Text Generation
MLX
Safetensors
qwen3
dflash
speculative-decoding
block-diffusion
draft-model
efficiency
bielik
diffusion-language-model
Instructions to use gangel/Bielik-11B-v3.0-DFlash-MLX-8bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use gangel/Bielik-11B-v3.0-DFlash-MLX-8bit with MLX:
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # if on a CUDA device, also pip install mlx[cuda] # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("gangel/Bielik-11B-v3.0-DFlash-MLX-8bit") prompt = "Once upon a time in" text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- MLX LM
How to use gangel/Bielik-11B-v3.0-DFlash-MLX-8bit with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Generate some text mlx_lm.generate --model "gangel/Bielik-11B-v3.0-DFlash-MLX-8bit" --prompt "Once upon a time"
- Atomic Chat
Bielik-11B-v3.0-DFlash-MLX-8bit
MLX 8-bit (Q8_0, group_size=32) quantized DFlash draft model for speakleash/Bielik-11B-v3.0-DFlash.
Intended for speculative decoding together with speakleash/Bielik-11B-v3.0-Instruct-MLX-8bit.
Usage
from mlx_lm import load as mlx_lm_load
from dflash.model_mlx import load_draft, stream_generate
from mlx_lm.sample_utils import make_sampler
target, tokenizer = mlx_lm_load("speakleash/Bielik-11B-v3.0-Instruct-MLX-8bit")
draft = load_draft("gangel/Bielik-11B-v3.0-DFlash-MLX-8bit")
prompt = tokenizer.apply_chat_template(
[{"role": "user", "content": "Jaka jest stolica Polski?"}],
tokenize=False, add_generation_prompt=True,
)
for r in stream_generate(target, draft, tokenizer, prompt,
block_size=16, max_tokens=256, temperature=0.0):
print(r.text, end="", flush=True)
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Model size
0.4B params
Tensor type
BF16
·
U32 ·
Hardware compatibility
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Quantized
Model tree for gangel/Bielik-11B-v3.0-DFlash-MLX-8bit
Base model
speakleash/Bielik-11B-v3.0-DFlash