初始化项目，由ModelHub XC社区提供模型
Model: sethuiyer/Nandine-7b Source: Original Platform
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README.md
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README.md
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---
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language:
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- en
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license: apache-2.0
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library_name: transformers
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tags:
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- merge
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- mergekit
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- lazymergekit
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inference: false
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base_model:
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- senseable/Westlake-7B
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- Guilherme34/Samantha-v2
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- uukuguy/speechless-mistral-six-in-one-7b
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pipeline_tag: text-generation
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model-index:
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- name: sethuiyer/Nandine-7b
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results:
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: AI2 Reasoning Challenge (25-Shot)
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type: ai2_arc
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config: ARC-Challenge
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split: test
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args:
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num_few_shot: 25
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metrics:
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- type: acc_norm
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value: 69.28
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name: normalized accuracy
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source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sethuiyer/Nandine-7b
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: HellaSwag (10-Shot)
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type: hellaswag
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split: validation
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args:
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num_few_shot: 10
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metrics:
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- type: acc_norm
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value: 87.01
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name: normalized accuracy
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source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sethuiyer/Nandine-7b
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: MMLU (5-Shot)
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type: cais/mmlu
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config: all
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split: test
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args:
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num_few_shot: 5
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metrics:
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- type: acc
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value: 64.83
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name: accuracy
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source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sethuiyer/Nandine-7b
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: TruthfulQA (0-shot)
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type: truthful_qa
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config: multiple_choice
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split: validation
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args:
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num_few_shot: 0
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metrics:
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- type: mc2
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value: 62.1
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source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sethuiyer/Nandine-7b
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: Winogrande (5-shot)
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type: winogrande
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config: winogrande_xl
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split: validation
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args:
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num_few_shot: 5
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metrics:
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- type: acc
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value: 83.19
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name: accuracy
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source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sethuiyer/Nandine-7b
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: GSM8k (5-shot)
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type: gsm8k
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config: main
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split: test
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args:
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num_few_shot: 5
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metrics:
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- type: acc
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value: 62.4
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name: accuracy
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source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sethuiyer/Nandine-7b
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name: Open LLM Leaderboard
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---
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# Nandine-7b
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<p align="center">
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<img src="https://huggingface.co/sethuiyer/Nandine-7b/resolve/main/nandine.webp" height="128px" alt="Nandine">
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</p>
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This is Nandine-7b, rated **87.47/100** by GPT-4 on a collection of 30 synthetic prompts generated by GPT-4.
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Nandine-7b is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
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* [senseable/Westlake-7B](https://huggingface.co/senseable/Westlake-7B)
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* [Guilherme34/Samantha-v2](https://huggingface.co/Guilherme34/Samantha-v2)
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* [uukuguy/speechless-mistral-six-in-one-7b](https://huggingface.co/uukuguy/speechless-mistral-six-in-one-7b)
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Nandine-7b represents a harmonious amalgamation of narrative skill, empathetic interaction, intellectual depth, and eloquent communication.
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## OpenLLM Benchmark
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| Model | Average ⬆️ | ARC | HellaSwag | MMLU | TruthfulQA | Winogrande | GSM8K |
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|--------------------------------|------------|-------|-----------|-------|------------|------------|-------|
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| sethuiyer/Nandine-7b 📑 | 71.47 | 69.28 | 87.01 | 64.83 | 62.1 | 83.19 | 62.4 |
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## Nous Benchmark
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| Model |AGIEval|GPT4All|TruthfulQA|Bigbench|Average|
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|---------------------------------------------------------|------:|------:|---------:|-------:|------:|
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|[Nandine-7b](https://huggingface.co/sethuiyer/Nandine-7b)| 43.54| 76.41| 61.73| 45.27| 56.74|
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For more details, refer [here](https://huggingface.co/sethuiyer/Nandine-7b/blob/main/EVAL.md)
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**Pros:**
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1. **Strong Narrative Skills:** Excels in storytelling, creating engaging and imaginative narratives.
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2. **Accurate Information Delivery:** Provides factual and detailed information across various topics.
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3. **Comprehensive Analysis:** Capable of well-rounded discussions on complex and ethical topics.
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4. **Emotional Intelligence:** Shows empathy and understanding in responses requiring emotional sensitivity.
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5. **Clarity and Structure:** Maintains clear and well-structured communication.
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**Cons:**
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1. **Language Translation Limitations:** Challenges in providing fluent and natural translations.
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2. **Incomplete Problem Solving:** Some logical or mathematical problems are not solved accurately.
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3. **Lack of Depth in Certain Areas:** Needs deeper exploration in some responses for a more comprehensive understanding.
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4. **Occasional Imbalance in Historical Context:** Some historical explanations could be more balanced.
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5. **Room for Enhanced Creativity:** While creative storytelling is strong, there's potential for more varied responses in hypothetical scenarios.
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**Intended Use:**
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Ideal for users seeking a versatile AI companion for creative writing, thoughtful discussions, and general assistance.
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## 🧩 Configuration
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```yaml
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models:
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- model: senseable/Westlake-7B
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parameters:
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weight: 0.55
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density: 0.6
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- model: Guilherme34/Samantha-v2
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parameters:
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weight: 0.10
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density: 0.3
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- model: uukuguy/speechless-mistral-six-in-one-7b
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parameters:
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weight: 0.35
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density: 0.6
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merge_method: dare_ties
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base_model: mistralai/Mistral-7B-v0.1
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parameters:
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int8_mask: true
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dtype: bfloat16
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```
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## 💻 Usage
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```python
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!pip install -qU transformers accelerate
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from transformers import AutoTokenizer
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import transformers
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import torch
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model = "sethuiyer/Nandine-7b"
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messages = [{"role": "user", "content": "What is a large language model?"}]
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tokenizer = AutoTokenizer.from_pretrained(model)
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prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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pipeline = transformers.pipeline(
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"text-generation",
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model=model,
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torch_dtype=torch.float16,
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device_map="auto",
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)
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outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
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print(outputs[0]["generated_text"])
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```
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## GGUF
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GGUF files are available at [Nandine-7b-GGUF](https://huggingface.co/sethuiyer/Nandine-7b-GGUF/tree/main)
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## Ollama
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Nandine is now available on Ollama. You can use it by running the command ```ollama run stuehieyr/nandine``` in your
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terminal. If you have limited computing resources, check out this [video](https://www.youtube.com/watch?v=Qa1h7ygwQq8) to learn how to run it on
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a Google Colab backend.
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# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
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Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_sethuiyer__Nandine-7b)
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| Metric |Value|
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|---------------------------------|----:|
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|Avg. |71.47|
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|AI2 Reasoning Challenge (25-Shot)|69.28|
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|HellaSwag (10-Shot) |87.01|
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|MMLU (5-Shot) |64.83|
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|TruthfulQA (0-shot) |62.10|
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|Winogrande (5-shot) |83.19|
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|GSM8k (5-shot) |62.40|
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