introvoyz041/Olmo-3-1125-32B-mlx-4Bit
The Model introvoyz041/Olmo-3-1125-32B-mlx-4Bit was converted to MLX format from allenai/Olmo-3-1125-32B using mlx-lm version 0.28.3.
Use with mlx
pip install mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("introvoyz041/Olmo-3-1125-32B-mlx-4Bit")
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)
- Downloads last month
- 64
Model tree for introvoyz041/Olmo-3-1125-32B-mlx-4Bit
Base model
allenai/Olmo-3-1125-32BDataset used to train introvoyz041/Olmo-3-1125-32B-mlx-4Bit
Evaluation results
- Olmo 3-Eval Math on BenchmarksModel README61.600
- BigCodeBench on BenchmarksModel README43.900
- HumanEval on BenchmarksModel README66.500
- DeepSeek LeetCode on BenchmarksModel README1.900
- DS 1000 on BenchmarksModel README29.700
- MBPP on BenchmarksModel README60.200
- MultiPL HumanEval on BenchmarksModel README35.900
- MultiPL MBPPP on BenchmarksModel README41.800
- Olmo 3-Eval Code on BenchmarksModel README40.000
- ARC MC on BenchmarksModel README94.700