Instructions to use jpalmae/qwen35b-espanol-abliterated-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 jpalmae/qwen35b-espanol-abliterated-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 jpalmae/qwen35b-espanol-abliterated-gguf:F16_PATCHED
# Run inference directly in the terminal:
llama cli -hf jpalmae/qwen35b-espanol-abliterated-gguf:F16_PATCHED

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf jpalmae/qwen35b-espanol-abliterated-gguf:F16_PATCHED
# Run inference directly in the terminal:
llama cli -hf jpalmae/qwen35b-espanol-abliterated-gguf:F16_PATCHED

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 jpalmae/qwen35b-espanol-abliterated-gguf:F16_PATCHED
# Run inference directly in the terminal:
./llama-cli -hf jpalmae/qwen35b-espanol-abliterated-gguf:F16_PATCHED

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 jpalmae/qwen35b-espanol-abliterated-gguf:F16_PATCHED
# Run inference directly in the terminal:
./build/bin/llama-cli -hf jpalmae/qwen35b-espanol-abliterated-gguf:F16_PATCHED

Use Docker

docker model run hf.co/jpalmae/qwen35b-espanol-abliterated-gguf:F16_PATCHED

LM Studio
Jan

vLLM

How to use jpalmae/qwen35b-espanol-abliterated-gguf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jpalmae/qwen35b-espanol-abliterated-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": "jpalmae/qwen35b-espanol-abliterated-gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/jpalmae/qwen35b-espanol-abliterated-gguf:F16_PATCHED

Ollama
How to use jpalmae/qwen35b-espanol-abliterated-gguf with Ollama:
```
ollama run hf.co/jpalmae/qwen35b-espanol-abliterated-gguf:F16_PATCHED
```
Unsloth Desktop

How to use jpalmae/qwen35b-espanol-abliterated-gguf with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf jpalmae/qwen35b-espanol-abliterated-gguf:F16_PATCHED

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": "jpalmae/qwen35b-espanol-abliterated-gguf:F16_PATCHED"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use jpalmae/qwen35b-espanol-abliterated-gguf with Docker Model Runner:
```
docker model run hf.co/jpalmae/qwen35b-espanol-abliterated-gguf:F16_PATCHED
```

Lemonade

How to use jpalmae/qwen35b-espanol-abliterated-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull jpalmae/qwen35b-espanol-abliterated-gguf:F16_PATCHED

Run and chat with the model

lemonade run user.qwen35b-espanol-abliterated-gguf-F16_PATCHED

List all available models

lemonade list

Hermes Agent

How to use jpalmae/qwen35b-espanol-abliterated-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 jpalmae/qwen35b-espanol-abliterated-gguf:F16_PATCHED

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 jpalmae/qwen35b-espanol-abliterated-gguf:F16_PATCHED

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use jpalmae/qwen35b-espanol-abliterated-gguf with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf jpalmae/qwen35b-espanol-abliterated-gguf:F16_PATCHED

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 "jpalmae/qwen35b-espanol-abliterated-gguf:F16_PATCHED" \
  --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"

Qwen3.6-35B-A3B Abliterated — Español Neutro/Chileno 🇨🇱 (GGUF)

Fine-tune de Huihui-Qwen3.6-35B-A3B-abliterated (MoE 35B/3B activos) en GGUF — combina uncensored (abliterated) + español neutro/chileno (sin sesgo rioplatense).

Archivos

Archivo	Tamaño	Uso
`model_Q4_K_M.gguf`	20GB	Consumer: RTX 3090/4090 24GB (~169 tok/s)
`model_F16_patched.gguf`	65GB	Precisión completa / re-cuantizar / A100·H100·RTX PRO 6000

⚠️ Parche MTP aplicado

Ambos GGUF llevan el parche binario que corrige la metadata MTP del conversor (block_count 41→40, nextn_predict_layers 1→0). Sin parche llama.cpp falla con missing tensor 'blk.39.nextn.eh_proj.weight'; con parche carga perfecto.

Uso con llama.cpp

# Consumer 24GB
llama-server -m model_Q4_K_M.gguf --alias qwen35b-espanol-abl -c 4096 -ngl 99 --port 8080

# GPU grande (F16)
llama-server -m model_F16_patched.gguf --alias qwen35b-espanol-abl -c 8192 -ngl 99 --port 8080

Entrenamiento

LoRA r=64 sobre Huihui-Qwen3.6-35B-A3B-abliterated, 1200 pasos, dataset 200k (C4 .cl + neutro, filtro anti-voseo). Detalles en jpalmae/qwen35b-espanol-abliterated.

Aviso

"Abliterated/uncensored": el modelo no rehúsa por defecto. Úsalo responsablemente.

Licencia

MIT.

Downloads last month: 53

GGUF

Model size

35B params

Architecture

qwen35moe

Hardware compatibility

4-bit

16-bit