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  ---
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  library_name: transformers
 
 
 
 
 
 
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  tags:
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  - unsloth
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  - trl
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  - sft
 
 
 
 
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  ---
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- # Model Card for Model ID
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-
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- <!-- Provide a quick summary of what the model is/does. -->
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-
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- ## Model Details
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-
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- ### Model Description
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-
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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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-
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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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-
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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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-
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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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- [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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- #### 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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- ## 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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+ language:
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+ - en
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+ base_model:
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+ - RozGrov/NemoDori-v0.2-12B-MN-BT
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+ datasets:
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+ - Inv/c2-logs-cleaned-deslopped
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  tags:
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  - unsloth
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  - trl
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  - sft
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+ - merge
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+ - mergekit
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+ - lazymergekit
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+ - RozGrov/NemoDori-v0.2-12B-MN-BT
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  ---
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+ # NemoDori-v0.2-Frankend.2-v1-16.6B
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+
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+ _Experimental!_
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+ A more upscaled version of [**NemoDori-v0.2-12B-MN-BT**](https://huggingface.co/RozGrov/NemoDori-v0.2-12B-MN-BT), now at **16.6B**.
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+ <br>
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+ This is also my first successful(?) fine-tuned model using **500 random rows** from dataset
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+ [Inv/c2-logs-cleaned-deslopped](https://huggingface.co/datasets/Inv/c2-logs-cleaned-deslopped) in 70 steps.
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+ The reason I used that dataset is... just for testing. What I thought is, if I can replace/fill up those duplicated layers by training it, maybe that makes it better.
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+ NemoDori v0.2 is my best merge model so far, but I'm afraid it's still 12B, not much to improve after merging all kinds of models.
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+ <br>
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+ Again, I'm just interested to play with these LLM stuff for awhile. Maybe more version of this will come out later.
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+ As far from my short testing, this model has become a little more strict than the parent model (v0.2).I haven't notice anything major yet.
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+ <br>
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+ You can use ST with this preset [here](https://huggingface.co/RozGrov/NemoDori-v0.2-Frankend.2-v1-16.6B/resolve/main/NemoDori-v0.2-Frankend.2-v1-16.6B%20-%20ST%20Preset.json).
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+ Unfortunately, you can't go wild with this model (from my short tests), sometimes it makes little senses, and sometimes... you will get a reddit link (i'm not kidding).
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+ I didn't have enough time to test it, because it's more pricey without quantization.
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+ <br>
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+ I trust @mradermacher to make the quants version of this model. (Thank you so much for making those GGUF on my models ^_^)
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+ <br>
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+ And yeah... Your feedbacks are always welcome, and let me know what's your experience using this model, that would be appreciated.
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+ Take care everyone.
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+ ### Merge Method
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+ This model was merged from the following models using the `passthrough` merge method:
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+ * [RozGrov/NemoDori-v0.2-12B-MN-BT](https://huggingface.co/RozGrov/NemoDori-v0.2-12B-MN-BT)
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+
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+ ## 🧩 Configuration
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+
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+ ```yaml
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+ slices:
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+ - sources:
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+ - model: RozGrov/NemoDori-v0.2-12B-MN-BT
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+ layer_range: [0, 8]
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+ - sources:
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+ - model: RozGrov/NemoDori-v0.2-12B-MN-BT
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+ layer_range: [8, 24]
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+ parameters:
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+ scale:
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+ - filter: q_proj
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+ value: 0.919
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+ - filter: k_proj
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+ value: 0.919
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+ - value: 1.0
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+ - sources:
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+ - model: RozGrov/NemoDori-v0.2-12B-MN-BT
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+ layer_range: [16, 24]
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+ parameters:
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+ scale:
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+ - filter: q_proj
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+ value: 0.7
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+ - filter: k_proj
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+ value: 0.7
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+ - filter: o_proj
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+ value: 0.0
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+ - filter: down_proj
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+ value: 0.0
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+ - value: 1.0
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+ - sources:
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+ - model: RozGrov/NemoDori-v0.2-12B-MN-BT
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+ layer_range: [16, 32]
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+ parameters:
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+ scale:
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+ - filter: q_proj
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+ value: 0.919
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+ - filter: k_proj
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+ value: 0.919
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+ - value: 1.0
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+ - sources:
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+ - model: RozGrov/NemoDori-v0.2-12B-MN-BT
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+ layer_range: [32, 40]
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+ merge_method: passthrough
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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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+
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+ model = "RozGrov/NemoDori-v0.2-Frankend.2-pre"
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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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+ ```