Instructions to use sairahul1/eptwts-x-posts-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use sairahul1/eptwts-x-posts-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 sairahul1/eptwts-x-posts-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf sairahul1/eptwts-x-posts-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 sairahul1/eptwts-x-posts-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf sairahul1/eptwts-x-posts-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 sairahul1/eptwts-x-posts-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf sairahul1/eptwts-x-posts-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 sairahul1/eptwts-x-posts-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf sairahul1/eptwts-x-posts-gguf:Q4_K_M

Use Docker

docker model run hf.co/sairahul1/eptwts-x-posts-gguf:Q4_K_M

LM Studio
Jan

vLLM

How to use sairahul1/eptwts-x-posts-gguf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sairahul1/eptwts-x-posts-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": "sairahul1/eptwts-x-posts-gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/sairahul1/eptwts-x-posts-gguf:Q4_K_M

Ollama
How to use sairahul1/eptwts-x-posts-gguf with Ollama:
```
ollama run hf.co/sairahul1/eptwts-x-posts-gguf:Q4_K_M
```
Unsloth Desktop

How to use sairahul1/eptwts-x-posts-gguf with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf sairahul1/eptwts-x-posts-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": "sairahul1/eptwts-x-posts-gguf:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use sairahul1/eptwts-x-posts-gguf with Docker Model Runner:
```
docker model run hf.co/sairahul1/eptwts-x-posts-gguf:Q4_K_M
```

Lemonade

How to use sairahul1/eptwts-x-posts-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull sairahul1/eptwts-x-posts-gguf:Q4_K_M

Run and chat with the model

lemonade run user.eptwts-x-posts-gguf-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use sairahul1/eptwts-x-posts-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 sairahul1/eptwts-x-posts-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 sairahul1/eptwts-x-posts-gguf:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use sairahul1/eptwts-x-posts-gguf with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf sairahul1/eptwts-x-posts-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 "sairahul1/eptwts-x-posts-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"

eptwts X posts (GGUF Q4_K_M)

Fine-tuned Qwen2.5-3B-Instruct (QLoRA adapter qwen25-3b-ep-sft-v3 → 16-bit merge → Q4_K_M GGUF) for local Ollama.

Attribution: fine-tuned style inspired by EP (@eptwts) public signal / knowledge-base writing. Not an official EP model. Attribute the inspiration; do not impersonate deceptively on X.

Prompt UX (natural language)

please give 3 posts for niche dental AI automation

Replies are plain-text posts (numbered or blank-line separated). No JSON. No “Sure, here are…”.

Install with Ollama (primary)

Download the GGUF + Modelfile, then create a local Ollama model. This works on Mac/Linux when the native hf.co pull fails:

huggingface-cli download sairahul1/eptwts-x-posts-gguf \
  eptwts-x-posts-q4_k_m.gguf Modelfile \
  --local-dir ./eptwts-gguf
cd eptwts-gguf
# ensure Modelfile FROM matches local gguf filename
# (this repo's Modelfile uses: FROM ./eptwts-x-posts-q4_k_m.gguf)
ollama create eptwts-x-posts-gguf -f Modelfile
ollama run eptwts-x-posts-gguf

Requires huggingface_hub (pip install huggingface_hub) and Ollama.

Optional: native Hub pull

ollama run hf.co/sairahul1/eptwts-x-posts-gguf

May fail with blocked redirect to a different host when Ollama follows the HF AWS CDN / Xet host. Prefer the download → ollama create path above.

Files

File	Notes
`eptwts-x-posts-q4_k_m.gguf`	Q4_K_M (~1.93 GB)
`Modelfile`	Qwen chat template + locked SYSTEM prompt; `FROM ./eptwts-x-posts-q4_k_m.gguf`
`system_prompt.txt`	Same system string used at train/serve

Training

Base: Qwen/Qwen2.5-3B-Instruct
Adapter run: qwen25-3b-ep-sft-v3 (niche-faithful NL prompts)
Quant: llama.cpp Q4_K_M

Downloads last month: 83

GGUF

Model size

3B params

Architecture

qwen2

Hardware compatibility

4-bit

Model tree for sairahul1/eptwts-x-posts-gguf

Base model

Qwen/Qwen2.5-3B

Finetuned

Qwen/Qwen2.5-3B-Instruct

Quantized

(289)

this model