alexgusevski/CapybaraHermes-2.5-Mistral-7B-mlx-2Bit
The Model alexgusevski/CapybaraHermes-2.5-Mistral-7B-mlx-2Bit was converted to MLX format from argilla/CapybaraHermes-2.5-Mistral-7B using mlx-lm version 0.29.1.
Use with mlx
pip install mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("alexgusevski/CapybaraHermes-2.5-Mistral-7B-mlx-2Bit")
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)
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Model size
0.7B params
Tensor type
F16
·
U32
·
Hardware compatibility
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2-bit
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Model tree for alexgusevski/CapybaraHermes-2.5-Mistral-7B-mlx-2Bit
Base model
mistralai/Mistral-7B-v0.1
Finetuned
teknium/OpenHermes-2.5-Mistral-7B
Finetuned
argilla/CapybaraHermes-2.5-Mistral-7B
Dataset used to train alexgusevski/CapybaraHermes-2.5-Mistral-7B-mlx-2Bit
Evaluation results
- normalized accuracy on AI2 Reasoning Challenge (25-Shot)test set Open LLM Leaderboard65.780
- normalized accuracy on HellaSwag (10-Shot)validation set Open LLM Leaderboard85.450
- accuracy on MMLU (5-Shot)test set Open LLM Leaderboard63.130
- mc2 on TruthfulQA (0-shot)validation set Open LLM Leaderboard56.910
- accuracy on Winogrande (5-shot)validation set Open LLM Leaderboard78.300
- accuracy on GSM8k (5-shot)test set Open LLM Leaderboard59.290