DeepHat V1 7B Heretic Abliterated Mlx 6Bit
The Model usermma/DeepHat-V1-7B-Heretic-Abliterated-mlx-6Bit was converted to MLX format from usermma/DeepHat-V1-7B-Heretic-Abliterated using mlx-lm version 0.31.2.
Fuente del modelo
- Tipo
- Text model
- Tamaño del archivo
- Aún no registrado
- Modelo base
- usermma/DeepHat-V1-7B-Heretic-Abliterated
- Versión
- vRevision d14b01d
- Actualizado
- 10 oct 2026
- Descargas
- 26
- Fuente
- Hugging Face
Descripción de la fuente
usermma/DeepHat-V1-7B-Heretic-Abliterated-mlx-6Bit
The Model usermma/DeepHat-V1-7B-Heretic-Abliterated-mlx-6Bit was converted to MLX format from usermma/DeepHat-V1-7B-Heretic-Abliterated using mlx-lm version 0.31.2.
Use with mlx
pip install mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("usermma/DeepHat-V1-7B-Heretic-Abliterated-mlx-6Bit")
prompt="hello"
if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
Fuentes
1 fuente
Fuentes
1 fuenteVerificado 12 sept
Extractos de fuentes
3 extractos
Extractos de fuentes
3 extractos--- language: en tags: - obliteratus - abliteration - uncensored - obliterate - mlx - mlx-my-repo base_model: usermma/DeepHat-V1-7B-Heretic-Abliterated --- # usermma/DeepHat-V1-7B-Heretic-Abliterated-mlx-6Bit The Model [usermma/DeepHat-V1-7B-Heretic-Abliterated-mlx-6Bit](https://huggingface.co/usermma/DeepHat-V1-7B-Heretic-Abliterated-mlx-6Bit) was converted to MLX format from [usermma/DeepHat-V1-7B-Heretic-Abliterated](https://huggingface.co/usermma/DeepHat-V1-7B-Heretic-Abliterated) using mlx-lm version **0.31.2**. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("usermma/DeepHat-V1-7B-Heretic-Abliterated-mlx-6Bit") prompt="hello" if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None: messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) response = generate(model, tokenizer, prompt=prompt, verbose=True) ```
Source context: 26 downloads · 0 likes · Library mlx · Repo usermma/DeepHat-V1-7B-Heretic-Abliterated-mlx-6Bit
Source context: 30 downloads · 0 likes · Library mlx · Repo usermma/DeepHat-V1-7B-Heretic-Abliterated-mlx-6Bit
