README.md

metadata

base_model: EVA-UNIT-01/EVA-Qwen2.5-72B-v0.0
datasets:
  - anthracite-org/kalo-opus-instruct-22k-no-refusal
  - Nopm/Opus_WritingStruct
  - Gryphe/Sonnet3.5-SlimOrcaDedupCleaned
  - Gryphe/Sonnet3.5-Charcard-Roleplay
  - Gryphe/ChatGPT-4o-Writing-Prompts
  - Epiculous/Synthstruct-Gens-v1.1-Filtered-n-Cleaned
  - Epiculous/SynthRP-Gens-v1.1-Filtered-n-Cleaned
  - nothingiisreal/Reddit-Dirty-And-WritingPrompts
  - allura-org/Celeste-1.x-data-mixture
library_name: transformers
license: other
license_name: qwen
license_link: https://huggingface.co/Qwen/Qwen2.5-72B-Instruct/blob/main/LICENSE
tags:
  - generated_from_trainer
  - mlx
model-index:
  - name: EVA-Qwen2.5-72B-SFFT-v0.0
    results: []

mlx-community/EVA-Qwen2.5-72B-v0.0-8bit

The Model mlx-community/EVA-Qwen2.5-72B-v0.0-8bit was converted to MLX format from EVA-UNIT-01/EVA-Qwen2.5-72B-v0.0 using mlx-lm version 0.19.0.

Use with mlx

pip install mlx-lm

from mlx_lm import load, generate

model, tokenizer = load("mlx-community/EVA-Qwen2.5-72B-v0.0-8bit")

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)