初始化项目，由ModelHub XC社区提供模型
Model: Salesforce/xgen-small-9B-base-r Source: Original Platform
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---
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license: cc-by-nc-4.0
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language:
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- en
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library_name: transformers
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---
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# Welcome to the xGen-small family!
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**xGen-small** ([blog](https://www.salesforce.com/blog/xgen-small-enterprise-ready-small-language-models/), [arXiv](https://arxiv.org/abs/2505.06496)) 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.
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**This model release is for research purposes only.**
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<p align="center">
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<img width="60%" src="https://huggingface.co/Salesforce/xgen-small/resolve/main/xgen-small.png?download=true">
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</p>
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## Model Series
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[xGen-small](https://www.salesforce.com/blog/xgen-small-enterprise-ready-small-language-models/) comes in two sizes (4B and 9B) with two variants (pre-trained and post-trained):
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| Model | # Total Params | Context Length | Variant | Download |
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|---------------------------------------|----------------|----------------|--------------|----------------|
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| salesforce/xgen-small-4B-base-r | 4B | 128k | Pre-trained | [🤗 Link](https://huggingface.co/Salesforce/xgen-small-4b-base-r) |
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| salesforce/xgen-small-4B-instruct-r | 4B | 128k | Post-trained | [🤗 Link](https://huggingface.co/Salesforce/xgen-small-4b-instruct-r) |
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| salesforce/xgen-small-9B-base-r | 9B | 128k | Pre-trained | [🤗 Link](https://huggingface.co/Salesforce/xgen-small-9b-base-r) |
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| salesforce/xgen-small-9B-instruct-r | 9B | 128k | Post-trained | [🤗 Link](https://huggingface.co/Salesforce/xgen-small-9b-instruct-r) |
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## Usage
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```python
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_name = "Salesforce/xgen-small-9B-base-r"
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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device = "cuda" if torch.cuda.is_available() else "cpu"
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model = AutoModelForCausalLM.from_pretrained(
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model_name,
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torch_dtype="auto"
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).to(device)
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prompt = "What is Salesforce?"
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inputs = tokenizer(
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prompt,
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return_tensors="pt",
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padding=False,
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truncation=True
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).to(device)
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generated = model.generate(**inputs, max_new_tokens=32)
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output = tokenizer.decode(
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generated[0],
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skip_special_tokens=True,
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)
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print(output)
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```
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## Evaluation
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| Category | Task | Llama 3.1-8B | Granite 3.3-8B | Qwen2.5-7B | xGen-small 9B Base|
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| :------------------------------- | :------------- | :----------- | :------------- | :--------- | :-----------------|
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| General Knowledge & Reasoning | ARC-Challenge | 58.0 | 62.5 | 63.7 | 67.4 |
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| General Knowledge & Reasoning | Big-Bench Hard | 46.3 | 46.8 | 53.6 | 58.2 |
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| General Knowledge & Reasoning | HellaSwag | 81.8 | 83.0 | 80.0 | 83.7 |
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| General Knowledge & Reasoning | MMLU | 65.1 | 62.7 | 74.2 | 71.1 |
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| General Knowledge & Reasoning | MMLU-Pro | 32.7 | 31.3 | 43.7 | 39.8 |
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| General Knowledge & Reasoning | TruthfulQA | 45.2 | 52.2 | 56.4 | 48.6 |
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| General Knowledge & Reasoning | WinoGrande | 76.9 | 80.3 | 76.1 | 78.6 |
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| Math & Science | GPQA | 31.9 | 30.3 | 31.4 | 32.0 |
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| Math & Science | GSM8K | 55.6 | 61.4 | 79.1 | 83.2 |
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| Math & Science | MATH | 22.0 | 30.9 | 50.2 | 52.5 |
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| Coding | HumanEval | 37.3 | 38.9 | 55.2 | 53.9 |
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| Coding | HumanEval+ | 31.4 | 34.3 | 47.7 | 47.9 |
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| Coding | MBPP | 45.0 | 43.5 | 57.1 | 50.1 |
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| Coding | MBPP+ | 51.0 | 48.1 | 64.8 | 57.6 |
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## Citation
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```bibtex
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@misc{xgensmall,
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title={xGen-small Technical Report},
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author={Erik Nijkamp and Bo Pang and Egor Pakhomov and Akash Gokul and Jin Qu and Silvio Savarese and Yingbo Zhou and Caiming Xiong},
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year={2025},
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eprint={2505.06496},
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archivePrefix={arXiv},
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primaryClass={cs.CL},
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url={https://arxiv.org/abs/2505.06496},
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}
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```
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## Ethical Considerations
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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.
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## Model Licenses
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The models are being released under CC-BY-NC-4.0, Copyright © Salesforce, Inc. All Rights Reserved.
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|
||||
"single_word": false,
|
||||
"special": true
|
||||
}
|
||||
},
|
||||
"bos_token": "<|endoftext|>",
|
||||
"chat_template": "{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '\n<|im_end|>' + '\n'}}{% endfor %}{{ '<|im_start|>assistant\n' }}",
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "<|endoftext|>",
|
||||
"extra_special_tokens": {},
|
||||
"model_max_length": 16384,
|
||||
"tokenizer_class": "GPT2Tokenizer",
|
||||
"unk_token": "<|endoftext|>"
|
||||
}
|
||||
3
vocab.json
Normal file
3
vocab.json
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:240b7e18e79826df8c0dbeff7133a3ab4708d7958fede6e27e9409e09159375f
|
||||
size 1610691
|
||||
Reference in New Issue
Block a user