LetheanNetwork/lemer-mlx
Gemma 4 E2B in MLX format, 4-bit quantized, converted from
LetheanNetwork/lemer's
bf16 safetensors via mlx_lm.convert. This is the unmodified Google
Gemma 4 E2B-IT weights — no LEK shift, no fine-tuning — hosted in our
namespace so downstream tools (benchmarks, apps) don't have to depend
on external mlx-community mirrors.
For the LEK-merged (consent-based ethical kernel) variant of the same
model, see lthn/lemer.
Variants in this family
| Repo | Format | Bits | Use case |
|---|---|---|---|
LetheanNetwork/lemer |
safetensors + gguf Q4_K_M | bf16 / 4 | Source weights + llama.cpp/Ollama |
LetheanNetwork/lemer-mlx |
mlx | 4 | This repo — Apple Silicon default |
LetheanNetwork/lemer-mlx-8bit |
mlx | 8 | Apple Silicon higher-precision |
LetheanNetwork/lemer-mlx-bf16 |
mlx | bf16 | Apple Silicon full-precision reference |
Usage
from mlx_lm import load, generate
model, tokenizer = load("LetheanNetwork/lemer-mlx")
response = generate(
model, tokenizer,
prompt=tokenizer.apply_chat_template(
[{"role": "user", "content": "Hello"}],
add_generation_prompt=True,
enable_thinking=True,
),
max_tokens=512,
)
Provenance
- Source:
LetheanNetwork/lemerbf16 safetensors (=google/gemma-4-E2B-it) - Converter:
mlx_lm.convert(mlx-lm — LM Studio / Apple ML Research) - Quant: 4-bit group quantization, ~4.5 bits/weight effective
- License: Apache 2.0 (Gemma Terms of Use)
License
Apache 2.0, subject to the Gemma Terms of Use.
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Model size
0.7B params
Tensor type
BF16
·
U32 ·
Hardware compatibility
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4-bit
# Make sure mlx-vlm is installed # pip install --upgrade mlx-vlm from mlx_vlm import load, generate from mlx_vlm.prompt_utils import apply_chat_template from mlx_vlm.utils import load_config # Load the model model, processor = load("LetheanNetwork/lemer-mlx") config = load_config("LetheanNetwork/lemer-mlx") # Prepare input image = ["http://images.cocodataset.org/val2017/000000039769.jpg"] prompt = "Describe this image." # Apply chat template formatted_prompt = apply_chat_template( processor, config, prompt, num_images=1 ) # Generate output output = generate(model, processor, formatted_prompt, image) print(output)