Ring-lite-distill-preview
Introduction
Ring-lite-distill-preview is an MoE LLM provided and open-sourced by InclusionAI, which has 16.8B parameters with 2.75B activated parameters. It was fine-tuned from Ling-lite using extensive reasoning-focused instruction data. This model delivers performance comparable to DeepSeek-R1-Distill-Qwen-7B on reasoning benchmarks while achieving better results on general benchmarks, especially superior performance on function-calling evaluation benchmarks (e.g., TEval, BFCl_v2) and instruction-following benchmarks (e.g., IFEval). This demonstrates that Ring-lite-distill is a more balanced and versatile model. Additionaly, it maintains competitive latency and throughput compared to other reasoning LLMs of similar size.
Model Downloads
| Model | #Total Params | #Activated Params | Context Length | Download |
|---|---|---|---|---|
| Ring-lite-distill-preview | 16.8B | 2.75B | 64K | 🤗 HuggingFace |
Evaluation
In order to fully evaluate the model's performance, we examined Ring-lite-distill-preview in terms of both reasoning ability and general ability.
Reasoning ability
| Model | AIME24 | MATH-500 | GPQA-diamond | LiveCodeBench |
|---|---|---|---|---|
| DeepSeek-R1-Distill-Qwen-7B (reported) | 55.5 | 92.8 | 49.1 | 37.6 |
| DeepSeek-R1-Distill-Qwen-7B (reproduce) | 53.2 | 93.7 | 50.4 | 36.5 |
| Ring-lite-distill-preview | 56.3 | 93.7 | 46.2 | 31.9 |
General ability
| Model | IFEval | T-eval | BFCL_v2 | MMLU |
|---|---|---|---|---|
| DeepSeek-R1-Distill-Qwen-7B (reproduce) | 39.3 | 26.9 | 38.9 | 44.1 |
| Ring-lite-distill-preview | 75.3 | 81.3 | 63.0 | 63.3 |
More details will be reported in our technical report.
Quickstart
🤗 Hugging Face Transformers
Here is a code snippet to show you how to use the chat model with transformers:
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "inclusionAI/Ring-lite-distill-preview"
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)
prompt = "Give me a short introduction to large language models."
messages = [
{"role": "system", "content": "You are Ring, an assistant created by inclusionAI"},
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
generated_ids = model.generate(
**model_inputs,
max_new_tokens=8192
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
Dataset
The training data of Ring-lite-distill-preview will be released soon.
Deployment
Please refer to GitHub
License
This code repository is licensed under the MIT License.
Citation
[TBD]