RichardErkhov/Dans-DiscountModels_-_Dans-Instruct-Mix-8b-ChatML-V0.2.0-gguf
Quantization made by Richard Erkhov.
Dans-Instruct-Mix-8b-ChatML-V0.2.0 - GGUF
- Model creator: https://huggingface.co/Dans-DiscountModels/
- Original model: https://huggingface.co/Dans-DiscountModels/Dans-Instruct-Mix-8b-ChatML-V0.2.0/
Original model description: --- libraryname: transformers basemodel: Dans-DiscountModels/Meta-Llama-3.1-8B-ChatML tags:
- axolotl
- generatedfromtrainer model-index:
- name: Dans-L3.1-Test results: [] ---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. -->
<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/> <details><summary>See axolotl config</summary>
axolotl version: 0.4.1
base_model: Dans-DiscountModels/Meta-Llama-3.1-8B-ChatML
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
trust_remote_code:
# wandb configuration
wandb_project: l3.1-8b-dans-instruct
wandb_watch:
wandb_run_id: attempt-03
wandb_log_model:
# push checkpoints to hub
hub_model_id: anthracite-core/Dans-L3.1-Test
# how to push checkpoints to hub
# https://huggingface.co/docs/transformers/v4.31.0/en/main_classes/trainer#transformers.TrainingArguments.hub_strategy
hub_strategy: "all_checkpoints"
# Whether to use hf `use_auth_token` for loading datasets. Useful for fetching private datasets
# Required to be true when used in combination with `push_dataset_to_hub`
hf_use_auth_token: true
# where to save the finished model to
output_dir: ./l3.1-8b-dans-instruct
# dataset settings (local or huggingface repo)
datasets:
- path: PocketDoc/Dans-MemoryCore-CoreCurriculum-Small
type: dan-chat
- path: AquaV/Energetic-Materials-Sharegpt
type: dan-chat
- path: AquaV/Chemical-Biological-Safety-Applications-Sharegpt
type: dan-chat
- path: AquaV/US-Army-Survival-Sharegpt
type: dan-chat
- path: AquaV/Resistance-Sharegpt
type: dan-chat
- path: AquaV/Interrogation-Sharegpt
type: dan-chat
- path: AquaV/Multi-Environment-Operations-Sharegpt
type: dan-chat
- path: PocketDoc/Dans-Mathmaxx
type: dan-chat
- path: PJMixers/Math-Multiturn-1K-ShareGPT
type: dan-chat
- path: PocketDoc/Dans-Benchmaxx
type: dan-chat
- path: PocketDoc/Dans-Codemaxx-LeetCode
type: dan-chat
- path: PocketDoc/Dans-Codemaxx-CodeFeedback-Conversations
type: dan-chat
- path: PocketDoc/Dans-Codemaxx-CodeFeedback-SingleTurn
type: dan-chat
- path: PocketDoc/Dans-Taskmaxx
type: dan-chat
- path: PocketDoc/Dans-Taskmaxx-DataPrepper
type: dan-chat
- path: PocketDoc/Dans-Taskmaxx-ConcurrentQA-Reworked
type: dan-chat
- path: PocketDoc/Dans-Toolmaxx-Agent
type: dan-chat
- path: PocketDoc/Dans-Toolmaxx-ShellCommands
type: dan-chat
- path: PocketDoc/Dans-ASCIIMaxx-Wordart
type: dan-chat
- path: PocketDoc/Dans-Prosemaxx-Gutenberg
type: dan-chat
- path: PocketDoc/Dans-Prosemaxx-Cowriter-XS
type: dan-chat
- path: PocketDoc/Dans-Prosemaxx-Adventure
type: dan-chat
- path: PocketDoc/Dans-Prosemaxx-Opus-Writing
type: dan-chat
- path: PocketDoc/Dans-Assistantmaxx-Sharegpt
type: dan-chat
- path: PocketDoc/Dans-Assistantmaxx-OpenAssistant2
type: dan-chat
- path: PocketDoc/Dans-Assistantmaxx-Opus-instruct-1
type: dan-chat
- path: PocketDoc/Dans-Assistantmaxx-Opus-instruct-2
type: dan-chat
- path: PocketDoc/Dans-Assistantmaxx-Opus-instruct-3
type: dan-chat
- path: PocketDoc/Dans-Assistantmaxx-NoRobots
type: dan-chat
- path: PocketDoc/Dans-Personamaxx
type: dan-chat
- path: PocketDoc/DansTestYard
type: completion
chat_template: chatml
plugins:
- axolotl.integrations.liger.LigerPlugin
liger_rope: true
liger_rms_norm: true
liger_swiglu: true
liger_fused_linear_cross_entropy: true
load_in_8bit: false
load_in_4bit: false
strict: false
dataset_prepared_path: ./l3.1-8b-dans-instruct-data
val_set_size: 0.0
sequence_len: 8192
sample_packing: true
eval_sample_packing: false
pad_to_sequence_len: true
gradient_checkpointing: true
gradient_checkpointing_kwargs:
use_reentrant: false
gradient_accumulation_steps: 2
micro_batch_size: 2
num_epochs: 3
optimizer: adamw_torch
lr_scheduler: cosine
learning_rate: 0.0000015
cosine_min_lr_ratio:
adam_beta1: 0.9
adam_beta2: 0.95
adam_epsilon: 0.00000001
weight_decay: 0.01
max_grad_norm: 20
train_on_inputs: false
group_by_length: true
bf16: true
fp16: false
tf32: false
early_stopping_patience:
resume_from_checkpoint:
auto_resume_from_checkpoints:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
warmup_ratio: 0.1
saves_per_epoch: 2
debug: false
deepspeed: deepspeed_configs/zero2.json
fsdp:
fsdp_config:
special_tokens:
pad_token: <|finetune_right_pad_id|>
eos_token: <|im_end|></details><br>
Dans-L3.1-Test
This model is a fine-tuned version of Dans-DiscountModels/Meta-Llama-3.1-8B-ChatML on the None dataset.
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1.5e-06
- trainbatchsize: 2
- evalbatchsize: 2
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradientaccumulationsteps: 2
- totaltrainbatch_size: 32
- totalevalbatch_size: 16
- optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-08
- lrschedulertype: cosine
- lrschedulerwarmup_steps: 230
- num_epochs: 3
Training results
Framework versions
- Transformers 4.45.0.dev0
- Pytorch 2.3.1+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
