Transformers
GGUF
fluently-lm
fluently
prinum
instruct
trained
math
roleplay
reasoning
axolotl
unsloth
argilla
qwen2
conversational
Instructions to use mradermacher/FluentlyLM-Prinum-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mradermacher/FluentlyLM-Prinum-GGUF with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("mradermacher/FluentlyLM-Prinum-GGUF", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use mradermacher/FluentlyLM-Prinum-GGUF 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 mradermacher/FluentlyLM-Prinum-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf mradermacher/FluentlyLM-Prinum-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf mradermacher/FluentlyLM-Prinum-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf mradermacher/FluentlyLM-Prinum-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 mradermacher/FluentlyLM-Prinum-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf mradermacher/FluentlyLM-Prinum-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 mradermacher/FluentlyLM-Prinum-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf mradermacher/FluentlyLM-Prinum-GGUF:Q4_K_M
Use Docker
docker model run hf.co/mradermacher/FluentlyLM-Prinum-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use mradermacher/FluentlyLM-Prinum-GGUF with Ollama:
ollama run hf.co/mradermacher/FluentlyLM-Prinum-GGUF:Q4_K_M
- Unsloth Desktop
- Pi
How to use mradermacher/FluentlyLM-Prinum-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf mradermacher/FluentlyLM-Prinum-GGUF:Q4_K_M
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "mradermacher/FluentlyLM-Prinum-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use mradermacher/FluentlyLM-Prinum-GGUF with Docker Model Runner:
docker model run hf.co/mradermacher/FluentlyLM-Prinum-GGUF:Q4_K_M
- Lemonade
How to use mradermacher/FluentlyLM-Prinum-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull mradermacher/FluentlyLM-Prinum-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.FluentlyLM-Prinum-GGUF-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use mradermacher/FluentlyLM-Prinum-GGUF with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf mradermacher/FluentlyLM-Prinum-GGUF:Q4_K_M
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default mradermacher/FluentlyLM-Prinum-GGUF:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use mradermacher/FluentlyLM-Prinum-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf mradermacher/FluentlyLM-Prinum-GGUF:Q4_K_M
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "mradermacher/FluentlyLM-Prinum-GGUF:Q4_K_M" \ --custom-provider-id llama-cpp \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
auto-patch README.md
Browse files
README.md
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---
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base_model: fluently
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datasets:
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- code
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library_name: transformers
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license: mit
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quantized_by: mradermacher
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tags:
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- fluently-lm
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<!-- ### convert_type: hf -->
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static quants of https://huggingface.co/fluently
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<!-- provided-files -->
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weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
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## Usage
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---
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base_model: fluently/FluentlyQwen2.5-32B
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datasets:
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- code
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library_name: transformers
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license: mit
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mradermacher:
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readme_rev: 1
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quantized_by: mradermacher
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tags:
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- fluently-lm
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<!-- ### convert_type: hf -->
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<!-- ### vocab_type: -->
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<!-- ### tags: -->
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static quants of https://huggingface.co/fluently/FluentlyQwen2.5-32B
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<!-- provided-files -->
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***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#FluentlyLM-Prinum-GGUF).***
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weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
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## Usage
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