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--- |
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language: |
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- zh |
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- en |
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pipeline_tag: text-generation |
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license: other |
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license_name: llama3 |
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license_link: LICENSE |
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tags: |
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- llama3 |
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- chinese |
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- meta |
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--- |
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# llama-3-8b-instruct-262k-chinese-lora |
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llama-3-8b-instruct-262k-chinese基于[Llama-3-8B-Instruct-262k](https://huggingface.co/gradientai/Llama-3-8B-Instruct-262k)，使用ORPO方法，在中英文偏好数据集[shibing624/DPO-En-Zh-20k-Preference](https://huggingface.co/datasets/shibing624/DPO-En-Zh-20k-Preference) |
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上微调得到的对话模型。 |
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模型的部署、训练等方法详见MedicalGPT的GitHub仓库：[https://github.com/shibing624/MedicalGPT](https://github.com/shibing624/MedicalGPT) |
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## Relate models |
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- 完整模型权重：https://huggingface.co/shibing624/llama-3-8b-instruct-262k-chinese |
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- lora权重：https://huggingface.co/shibing624/llama-3-8b-instruct-262k-chinese-lora |
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## Features |
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模型优势： |
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1. 支持超长context length 262k token，适合RAG |
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2. 支持中英文 |
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3. 支持多轮对话，代码编码、推理能力强，英文知识充分 |
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4. 模型推理需要显存： |
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Quantization | Peak Usage for Encoding 2048 Tokens | Peak Usage for Generating 8192 Tokens |
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-- | -- | -- |
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FP16/BF16 | 18.66GB | 24.58GB |
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Int4 | 9.21GB | 14.62GB |
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缺点： |
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1. model size只有8B，知识类问答幻觉明显 |
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2. 中文知识欠缺，容易幻觉，特别是中文古文知识，属于llama类模型通病 |
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## 如何使用 |
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```python |
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import transformers |
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import torch |
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model_id = "shibing624/llama-3-8b-instruct-262k-chinese" |
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pipeline = transformers.pipeline( |
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"text-generation", |
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model=model_id, |
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model_kwargs={"torch_dtype": torch.float16}, |
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device="cuda", |
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) |
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messages = [{"role": "system", "content": ""}] |
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messages.append({"role": "user", "content": "介绍一下机器学习"}) |
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prompt = pipeline.tokenizer.apply_chat_template( |
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messages, |
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tokenize=False, |
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add_generation_prompt=True |
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) |
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terminators = [ |
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pipeline.tokenizer.eos_token_id, |
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pipeline.tokenizer.convert_tokens_to_ids("<|eot_id|>") |
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] |
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outputs = pipeline( |
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prompt, |
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max_new_tokens=512, |
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eos_token_id=terminators, |
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do_sample=True, |
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temperature=0.6, |
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top_p=0.9 |
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) |
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content = outputs[0]["generated_text"][len(prompt):] |
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print(content) |
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``` |
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result: |
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```shell |
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机器学习（Machine Learning）是一种基于计算机算法的自动数据分析技术，用于从数据中学习并预测未来的结果。它是人工智能（AI）和数据挖掘（Data Mining）的子领域，旨在通过训练和调整算法来发现数据中的模式、关系和规律。 |
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机器学习算法可以分为监督学习、无监督学习和半监督学习三类： |
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1. 监督学习（Supervised Learning）：在这种类型的学习中，算法被提供带有标签的数据集，用于训练。算法学习如何将输入数据映射到输出数据，并在新数据上进行预测。常见的监督学习算法包括逻辑回归、决策树、支持向量机（SVM）、随机森林和神经网络。 |
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2. 无监督学习（Unsupervised Learning）：在这种类型的学习中，算法没有标签数据。算法学习数据中的模式、结构和关系，并可能发现新的数据集群或特征。常见的无监督学习算法包括聚类、主成分分析（PCA）、独立成分分析（ICA）和高维度数据降维。 |
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3. 半监督学习（Semi-supervised Learning）：在这种类型的学习中，算法被提供部分带有标签的数据集。算法学习如何将输入数据映射到输出数据，并在新数据上进行预测。半监督学习算法结合了监督学习和无监督学习的优点，常见的半监督学习算法包括自我标注（Self-Labeling）和基于图的半监督学习（Graph-based Semi-supervised Learning）。 |
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机器学习的应用广泛，包括自然语言处理、计算机视觉、推荐系统、人工智能和自动驾驶等领域。它的优势包括： |
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1. 自动化：机器学习算法可以自动从数据中发现模式和关系，无需人为干预。 |
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2. 高效性：机器学习算法可以处理大量数据，并且可以在不需要人为干预的情况下进行预测。 |
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3. 适应性：机器学习算法可以根据数据集的变化和更新进行调整。 |
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4. 精准性：机器学习算法可以通过训练和测试来提高预测的准确性。 |
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``` |
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## train detail |
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train loss: |
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<img src="https://huggingface.co/shibing624/llama-3-8b-instruct-262k-chinese/raw/main/train_lossv2.svg" width="600"> |
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eval loss: |
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<img src="https://huggingface.co/shibing624/llama-3-8b-instruct-262k-chinese/raw/main/eval_lossv2.svg" width="600"> |
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# About Llama-3-8B-Instruct-262k |
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Gradient incorporates your data to deploy autonomous assistants that power critical operations across your business. To learn more or collaborate on a custom model. |
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This model extends LLama-3 8B's context length from 8k to -> 160K, developed by Gradient, sponsored by compute from [Crusoe Energy](https://huggingface.co/crusoeai). It demonstrates that SOTA LLMs can learn to operate on long context with minimal training (< 200M tokens) by appropriately adjusting RoPE theta. |
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<img src="https://cdn-uploads.huggingface.co/production/uploads/6585dc9be92bc5f258156bd6/hiHWva3CbsrnPvZTp5-lu.png" width="600"> |
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**Approach:** |
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- [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) as the base |
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- NTK-aware interpolation [1] to initialize an optimal schedule for RoPE theta, followed by a new data-driven RoPE theta optimization technique |
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- Progressive training on increasing context lengths similar to the [Large World Model](https://huggingface.co/LargeWorldModel) [2] (See details below) |
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**Infra:** |
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We build on top of the EasyContext Blockwise RingAttention library [3] to scalably and efficiently train on contexts up to 262144 tokens on [Crusoe Energy](https://huggingface.co/crusoeai) high performance L40S cluster. |
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**Data:** |
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For training data, we generate long contexts by augmenting [SlimPajama](https://huggingface.co/datasets/cerebras/SlimPajama-627B). |
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**Progressive Training Details:** |
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| Parameter | 65K | 262K | |
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|-----------------------------|----------------|------------| |
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| Initialize From | LLaMA-3-8B-Inst| 65K | |
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| Sequence Length | 2^16 | 2^18 | |
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| RoPE theta | 15.3 M | 207.1 M | |
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| Batch Size (Tokens / Step) | 2.097 M | 4.192 M | |
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| Steps | 30 | 24 | |
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| Total Tokens | 63 M | 101 M | |
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| Learning Rate | 2.00E-05 | 2.00E-05 | |
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| # GPUs | 32 | 32 | |
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| GPU Type | NVIDIA L40S | NVIDIA L40S| |
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