Instructions to use QuantFactory/Qwen2.5-Gutenberg-Doppel-14B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantFactory/Qwen2.5-Gutenberg-Doppel-14B-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("QuantFactory/Qwen2.5-Gutenberg-Doppel-14B-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use QuantFactory/Qwen2.5-Gutenberg-Doppel-14B-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/Qwen2.5-Gutenberg-Doppel-14B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf QuantFactory/Qwen2.5-Gutenberg-Doppel-14B-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/Qwen2.5-Gutenberg-Doppel-14B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf QuantFactory/Qwen2.5-Gutenberg-Doppel-14B-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/Qwen2.5-Gutenberg-Doppel-14B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/Qwen2.5-Gutenberg-Doppel-14B-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/Qwen2.5-Gutenberg-Doppel-14B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/Qwen2.5-Gutenberg-Doppel-14B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/Qwen2.5-Gutenberg-Doppel-14B-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use QuantFactory/Qwen2.5-Gutenberg-Doppel-14B-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/Qwen2.5-Gutenberg-Doppel-14B-GGUF:Q4_K_M
```

Unsloth Studio

How to use QuantFactory/Qwen2.5-Gutenberg-Doppel-14B-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 QuantFactory/Qwen2.5-Gutenberg-Doppel-14B-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 QuantFactory/Qwen2.5-Gutenberg-Doppel-14B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for QuantFactory/Qwen2.5-Gutenberg-Doppel-14B-GGUF to start chatting

How to use QuantFactory/Qwen2.5-Gutenberg-Doppel-14B-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/Qwen2.5-Gutenberg-Doppel-14B-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/Qwen2.5-Gutenberg-Doppel-14B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use QuantFactory/Qwen2.5-Gutenberg-Doppel-14B-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/Qwen2.5-Gutenberg-Doppel-14B-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/Qwen2.5-Gutenberg-Doppel-14B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/Qwen2.5-Gutenberg-Doppel-14B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen2.5-Gutenberg-Doppel-14B-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use QuantFactory/Qwen2.5-Gutenberg-Doppel-14B-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/Qwen2.5-Gutenberg-Doppel-14B-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/Qwen2.5-Gutenberg-Doppel-14B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use QuantFactory/Qwen2.5-Gutenberg-Doppel-14B-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/Qwen2.5-Gutenberg-Doppel-14B-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/Qwen2.5-Gutenberg-Doppel-14B-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"

Qwen2.5-Gutenberg-Doppel-14B-GGUF / README.md

munish0838

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metadata

license: apache-2.0
library_name: transformers
base_model:
  - Qwen/Qwen2.5-14B-Instruct
model-index:
  - name: Qwen2.5-Gutenberg-Doppel-14B
    results:
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: IFEval (0-Shot)
          type: HuggingFaceH4/ifeval
          args:
            num_few_shot: 0
        metrics:
          - type: inst_level_strict_acc and prompt_level_strict_acc
            value: 80.91
            name: strict accuracy
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=nbeerbower/Qwen2.5-Gutenberg-Doppel-14B
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: BBH (3-Shot)
          type: BBH
          args:
            num_few_shot: 3
        metrics:
          - type: acc_norm
            value: 48.24
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=nbeerbower/Qwen2.5-Gutenberg-Doppel-14B
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: MATH Lvl 5 (4-Shot)
          type: hendrycks/competition_math
          args:
            num_few_shot: 4
        metrics:
          - type: exact_match
            value: 0
            name: exact match
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=nbeerbower/Qwen2.5-Gutenberg-Doppel-14B
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: GPQA (0-shot)
          type: Idavidrein/gpqa
          args:
            num_few_shot: 0
        metrics:
          - type: acc_norm
            value: 11.07
            name: acc_norm
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=nbeerbower/Qwen2.5-Gutenberg-Doppel-14B
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: MuSR (0-shot)
          type: TAUR-Lab/MuSR
          args:
            num_few_shot: 0
        metrics:
          - type: acc_norm
            value: 10.02
            name: acc_norm
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=nbeerbower/Qwen2.5-Gutenberg-Doppel-14B
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: MMLU-PRO (5-shot)
          type: TIGER-Lab/MMLU-Pro
          config: main
          split: test
          args:
            num_few_shot: 5
        metrics:
          - type: acc
            value: 43.57
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=nbeerbower/Qwen2.5-Gutenberg-Doppel-14B
          name: Open LLM Leaderboard

QuantFactory/Qwen2.5-Gutenberg-Doppel-14B-GGUF

This is quantized version of nbeerbower/Qwen2.5-Gutenberg-Doppel-14B created using llama.cpp

Original Model Card

Qwen2.5-Gutenberg-Doppel-14B

Qwen/Qwen2.5-14B-Instruct finetuned on jondurbin/gutenberg-dpo-v0.1 and nbeerbower/gutenberg2-dpo.

Method

ORPO tuned with 4x A40 for 3 epochs.

Thank you @ParasiticRogue for sponsoring.

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	32.30
IFEval (0-Shot)	80.91
BBH (3-Shot)	48.24
MATH Lvl 5 (4-Shot)	0.00
GPQA (0-shot)	11.07
MuSR (0-shot)	10.02
MMLU-PRO (5-shot)	43.57