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README.md
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## Model Details
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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- **Developed by:** [More Information Needed]
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- **Funded by [optional]:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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- **Model type:** [More Information Needed]
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- **Language(s) (NLP):** [More Information Needed]
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- **License:** [More Information Needed]
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- **Finetuned from model [
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<!-- Provide the basic links for the model. -->
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[More Information Needed]
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## Bias, Risks, and Limitations
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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[More Information Needed]
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### Recommendations
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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##
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Use the code below to get started with the model.
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[More Information Needed]
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## Training Details
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### Training Data
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<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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[More Information Needed]
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### Training Procedure
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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#### Preprocessing [optional]
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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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[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 Details
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This model is being made to enhance our work within crewai. We started with a high context length (1048K) version of Llama 3. We then fine-tuned on top of that to get a base agent.
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## Model Description
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Built on the following:
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- Models:
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- [Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct)
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- [Llama-3-8B-Instruct-Gradient-1048k](https://huggingface.co/gradientai/Llama-3-8B-Instruct-Gradient-1048k)
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- Datasets:
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- m-a-p/CodeFeedback-Filtered-Instruction
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- RomanTeucher/awesome_topic_code_snippets
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- dair-ai/emotion
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- mzbac/function-calling-llama-3-format-v1.1
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- gretelai/synthetic_text_to_sql
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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- **Developed by:** [More Information Needed]
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- **Model type:** [More Information Needed]
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- **License:** [More Information Needed]
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- **Finetuned from model [Llama-3-8B-Instruct-Gradient-1048k]
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## Model Sources [optional]
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<!-- Provide the basic links for the model. -->
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[More Information Needed]
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### Recommendations
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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## Code Examples
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Use the following format when using the model for inference:
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```
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<|begin_of_text|><|start_header_id|>system<|end_header_id|>
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You are the helpful assistant. <|eot_id|><|start_header_id|>user<|end_header_id|>
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{prompt} <|eot_id|><|start_header_id|>assistant<|end_header_id|>
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```
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Example of multi turn
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```
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<|begin_of_text|><|start_header_id|>system<|end_header_id|>
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You are a helpful AI assistant for travel tips and recommendations<|eot_id|><|start_header_id|>user<|end_header_id|>
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What is France's capital?<|eot_id|><|start_header_id|>assistant<|end_header_id|>
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Bonjour! The capital of France is Paris!<|eot_id|><|start_header_id|>user<|end_header_id|>
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What can I do there?<|eot_id|><|start_header_id|>assistant<|end_header_id|>
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Paris, the City of Light, offers a romantic getaway with must-see attractions like the Eiffel Tower and Louvre Museum, romantic experiences like river cruises and charming neighborhoods, and delicious food and drink options, with helpful tips for making the most of your trip.<|eot_id|><|start_header_id|>user<|end_header_id|>
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Give me a detailed list of the attractions I should visit, and time it takes in each one, to plan my trip accordingly.<|eot_id|><|start_header_id|>assistant<|end_header_id|>
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```
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## How to Get Started with the Model
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Use the code below to get started with the model.
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```
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import transformers
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import torch
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model_id = "meta-llama/Meta-Llama-3-70B-Instruct"
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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.bfloat16},
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device_map="auto",
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)
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messages = [
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{"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
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{"role": "user", "content": "Who are you?"},
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]
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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=256,
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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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print(outputs[0]["generated_text"][len(prompt):])
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```
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