BigQwen2.5-52B-Instruct

BigQwen2.5-52B-Instruct

BigQwen2.5-52B-Instruct is a Qwen/Qwen2-32B-Instruct self-merge made with MergeKit.

It applies the mlabonne/Meta-Llama-3-120B-Instruct recipe.

I made it due to popular demand but I haven't tested it so use it at your own risk. ¯\_(ツ)_/¯

🔍 Applications

It might be good for creative writing tasks. I recommend a context length of 32k but you can go up to 131,072 tokens in theory.

🏆 Evaluation

🧩 Configuration

The following YAML configuration was used to produce this model:

slices:
- sources:
  - layer_range: [0, 16]
    model: Qwen/Qwen2.5-32B-Instruct
- sources:
  - layer_range: [8, 24]
    model: Qwen/Qwen2.5-32B-Instruct
- sources:
  - layer_range: [16, 32]
    model: Qwen/Qwen2.5-32B-Instruct
- sources:
  - layer_range: [24, 40]
    model: Qwen/Qwen2.5-32B-Instruct
- sources:
  - layer_range: [32, 48]
    model: Qwen/Qwen2.5-32B-Instruct
- sources:
  - layer_range: [40, 56]
    model: Qwen/Qwen2.5-32B-Instruct
- sources:
  - layer_range: [56, 64]
    model: Qwen/Qwen2.5-32B-Instruct
merge_method: passthrough
dtype: bfloat16

💻 Usage

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "mlabonne/BigQwen2.5-52B-Instruct"
messages = [{"role": "user", "content": "What is a large language model?"}]

tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    torch_dtype=torch.float16,
    device_map="auto",
)

outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])