---
title: scitulu-70b
canonical_url: "https://www.modelscope.cn/models/LLM-Research/scitulu-70b"
md_url: "https://www.modelscope.cn/models/LLM-Research/scitulu-70b.md"
repository: LLM-Research/scitulu-70b
last_updated: 2024-09-07
license: apache-2.0
pipeline_tag: text-generation
tasks:
  - text-generation
model_type:
  - llama
architectures:
  - LlamaForCausalLM
library_name:
  - pytorch
frameworks:
  - pytorch
language:
  - en
inference_backends:
  - "deploy_task text/emb"
  - "lmdeploy 0.9.1"
  - "lmdeploy_turbomind 0.9.1"
  - "sglang 0.5.2"
  - "vllm 0.9.2"
downloads: 2150
stars: 0
---

# scitulu-70b

> scitulu-70b - LLM-Research 在 ModelScope 开源的模型。Model Card for SciTulu 70B

LLM-Research/scitulu-70b 是 ModelScope 魔搭社区上的text-generation模型，采用 apache-2.0 许可，可用 deploy_task text/emb、lmdeploy 0.9.1、lmdeploy_turbomind 0.9.1 部署。

- **Repository**: LLM-Research/scitulu-70b
- **License**: apache-2.0
- **Tasks**: text-generation
- **Inference backends**: deploy_task text/emb, lmdeploy 0.9.1, lmdeploy_turbomind 0.9.1, sglang 0.5.2, vllm 0.9.2
- **Downloads**: 2150
- **Stars**: 0
- **Last updated**: 2024-09-07

Source: https://www.modelscope.cn/models/LLM-Research/scitulu-70b

---

# Model Card for SciTulu 70B

SciTulu is a collection of instruction-following language models targeting scientific literature understanding use cases. Starting from the [Tulu v2 70B](https://huggingface.co/allenai/tulu-2-70b) model, SciTulu is trained on a mix of science-specific demonstrations from the [SciRIFF dataset](https://huggingface.co/datasets/allenai/SciRIFF-train-mix), together with general-domain instructions from the [Tulu v2 SFT mix](https://huggingface.co/datasets/allenai/tulu-v2-sft-mixture). SciTulu 70B achives a 6.5% average improvement over Tulu v2 70B on nine held-out scientific literature understanding tasks. More information can be found in our preprint: [SciRIFF: A Resource to Enhance Language Model Instruction-Following over Scientific Literature](https://arxiv.org/abs/2406.07835).

Training and evaluation code for SciTulu is available in our GitHub repository: https://github.com/allenai/SciRIFF.

See the [Tulu model card](https://huggingface.co/allenai/tulu-2-70b) for more information on potential risks, biases, and limitations.