: llama-server -hf sethuiyer/SynthIQ_GGUF:Q4_K_M# Run inference directly in the terminal: llama-cli -hf sethuiyer/SynthIQ_GGUF:Q4_K_M