Instructions to use tsilva/qwen2.5-3b-trump-style-gguf-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use tsilva/qwen2.5-3b-trump-style-gguf-v2 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 tsilva/qwen2.5-3b-trump-style-gguf-v2:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf tsilva/qwen2.5-3b-trump-style-gguf-v2:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf tsilva/qwen2.5-3b-trump-style-gguf-v2:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf tsilva/qwen2.5-3b-trump-style-gguf-v2: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 tsilva/qwen2.5-3b-trump-style-gguf-v2:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf tsilva/qwen2.5-3b-trump-style-gguf-v2: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 tsilva/qwen2.5-3b-trump-style-gguf-v2:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf tsilva/qwen2.5-3b-trump-style-gguf-v2:Q4_K_M

Use Docker

docker model run hf.co/tsilva/qwen2.5-3b-trump-style-gguf-v2:Q4_K_M

LM Studio
Jan
Ollama
How to use tsilva/qwen2.5-3b-trump-style-gguf-v2 with Ollama:
```
ollama run hf.co/tsilva/qwen2.5-3b-trump-style-gguf-v2:Q4_K_M
```
Unsloth Desktop

How to use tsilva/qwen2.5-3b-trump-style-gguf-v2 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf tsilva/qwen2.5-3b-trump-style-gguf-v2: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": "tsilva/qwen2.5-3b-trump-style-gguf-v2:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use tsilva/qwen2.5-3b-trump-style-gguf-v2 with Docker Model Runner:
```
docker model run hf.co/tsilva/qwen2.5-3b-trump-style-gguf-v2:Q4_K_M
```

Lemonade

How to use tsilva/qwen2.5-3b-trump-style-gguf-v2 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull tsilva/qwen2.5-3b-trump-style-gguf-v2:Q4_K_M

Run and chat with the model

lemonade run user.qwen2.5-3b-trump-style-gguf-v2-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use tsilva/qwen2.5-3b-trump-style-gguf-v2 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf tsilva/qwen2.5-3b-trump-style-gguf-v2: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 tsilva/qwen2.5-3b-trump-style-gguf-v2:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use tsilva/qwen2.5-3b-trump-style-gguf-v2 with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf tsilva/qwen2.5-3b-trump-style-gguf-v2: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 "tsilva/qwen2.5-3b-trump-style-gguf-v2: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 3B Trump-Like Public Speaking Style v2 - GGUF export

Overview

This model artifact was generated by an llmstyler Runbook training job. It is part of a versioned style-tuning release. The adapter, merged model, GGUF export, and ONNX export use separate repositories so each artifact can be consumed with the tooling that expects that format.

Versioning and Naming

Field	Value
Artifact kind	GGUF export
Artifact version	v2
Repo	tsilva/qwen2.5-3b-trump-style-gguf-v2
Training run id	qwen25_3b_trump_v2
Run name	qwen2.5-3b-trump-style-qlora-v2
Generated by	llmstyler 0.1.0

Default standard: keep each published model artifact immutable and include the version suffix in the repo name. Publish a new version when the dataset, style prompt, base model, training recipe, or export settings change.

Training Inputs

Field	Value
Dataset	tsilva/stylemix_trump-v2
Dataset split	train
Restyled only	True
Base model	unsloth/Qwen2.5-3B-Instruct-bnb-4bit
4-bit load	True
Style id	trump_like_public_speaking

Style System Prompt

Training Recipe

Setting	Value
Max sequence length	2048
Epochs	5
Per-device batch size	2
Gradient accumulation	4
Learning rate	0.0003
Warmup ratio	0.05
LoRA rank	32
LoRA alpha	32
Eval fraction	0.2
Seed	3407
Report to	tensorboard, wandb

Published Artifacts

Artifact	Repo
QLoRA adapter	tsilva/qwen2.5-3b-trump-style-qlora-v2
Merged 16-bit model	tsilva/qwen2.5-3b-trump-style-merged-v2
GGUF	tsilva/qwen2.5-3b-trump-style-gguf-v2
ONNX	tsilva/qwen2.5-3b-trump-style-onnx-v2

GGUF quantization methods: q4_k_m

Metrics

Train

Metric	Value
epoch	2.4444444444444446
total_flos	4889840584826880.0
train_loss	1.3235322819514708
train_runtime	251.3496
train_samples_per_second	2.865
train_steps_per_second	0.358

Evaluation

Metric	Value
epoch	2.4444444444444446
eval_loss	1.3690619468688965
eval_runtime	4.1924
eval_samples_per_second	8.587
eval_steps_per_second	2.147

Intended Use

Use this artifact for style-following chat experiments and evaluation. The adapter is intended for PEFT loading with the base model. The merged model is intended for direct Transformer loading. GGUF is intended for llama.cpp compatible runtimes. ONNX is intended for ONNX Runtime compatible workflows.

Limitations

The model may over-apply the target style, miss factual nuance, or reproduce limitations from the source dataset and rewrite model. Evaluate task accuracy, safety behavior, refusal behavior, and style strength before deployment.

Downloads last month: 27

GGUF

Model size

3B params

Architecture

qwen2

Hardware compatibility

4-bit

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Model tree for tsilva/qwen2.5-3b-trump-style-gguf-v2

Base model

Qwen/Qwen2.5-3B

Finetuned

Qwen/Qwen2.5-3B-Instruct

Quantized

unsloth/Qwen2.5-3B-Instruct-bnb-4bit

Quantized

(18)

this model

tsilva
/

qwen2.5-3b-trump-style-gguf-v2

Qwen2.5 3B Trump-Like Public Speaking Style v2 - GGUF export

Overview

Versioning and Naming

Training Inputs

Style System Prompt

Training Recipe

Published Artifacts

Metrics

Train

Evaluation

Intended Use

Limitations

Model tree for tsilva/qwen2.5-3b-trump-style-gguf-v2

Dataset used to train tsilva/qwen2.5-3b-trump-style-gguf-v2