QuantFactory/Llama-3-8B-OpenHermes-2.5-1M-GGUF
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---
license: llama3
base_model: Magpie-Align/Llama-3-8B-OpenHermes-2.5-1M
tags:
- axolotl
- generated_from_trainer
model-index:
- name: Llama-3-8B-OpenHermes-2.5-1M
results: []
pipeline_tag: text-generation
---
# QuantFactory/Llama-3-8B-OpenHermes-2.5-1M-GGUF
This is quantized version of [Magpie-Align/Llama-3-8B-OpenHermes-2.5-1M](https://huggingface.co/Magpie-Align/Llama-3-8B-OpenHermes-2.5-1M) created using llama.cpp
# Model Description
[<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: `0.4.0`
```yaml
base_model: meta-llama/Meta-Llama-3-8B
model_type: LlamaForCausalLM
tokenizer_type: AutoTokenizer
load_in_8bit: false
load_in_4bit: false
strict: false
datasets:
- path: teknium/OpenHermes-2.5
type: sharegpt
conversation: llama3
dataset_prepared_path: last_run_prepared
val_set_size: 0.001
output_dir: ./out_Llama-8B-Openhermes-2.5
sequence_len: 8192
sample_packing: true
eval_sample_packing: false
pad_to_sequence_len: true
wandb_project: SynDa
wandb_entity:
wandb_watch:
wandb_name: Llama-3-8B-OpenHermes-2.5-1M
wandb_log_model:
hub_model_id: Magpie-Align/Llama-3-8B-OpenHermes-2.5-1M
gradient_accumulation_steps: 8
micro_batch_size: 1
num_epochs: 2
optimizer: paged_adamw_8bit
lr_scheduler: cosine
learning_rate: 2e-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: 100
evals_per_epoch: 3
eval_table_size:
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
pad_token: <|end_of_text|>
```
</details><br>
# Llama-3-8B-OpenHermes-2.5-1M
This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6993
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- 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: 100
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.9499 | 0.0007 | 1 | 0.9305 |
| 0.6229 | 0.3337 | 488 | 0.7164 |
| 0.6231 | 0.6674 | 976 | 0.7045 |
| 0.5959 | 1.0011 | 1464 | 0.6959 |
| 0.4997 | 1.3181 | 1952 | 0.7003 |
| 0.529 | 1.6518 | 2440 | 0.6993 |
### Framework versions
- Transformers 4.40.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1

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{"framework": "pytorch", "task": "text-generation", "allow_remote": true}