Instructions to use ehristoforu/FluentlyLM-Prinum-Q2_K-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ehristoforu/FluentlyLM-Prinum-Q2_K-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="ehristoforu/FluentlyLM-Prinum-Q2_K-GGUF")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("ehristoforu/FluentlyLM-Prinum-Q2_K-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use ehristoforu/FluentlyLM-Prinum-Q2_K-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 ehristoforu/FluentlyLM-Prinum-Q2_K-GGUF:Q2_K
# Run inference directly in the terminal:
llama cli -hf ehristoforu/FluentlyLM-Prinum-Q2_K-GGUF:Q2_K

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf ehristoforu/FluentlyLM-Prinum-Q2_K-GGUF:Q2_K
# Run inference directly in the terminal:
llama cli -hf ehristoforu/FluentlyLM-Prinum-Q2_K-GGUF:Q2_K

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 ehristoforu/FluentlyLM-Prinum-Q2_K-GGUF:Q2_K
# Run inference directly in the terminal:
./llama-cli -hf ehristoforu/FluentlyLM-Prinum-Q2_K-GGUF:Q2_K

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 ehristoforu/FluentlyLM-Prinum-Q2_K-GGUF:Q2_K
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ehristoforu/FluentlyLM-Prinum-Q2_K-GGUF:Q2_K

Use Docker

docker model run hf.co/ehristoforu/FluentlyLM-Prinum-Q2_K-GGUF:Q2_K

LM Studio
Jan

vLLM

How to use ehristoforu/FluentlyLM-Prinum-Q2_K-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ehristoforu/FluentlyLM-Prinum-Q2_K-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ehristoforu/FluentlyLM-Prinum-Q2_K-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ehristoforu/FluentlyLM-Prinum-Q2_K-GGUF:Q2_K

SGLang

How to use ehristoforu/FluentlyLM-Prinum-Q2_K-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 "ehristoforu/FluentlyLM-Prinum-Q2_K-GGUF" \
    --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": "ehristoforu/FluentlyLM-Prinum-Q2_K-GGUF",
		"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 "ehristoforu/FluentlyLM-Prinum-Q2_K-GGUF" \
        --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": "ehristoforu/FluentlyLM-Prinum-Q2_K-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use ehristoforu/FluentlyLM-Prinum-Q2_K-GGUF with Ollama:
```
ollama run hf.co/ehristoforu/FluentlyLM-Prinum-Q2_K-GGUF:Q2_K
```
Unsloth Desktop

How to use ehristoforu/FluentlyLM-Prinum-Q2_K-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ehristoforu/FluentlyLM-Prinum-Q2_K-GGUF:Q2_K

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": "ehristoforu/FluentlyLM-Prinum-Q2_K-GGUF:Q2_K"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use ehristoforu/FluentlyLM-Prinum-Q2_K-GGUF with Docker Model Runner:
```
docker model run hf.co/ehristoforu/FluentlyLM-Prinum-Q2_K-GGUF:Q2_K
```

Lemonade

How to use ehristoforu/FluentlyLM-Prinum-Q2_K-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ehristoforu/FluentlyLM-Prinum-Q2_K-GGUF:Q2_K

Run and chat with the model

lemonade run user.FluentlyLM-Prinum-Q2_K-GGUF-Q2_K

List all available models

lemonade list

Hermes Agent

How to use ehristoforu/FluentlyLM-Prinum-Q2_K-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 ehristoforu/FluentlyLM-Prinum-Q2_K-GGUF:Q2_K

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 ehristoforu/FluentlyLM-Prinum-Q2_K-GGUF:Q2_K

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use ehristoforu/FluentlyLM-Prinum-Q2_K-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ehristoforu/FluentlyLM-Prinum-Q2_K-GGUF:Q2_K

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 "ehristoforu/FluentlyLM-Prinum-Q2_K-GGUF:Q2_K" \
  --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"

FluentlyLM-Prinum-Q2_K-GGUF

File size: 2,138 Bytes

d595941

---
inference: false
library_name: transformers
tags:
- fluently-lm
- fluently
- prinum
- instruct
- trained
- math
- roleplay
- reasoning
- axolotl
- unsloth
- argilla
- qwen2
- llama-cpp
- gguf-my-repo
license: mit
language:
- en
- fr
- es
- ru
- zh
- ja
- fa
- code
datasets:
- fluently-sets/ultraset
- fluently-sets/ultrathink
- fluently-sets/reasoning-1-1k
- fluently-sets/MATH-500-Overall
pipeline_tag: text-generation
base_model: fluently-lm/FluentlyLM-Prinum
---

# ehristoforu/FluentlyLM-Prinum-Q2_K-GGUF
This model was converted to GGUF format from [`fluently-lm/FluentlyLM-Prinum`](https://huggingface.co/fluently-lm/FluentlyLM-Prinum) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/fluently-lm/FluentlyLM-Prinum) for more details on the model.

## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)

```bash
brew install llama.cpp

```
Invoke the llama.cpp server or the CLI.

### CLI:
```bash
llama-cli --hf-repo ehristoforu/FluentlyLM-Prinum-Q2_K-GGUF --hf-file fluentlylm-prinum-q2_k.gguf -p "The meaning to life and the universe is"
```

### Server:
```bash
llama-server --hf-repo ehristoforu/FluentlyLM-Prinum-Q2_K-GGUF --hf-file fluentlylm-prinum-q2_k.gguf -c 2048
```

Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.

Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```

Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```

Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo ehristoforu/FluentlyLM-Prinum-Q2_K-GGUF --hf-file fluentlylm-prinum-q2_k.gguf -p "The meaning to life and the universe is"
```
or 
```
./llama-server --hf-repo ehristoforu/FluentlyLM-Prinum-Q2_K-GGUF --hf-file fluentlylm-prinum-q2_k.gguf -c 2048
```