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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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<!-- 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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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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_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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## 🧩 Configuration
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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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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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```
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