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  library_name: transformers
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- tags: []
 
 
 
 
 
 
 
 
 
 
 
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  ---
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- # Model Card for Model ID
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- <!-- Provide a quick summary of what the model is/does. -->
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- ## Model Details
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- ### Model Description
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- <!-- Provide a longer summary of what this model is. -->
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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 [optional]:** [More Information Needed]
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- ### Model Sources [optional]
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- <!-- Provide the basic links for the model. -->
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- - **Repository:** [More Information Needed]
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- - **Paper [optional]:** [More Information Needed]
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- - **Demo [optional]:** [More Information Needed]
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- ## Uses
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- <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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- ### Direct Use
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- <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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- [More Information Needed]
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- ### Downstream Use [optional]
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- <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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- [More Information Needed]
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- ### Out-of-Scope Use
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- <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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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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- <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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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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- ## 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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- [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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- ## 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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- #### Metrics
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- <!-- These are the evaluation metrics being used, ideally with a description of why. -->
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- ### Results
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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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- ### Compute Infrastructure
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- #### Hardware
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- #### Software
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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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- **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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- ## Model Card Authors [optional]
 
 
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- ## Model Card Contact
 
 
 
 
 
 
 
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- [More Information Needed]
 
 
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  ---
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  library_name: transformers
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+ tags:
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+ - llama2
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+ - deutsch
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+ - german
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+ - seedbox
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+ license: llama2
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+ datasets:
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+ - seedboxai/multitask_german_examples_32k
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+ language:
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+ - de
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+ - en
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+ pipeline_tag: text-generation
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  ---
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+ ![image/png](https://cdn-uploads.huggingface.co/production/uploads/645ded34a45b4182d7f5c385/Lu_-yOozdIQLBe4FrmWUI.png)
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+ # KafkaLM-7B-DARE_TIES-LaserRMT-QLoRA-DPO-v0.5
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+ **KafkaLM 7b** is a Mistral 7b model - further pre-trained on a large German dataset from Björn Plüster and LAION. [leo-mistral-hessianai-7b](https://huggingface.co/LeoLM/leo-mistral-hessianai-7b) - which was finetuned on an ensemble of popular high-quality open-source instruction sets (translated from English to German).
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+ KafkaLM 7b is a [Seedbox](https://huggingface.co/seedboxai) project trained by [Dennis Dickmann](https://huggingface.co/doubledsbv).
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ **Why Kafka?**
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+ The models are proficient, yet creative, and have some tendencies to linguistically push boundaries 😊
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+ ## Model Details
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+ The purpose of releasing the **KafkaLM series** is to contribute to the German AI community with a set of fine-tuned LLMs that are easy to use in everyday applications across a variety of tasks.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ The main goal was to provide LLMs proficient in German, especially to be used in German-speaking business contexts where English alone is not sufficient.
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+ ## LaerRMT w/ Q-Lora
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+ Based on the brilliant work from [laserRMT](https://github.com/cognitivecomputations/laserRMT/) team, I used the SNR implementation for identifying candiate layers to be used for the DPO training.
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+ ### Dataset
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+ I used a 8k filtered version of the following [seedboxai/multitask_german_examples_32k](https://huggingface.co/datasets/seedboxai/multitask_german_examples_32k)
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+ ### Prompt Format
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+ This model follows the subsequent prompt format:
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+ ```
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+ <|system|>
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+ Du bist ein freundlicher und hilfsbereiter KI-Assistent. Du beantwortest Fragen faktenorientiert und präzise, ohne dabei relevante Fakten auszulassen.</s>
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+ <|user|>
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+ Welche Möglichkeiten der energetischen Sanierung habe ich neben Solar und Energiespeicher?</s>
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+ <|assistant|>
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+ ```
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+ ```
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+ ## 🧩 Configuration
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+ ```yaml
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+ models:
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+ - model: mistralai/Mistral-7B-v0.1
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+ # no parameters necessary for base model
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+ - model: seedboxai/KafkaLM-7B-German-V0.1
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+ parameters:
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+ density: 0.65
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+ weight: 0.50
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+ - model: mlabonne/Monarch-7B
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+ parameters:
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+ density: 0.60
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+ weight: 0.30
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+ - model: mayflowergmbh/Wiedervereinigung-7b-dpo-laser
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+ parameters:
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+ density: 0.60
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+ weight: 0.20
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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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+ random_seed: 0
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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 = "seedboxai/KafkaLM-7B-DARE_TIES-LaserRMT-QLoRA-DPO-v0.5"
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+ messages = [{"role": "user", "content": "Was ist der Sinn des Lebens?"}]
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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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+ ## Disclaimer
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+ The license on this model does not constitute legal advice. We are not responsible for the actions of third parties who use this model.
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+ This model should only be used for research purposes. The original Llama2 license and all restrictions of datasets used to train this model apply.