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Model: ruslandev/llama-3-8b-gpt-4o-ru1.0-gguf
Source: Original Platform

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README.md

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+---
+license: llama3
+base_model: meta-llama/Meta-Llama-3-8B-Instruct
+tags:
+- generated_from_trainer
+model-index:
+- name: >-
+    home/ubuntu/llm_training/axolotl/llama3-8b-gpt-4o-ru/output_llama3_8b_gpt_4o_ru
+  results: []
+datasets:
+- ruslandev/tagengo-rus-gpt-4o
+---
+
+# Llama-3 8B GPT-4o-RU1.0
+
+[[Dataset]](https://huggingface.co/datasets/ruslandev/tagengo-rus-gpt-4o)
+
+This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct).
+The idea behind this model is to train on a dataset derived from a smaller subset of the [tagengo-gpt4](https://huggingface.co/datasets/lightblue/tagengo-gpt4), but with improved data quality.
+I tried to achieve higher data quality by prompting GPT-4o, the latest OpenAI's LLM with better multilingual capabilities. The training objective is primarily focused on the Russian language (80% of the training examples).
+After training for 1 epoch on 2 NVIDIA A100 the model shows promising results on the MT-Bench evaluation benchmark, surpassing GPT-3.5-turbo and being on par with [Suzume](https://huggingface.co/lightblue/suzume-llama-3-8B-multilingual) in Russian language scores, 
+even though the latter is trained on 8x bigger and more diverse dataset. 
+
+## How to use
+
+The easiest way to use this model on your own computer is to use the GGUF version of this model ([ruslandev/llama-3-8b-gpt-4o-ru1.0-gguf](https://huggingface.co/ruslandev/llama-3-8b-gpt-4o-ru1.0-gguf)) using a program such as [llama.cpp](https://github.com/ggerganov/llama.cpp).
+If you want to use this model directly with the Huggingface Transformers stack, I recommend using my framework [gptchain](https://github.com/RuslanPeresy/gptchain).
+
+```
+git clone https://github.com/RuslanPeresy/gptchain.git
+cd gptchain
+pip install -r requirements-train.txt
+python gptchain.py chat -m ruslandev/llama-3-8b-gpt-4o-ru1.0 \
+	--chatml true \
+	-q '[{"from": "human", "value": "Из чего состоит нейронная сеть?"}]'
+```
+
+## Evaluation scores
+
+I achieved the following scores on Ru/En MT-Bench:
+|            |meta-llama/Meta-Llama-3-8B-Instruct | ruslandev/llama-3-8b-gpt-4o-ru1.0 | lightblue/suzume-llama-3-8B-multilingual | Nexusflow/Starling-LM-7B-beta | gpt-3.5-turbo |
+|:----------:|:----------------------------------:|:---------------------------------:|:----------------------------------------:|:-----------------------------:|:-------------:|
+| Russian 🇷🇺 | NaN                                | 8.12                              | 8.19                                     | 8.06                          | 7.94          |
+| English 🇺🇸 | 7.98                               | 8.01                              | 7.73                                     | 7.92                          | 8.26          |
+
+## Training procedure
+
+[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
+<details><summary>See axolotl config</summary>
+
+axolotl version: `0.4.1`
+```yaml
+base_model: meta-llama/Meta-Llama-3-8B-Instruct
+model_type: LlamaForCausalLM
+tokenizer_type: AutoTokenizer  # PreTrainedTokenizerFast
+
+load_in_8bit: false
+load_in_4bit: false
+strict: false
+
+datasets:
+  - path: ruslandev/tagengo-rus-gpt-4o
+    type: sharegpt
+    conversation: llama-3
+dataset_prepared_path: /home/ubuntu/llm_training/axolotl/llama3-8b-gpt-4o-ru/prepared_tagengo_rus
+val_set_size: 0.01
+output_dir: /home/ubuntu/llm_training/axolotl/llama3-8b-gpt-4o-ru/output_llama3_8b_gpt_4o_ru
+
+sequence_len: 8192
+sample_packing: true
+pad_to_sequence_len: true
+eval_sample_packing: false
+
+use_wandb: false
+#wandb_project: axolotl
+#wandb_entity: wandb_entity
+#wandb_name: llama_3_8b_gpt_4o_ru
+
+gradient_accumulation_steps: 2
+micro_batch_size: 2
+num_epochs: 1
+optimizer: paged_adamw_8bit
+lr_scheduler: cosine
+learning_rate: 1e-5
+
+train_on_inputs: false
+group_by_length: false
+bf16: auto
+fp16:
+tf32: false
+
+gradient_checkpointing: true
+gradient_checkpointing_kwargs:
+  use_reentrant: false
+early_stopping_patience:
+resume_from_checkpoint:
+logging_steps: 1
+xformers_attention:
+flash_attention: true
+
+warmup_steps: 10
+evals_per_epoch: 5
+eval_table_size:
+saves_per_epoch: 1
+debug:
+deepspeed: /home/ubuntu/axolotl/deepspeed_configs/zero2.json
+weight_decay: 0.0
+special_tokens:
+  pad_token: <|end_of_text|>
+
+```
+
+</details><br>
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 1e-05
+- train_batch_size: 2
+- eval_batch_size: 2
+- seed: 42
+- distributed_type: multi-GPU
+- num_devices: 2
+- gradient_accumulation_steps: 2
+- total_train_batch_size: 8
+- total_eval_batch_size: 4
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: cosine
+- lr_scheduler_warmup_steps: 10
+- num_epochs: 1
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss |
+|:-------------:|:-----:|:----:|:---------------:|
+| 1.1347        | 0.016 | 1    | 1.1086          |
+| 0.916         | 0.208 | 13   | 0.8883          |
+| 0.8494        | 0.416 | 26   | 0.8072          |
+| 0.8657        | 0.624 | 39   | 0.7814          |
+| 0.8077        | 0.832 | 52   | 0.7702          |
+
+
+### Framework versions
+
+- Transformers 4.41.1
+- Pytorch 2.2.2+cu121
+- Datasets 2.19.1
+- Tokenizers 0.19.1

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