Instructions to use mlabonne/BigQwen2.5-52B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mlabonne/BigQwen2.5-52B-Instruct with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="mlabonne/BigQwen2.5-52B-Instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("mlabonne/BigQwen2.5-52B-Instruct")
model = AutoModelForCausalLM.from_pretrained("mlabonne/BigQwen2.5-52B-Instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use mlabonne/BigQwen2.5-52B-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mlabonne/BigQwen2.5-52B-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mlabonne/BigQwen2.5-52B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/mlabonne/BigQwen2.5-52B-Instruct

SGLang

How to use mlabonne/BigQwen2.5-52B-Instruct with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "mlabonne/BigQwen2.5-52B-Instruct" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mlabonne/BigQwen2.5-52B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "mlabonne/BigQwen2.5-52B-Instruct" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mlabonne/BigQwen2.5-52B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use mlabonne/BigQwen2.5-52B-Instruct with Docker Model Runner:
```
docker model run hf.co/mlabonne/BigQwen2.5-52B-Instruct
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

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

Metric	BigQwen2.5-Echo-47B-Instruct	BigQwen2.5-52B-Instruct	Qwen2.5-32B-Instruct
Avg.	30.31	37.42	36.17
IFEval (0-Shot)	73.57	79.29	83.46
BBH (3-Shot)	44.52	59.81	56.49
MATH Lvl 5 (4-Shot)	3.47	17.82	0
GPQA (0-shot)	8.61	6.94	11.74
MuSR (0-shot)	10.19	10.45	13.5
MMLU-PRO (5-shot)	41.49	50.22	51.85

🧩 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"])

Downloads last month: 14

Safetensors

Model size

52B params

Tensor type

BF16

Model tree for mlabonne/BigQwen2.5-52B-Instruct

Base model

Qwen/Qwen2.5-32B

Finetuned

Qwen/Qwen2.5-32B-Instruct

Finetuned

(1218)

this model

Quantizations

3 models

Evaluation results

strict accuracy on IFEval (0-Shot)
Open LLM Leaderboard

79.290
normalized accuracy on BBH (3-Shot)
Open LLM Leaderboard

59.810
exact match on MATH Lvl 5 (4-Shot)
Open LLM Leaderboard

17.820
acc_norm on GPQA (0-shot)
Open LLM Leaderboard

6.940
acc_norm on MuSR (0-shot)
Open LLM Leaderboard

10.450
accuracy on MMLU-PRO (5-shot)
test set Open LLM Leaderboard

50.220