README.md

metadata

library_name: transformers
license: other
license_name: eva-llama3.3
base_model: EVA-UNIT-01/EVA-LLaMA-3.33-70B-v0.0
tags:
  - generated_from_trainer
  - mlx
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
  - cognitivecomputations/dolphin-2.9.3
model-index:
  - name: dev/shm/EVA-LLaMA-3.33-70B-v0.1
    results: []

shanginn/EVA-UNIT-01-EVA-LLaMA-3.33-70B-v0.0-q4

The Model shanginn/EVA-UNIT-01-EVA-LLaMA-3.33-70B-v0.0-q4 was converted to MLX format from EVA-UNIT-01/EVA-LLaMA-3.33-70B-v0.0 using mlx-lm version 0.19.2.

Use with mlx

pip install mlx-lm

from mlx_lm import load, generate

model, tokenizer = load("shanginn/EVA-UNIT-01-EVA-LLaMA-3.33-70B-v0.0-q4")

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)