Downloads · 30 days
15
20% of all-time downloads
LetheanNetwork/lemer-mlx-bf16
lemer-mlx-bf16 is a image-text-to-text model from LetheanNetwork. Use it for the image-text-to-text task on the model card, and read the license before you ship it in a product. It is set up for mlx. The card lists the license as eupl-1.2.
Gemma 4 E2B in MLX format, full bf16 precision, converted from LetheanNetwork/lemer's bf16 safetensors via mlxlm.convert --dtype bfloat16. No quantization — this is the full-precision reference for the MLX family. For…
Downloads · 30 days
15
20% of all-time downloads
All-time downloads
74
Public
Parameters
4.6B
9.3 GB on disk
Likes
0
Public
Click a slice to open those files.
.safetensors9.3 GB · 100%
From the Hugging Face model README
Gemma 4 E2B in MLX format, full bf16 precision, converted from
LetheanNetwork/lemer's
bf16 safetensors via mlx_lm.convert --dtype bfloat16. No quantization —
this is the full-precision reference for the MLX family. For smaller /
faster variants see
LetheanNetwork/lemer-mlx
(4-bit) or
LetheanNetwork/lemer-mlx-8bit.
For the LEK-merged variant see
lthn/lemer.
| Repo | Format | Bits | Use case |
|---|---|---|---|
LetheanNetwork/lemer | safetensors + gguf Q4_K_M | bf16 / 4 | Source weights + llama.cpp/Ollama |
LetheanNetwork/lemer-mlx | mlx | 4 | Apple Silicon default |
LetheanNetwork/lemer-mlx-8bit | mlx | 8 | Higher precision |
LetheanNetwork/lemer-mlx-bf16 | mlx | bf16 | This repo — full-precision reference |
from mlx_lm import load, generate
model, tokenizer = load("LetheanNetwork/lemer-mlx-bf16")
response = generate(
model, tokenizer,
prompt=tokenizer.apply_chat_template(
[{"role": "user", "content": "Hello"}],
add_generation_prompt=True,
enable_thinking=True,
),
max_tokens=512,
)
LetheanNetwork/lemer bf16 safetensors (= google/gemma-4-E2B-it)mlx_lm.convert --dtype bfloat16 (no quantization)Apache 2.0, subject to the Gemma Terms of Use.