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lucyknada/
Salesforce_xgen-small-9B-instruct-r-exl3

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Welcome to the xGen-small family!

xGen-small (blog, arXiv) is an enterprise-ready compact LM that combines domain-focused data curation, scalable pre-training, length-extension, and RL fine-tuning to deliver long-context performance at predictable, low cost. This model release is for research purposes only.

Model Series

xGen-small comes in two sizes (4B and 9B) with two variants (pre-trained and post-trained):

Model	# Total Params	Context Length	Variant	Download
salesforce/xgen-small-4B-base-r	4B	128k	Pre-trained	🤗 Link
salesforce/xgen-small-4B-instruct-r	4B	128k	Post-trained	🤗 Link
salesforce/xgen-small-9B-base-r	9B	128k	Pre-trained	🤗 Link
salesforce/xgen-small-9B-instruct-r	9B	128k	Post-trained	🤗 Link

Usage

python

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "Salesforce/xgen-small-9B-instruct-r"
tokenizer = AutoTokenizer.from_pretrained(model_name)
device = "cuda" if torch.cuda.is_available() else "cpu"
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto"
).to(device)

prompt = "What is Salesforce?"
messages = [{"role": "user", "content": prompt}]
inputs = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt=True,
    return_tensors="pt"
).to(model.device)

generated = model.generate(inputs, max_new_tokens=128)
output = tokenizer.decode(
    generated[0],
    skip_special_tokens=True,
)
print(output)

Evaluation

Category	Task	Llama 3.1-8B	Granite 3.3-8B	Qwen2.5-7B	xGen-small 9B Instruct
General Knowledge & Reasoning	MMLU	68.3	62.7	72.4	72.4
General Knowledge & Reasoning	MMLU-Pro	43.2	43.5	56.7	57.3
Chat	Arena-Hard-v1.0	28.9	30.5	48.1	60.1
Chat	MT-Bench	8.25	8.57	8.56	8.90
Math & Science	GPQA	31.9	35.3	32.6	45.8
Math & Science	GSM8K	84.2	89.4	91.9	95.3
Math & Science	MATH	48.9	70.9	74.6	91.6
Math & Science	AIME 2024	6.7	10.0	6.7	50.0
Coding	HumanEval+	61.6	65.9	74.4	78.7
Coding	MBPP+	55.3	60.3	68.8	63.8
Coding	LiveCodeBench	10.3	10.3	12.1	50.6

Citation

bibtex

@misc{xgensmall,
      title={xGen-small Technical Report}, 
      author={Erik Nijkamp and Bo Pang and Egor Pakhomov and Akash Gokul and Jin Qu and Silvio Savarese and Yingbo Zhou and Caiming Xiong},
      year={2025},
      eprint={2505.06496},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2505.06496}, 
}

Ethical Considerations

This release is for research purposes only in support of an academic paper. Our models, datasets, and code are not specifically designed or evaluated for all downstream purposes. We strongly recommend users evaluate and address potential concerns related to accuracy, safety, and fairness before deploying this model. We encourage users to consider the common limitations of AI, comply with applicable laws, and leverage best practices when selecting use cases, particularly for high-risk scenarios where errors or misuse could significantly impact people's lives, rights, or safety. For further guidance on use cases, refer to our AUP and AI AUP.

lucyknada/Salesforce_xgen-small-9B-instruct-r-exl3