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[More Information Needed]
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#### Training Hyperparameters
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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#### Speeds, Sizes, Times [optional]
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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[More Information Needed]
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## Evaluation
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<!-- This section describes the evaluation protocols and provides the results. -->
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### Testing Data, Factors & Metrics
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#### Testing Data
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<!-- This should link to a Dataset Card if possible. -->
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[More Information Needed]
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#### Factors
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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[More Information Needed]
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#### Metrics
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<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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[More Information Needed]
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### Results
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[More Information Needed]
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#### Summary
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## Model Examination [optional]
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<!-- Relevant interpretability work for the model goes here -->
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[More Information Needed]
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## Environmental Impact
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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- **Hardware Type:** [More Information Needed]
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- **Hours used:** [More Information Needed]
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- **Cloud Provider:** [More Information Needed]
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- **Compute Region:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
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## Technical Specifications [optional]
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### Model Architecture and Objective
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[More Information Needed]
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### Compute Infrastructure
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[More Information Needed]
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#### Hardware
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[More Information Needed]
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#### Software
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[More Information Needed]
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## Citation [optional]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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**BibTeX:**
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[More Information Needed]
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**APA:**
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[More Information Needed]
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## Glossary [optional]
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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[More Information Needed]
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## More Information [optional]
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[More Information Needed]
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## Model Card Authors [optional]
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[More Information Needed]
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## Model Card Contact
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[More Information Needed]
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---
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license: other
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tags:
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- merge
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- mergekit
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- lazymergekit
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base_model:
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- nbeerbower/llama-3-stella-8B
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- Hastagaras/llama-3-8b-okay
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- nbeerbower/llama-3-gutenberg-8B
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- openchat/openchat-3.6-8b-20240522
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- Kukedlc/NeuralLLaMa-3-8b-DT-v0.1
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- cstr/llama3-8b-spaetzle-v20
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- mlabonne/ChimeraLlama-3-8B-v3
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- flammenai/Mahou-1.1-llama3-8B
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- KingNish/KingNish-Llama3-8b
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# Daredevil-8B
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Daredevil-8B is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
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* [nbeerbower/llama-3-stella-8B](https://huggingface.co/nbeerbower/llama-3-stella-8B)
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* [Hastagaras/llama-3-8b-okay](https://huggingface.co/Hastagaras/llama-3-8b-okay)
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* [nbeerbower/llama-3-gutenberg-8B](https://huggingface.co/nbeerbower/llama-3-gutenberg-8B)
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* [openchat/openchat-3.6-8b-20240522](https://huggingface.co/openchat/openchat-3.6-8b-20240522)
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* [Kukedlc/NeuralLLaMa-3-8b-DT-v0.1](https://huggingface.co/Kukedlc/NeuralLLaMa-3-8b-DT-v0.1)
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* [cstr/llama3-8b-spaetzle-v20](https://huggingface.co/cstr/llama3-8b-spaetzle-v20)
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* [mlabonne/ChimeraLlama-3-8B-v3](https://huggingface.co/mlabonne/ChimeraLlama-3-8B-v3)
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* [flammenai/Mahou-1.1-llama3-8B](https://huggingface.co/flammenai/Mahou-1.1-llama3-8B)
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* [KingNish/KingNish-Llama3-8b](https://huggingface.co/KingNish/KingNish-Llama3-8b)
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## 🧩 Configuration
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```yaml
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models:
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- model: NousResearch/Meta-Llama-3-8B
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# No parameters necessary for base model
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- model: nbeerbower/llama-3-stella-8B
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parameters:
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density: 0.6
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weight: 0.16
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- model: Hastagaras/llama-3-8b-okay
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parameters:
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density: 0.56
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weight: 0.1
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- model: nbeerbower/llama-3-gutenberg-8B
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parameters:
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density: 0.6
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weight: 0.18
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- model: openchat/openchat-3.6-8b-20240522
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parameters:
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density: 0.56
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weight: 0.12
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- model: Kukedlc/NeuralLLaMa-3-8b-DT-v0.1
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parameters:
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density: 0.58
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weight: 0.18
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- model: cstr/llama3-8b-spaetzle-v20
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parameters:
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density: 0.56
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weight: 0.08
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- model: mlabonne/ChimeraLlama-3-8B-v3
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parameters:
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density: 0.56
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weight: 0.08
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- model: flammenai/Mahou-1.1-llama3-8B
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parameters:
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density: 0.55
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weight: 0.05
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- model: KingNish/KingNish-Llama3-8b
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parameters:
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density: 0.55
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weight: 0.05
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merge_method: dare_ties
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base_model: NousResearch/Meta-Llama-3-8B
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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 = "mlabonne/Daredevil-8B"
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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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