Instructions to use QuantFactory/qwen2.5-reinstruct-alternate-lumen-14B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantFactory/qwen2.5-reinstruct-alternate-lumen-14B-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("QuantFactory/qwen2.5-reinstruct-alternate-lumen-14B-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use QuantFactory/qwen2.5-reinstruct-alternate-lumen-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-reinstruct-alternate-lumen-14B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf QuantFactory/qwen2.5-reinstruct-alternate-lumen-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-reinstruct-alternate-lumen-14B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf QuantFactory/qwen2.5-reinstruct-alternate-lumen-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-reinstruct-alternate-lumen-14B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/qwen2.5-reinstruct-alternate-lumen-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-reinstruct-alternate-lumen-14B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/qwen2.5-reinstruct-alternate-lumen-14B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/qwen2.5-reinstruct-alternate-lumen-14B-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use QuantFactory/qwen2.5-reinstruct-alternate-lumen-14B-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/qwen2.5-reinstruct-alternate-lumen-14B-GGUF:Q4_K_M
```

Unsloth Studio

How to use QuantFactory/qwen2.5-reinstruct-alternate-lumen-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-reinstruct-alternate-lumen-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-reinstruct-alternate-lumen-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-reinstruct-alternate-lumen-14B-GGUF to start chatting

How to use QuantFactory/qwen2.5-reinstruct-alternate-lumen-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-reinstruct-alternate-lumen-14B-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/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-reinstruct-alternate-lumen-14B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use QuantFactory/qwen2.5-reinstruct-alternate-lumen-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-reinstruct-alternate-lumen-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-reinstruct-alternate-lumen-14B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use QuantFactory/qwen2.5-reinstruct-alternate-lumen-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-reinstruct-alternate-lumen-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-reinstruct-alternate-lumen-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"

Docker Model Runner
How to use QuantFactory/qwen2.5-reinstruct-alternate-lumen-14B-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/qwen2.5-reinstruct-alternate-lumen-14B-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/qwen2.5-reinstruct-alternate-lumen-14B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/qwen2.5-reinstruct-alternate-lumen-14B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.qwen2.5-reinstruct-alternate-lumen-14B-GGUF-Q4_K_M

List all available models

lemonade list

QuantFactory/qwen2.5-reinstruct-alternate-lumen-14B-GGUF

This is quantized version of Lambent/qwen2.5-reinstruct-alternate-lumen-14B created using llama.cpp

Original Model Card

qwenreinstruct

This is a merge of pre-trained language models created using mergekit.

Merge Details

Extracted an approximate LoRA of v000000/Qwen2.5-Lumen-14B, rank 128 difference between that and Instruct, and first applied this to Lambent/qwen2.5-14B-alternate-instruct-slerp which had no issues with EQ-Bench.

Then, here, re-applied a density and weight of original Instruct which in previous merges gave me no issues with EQ-Bench.

This one has EQ-Bench of 77.6713 and no "emotions don't match reference error" (if possibly still one not parsed). This is similar to Lumen and original Instruct and slightly exceeds both (within margin of error). My hope is that it has healed Instruct somewhat and regained its intelligence.

Merge Method

This model was merged using the della merge method using Lambent/qwen2.5-lumen-rebased-14B as a base.

Models Merged

The following models were included in the merge:

Qwen/Qwen2.5-14B-Instruct

Configuration

The following YAML configuration was used to produce this model:

models:
  - model: Qwen/Qwen2.5-14B-Instruct
    parameters:
      weight: 0.3
      density: 0.4
merge_method: della
base_model: Lambent/qwen2.5-lumen-rebased-14B
parameters:
  epsilon: 0.05
  lambda: 1
dtype: bfloat16
tokenizer_source: base

Downloads last month: 339

GGUF

Model size

15B 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/qwen2.5-reinstruct-alternate-lumen-14B-GGUF

Base model

Qwen/Qwen2.5-14B

Finetuned

Qwen/Qwen2.5-14B-Instruct

Quantized

(187)

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