Text Generation
MLX
Safetensors
English
qwen2
qwen
qwen2.5
32b
creative-writing
roleplay
Not-For-All-Audiences
conversational
6-bit
Instructions to use darthcrawl/EVA-Qwen2.5-32B-v0.2-mlx-6bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use darthcrawl/EVA-Qwen2.5-32B-v0.2-mlx-6bit with MLX:
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("darthcrawl/EVA-Qwen2.5-32B-v0.2-mlx-6bit") prompt = "Write a story about Einstein" messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- MLX LM
How to use darthcrawl/EVA-Qwen2.5-32B-v0.2-mlx-6bit with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "darthcrawl/EVA-Qwen2.5-32B-v0.2-mlx-6bit"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "darthcrawl/EVA-Qwen2.5-32B-v0.2-mlx-6bit" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "darthcrawl/EVA-Qwen2.5-32B-v0.2-mlx-6bit", "messages": [ {"role": "user", "content": "Hello"} ] }' - Atomic Chat
EVA-Qwen2.5-32B-v0.2-mlx-6bit
MLX 6-bit quant of EVA-UNIT-01/EVA-Qwen2.5-32B-v0.2. Disk: ~25 GB. Active memory: ~27 GB plus KV cache.
Quantization
mlx_lm.convert --hf-path EVA-UNIT-01/EVA-Qwen2.5-32B-v0.2 \
--mlx-path ./EVA-Qwen2.5-32B-v0.2-mlx-6bit -q --q-bits 6
REQUIRED: EOS patch before serving
Same as the 4-bit. Source ships incomplete eos_token_id. Patch needed for ChatML turn boundaries:
python3 -c "
import json
p = './EVA-Qwen2.5-32B-v0.2-mlx-6bit/generation_config.json'
g = json.load(open(p))
g['eos_token_id'] = [151643, 151645]
json.dump(g, open(p, 'w'), indent=2)
"
This quant has it applied.
Recommended sampling
Same as 4-bit:
temperature: 1.0
min_p: 0.05
repetition_penalty: 1.05
repetition_context_size: 2048
Known mlx_lm.server bugs
Same as the 4-bit (XTC tokenizer bug, seed ignored, prompt-cache size cap recommended).
License
Apache-2.0 from base.
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Model size
33B params
Tensor type
BF16
·
U32 ·
Hardware compatibility
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6-bit