Instructions to use QuantFactory/Replete-LLM-V2.5-Qwen-7b-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantFactory/Replete-LLM-V2.5-Qwen-7b-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("QuantFactory/Replete-LLM-V2.5-Qwen-7b-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use QuantFactory/Replete-LLM-V2.5-Qwen-7b-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 QuantFactory/Replete-LLM-V2.5-Qwen-7b-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf QuantFactory/Replete-LLM-V2.5-Qwen-7b-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 QuantFactory/Replete-LLM-V2.5-Qwen-7b-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf QuantFactory/Replete-LLM-V2.5-Qwen-7b-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 QuantFactory/Replete-LLM-V2.5-Qwen-7b-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/Replete-LLM-V2.5-Qwen-7b-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 QuantFactory/Replete-LLM-V2.5-Qwen-7b-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/Replete-LLM-V2.5-Qwen-7b-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/Replete-LLM-V2.5-Qwen-7b-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use QuantFactory/Replete-LLM-V2.5-Qwen-7b-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/Replete-LLM-V2.5-Qwen-7b-GGUF:Q4_K_M
```
Unsloth Desktop

How to use QuantFactory/Replete-LLM-V2.5-Qwen-7b-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf QuantFactory/Replete-LLM-V2.5-Qwen-7b-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": "QuantFactory/Replete-LLM-V2.5-Qwen-7b-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use QuantFactory/Replete-LLM-V2.5-Qwen-7b-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/Replete-LLM-V2.5-Qwen-7b-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/Replete-LLM-V2.5-Qwen-7b-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/Replete-LLM-V2.5-Qwen-7b-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Replete-LLM-V2.5-Qwen-7b-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use QuantFactory/Replete-LLM-V2.5-Qwen-7b-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 QuantFactory/Replete-LLM-V2.5-Qwen-7b-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 QuantFactory/Replete-LLM-V2.5-Qwen-7b-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use QuantFactory/Replete-LLM-V2.5-Qwen-7b-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf QuantFactory/Replete-LLM-V2.5-Qwen-7b-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 "QuantFactory/Replete-LLM-V2.5-Qwen-7b-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"

QuantFactory/Replete-LLM-V2.5-Qwen-7b-GGUF

This is quantized version of Replete-AI/Replete-LLM-V2.5-Qwen-7b created using llama.cpp

Original Model Card

Replete-LLM-V2.5-Qwen-7b

Replete-LLM-V2.5-Qwen-7b is a continues finetuned version of Qwen2.5-7B. I noticed recently that the Qwen team did not learn from my methods of continuous finetuning, the great benefits, and no downsides of it. So I took it upon myself to merge the instruct model with the base model myself using the Ties merge method

This version of the model shows higher performance than the original instruct and base models.

Quants:

GGUF: https://huggingface.co/bartowski/Replete-LLM-V2.5-Qwen-7b-GGUF

Benchmarks: (Coming soon)

Downloads last month: 150

GGUF

Model size

8B params

Architecture

qwen2

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

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Model tree for QuantFactory/Replete-LLM-V2.5-Qwen-7b-GGUF

Base model

Qwen/Qwen2.5-7B

Finetuned

Qwen/Qwen2.5-7B-Instruct

Quantized

(396)

this model