Primeness_-_primelive3-awq

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primelive3 - AWQ

• Model creator: https://huggingface.co/Primeness/
• Original model: https://huggingface.co/Primeness/primelive3/

Original model description:

license: other base_model: Qwen/Qwen1.5-0.5B tags:

• generated_from_trainer model-index:
• name: outputs/out 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/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>

<details><summary>See axolotl config</summary>

axolotl version: 0.4.0

base_model: Qwen/Qwen1.5-0.5B
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
trust_remote_code: true


load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: silk-road/ChatHaruhi-RolePlaying
    type: "completion"
dataset_prepared_path: 
val_set_size: 0.00
output_dir: ./outputs/out

sequence_len: 250
sample_packing: true
pad_to_sequence_len: true
save_safetensors: true
gpu_memory_limit: 80GiB

adapter:
lora_model_dir:
lora_r:
lora_alpha:
lora_dropout:
lora_target_linear:
lora_fan_in_fan_out:

wandb_project:
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:

gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 1
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.00005

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: false   #toggle

warmup_steps: 100
evals_per_epoch: 4
eval_table_size:
saves_per_epoch: 1
debug:
deepspeed: #deepspeed_configs/zero2.json # multi-gpu only
weight_decay: 0.01
fsdp:
fsdp_config:
special_tokens:

</details><br>

outputs/out

This model is a fine-tuned version of Qwen/Qwen1.5-0.5B 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: 5e-05
• train_batch_size: 4
• eval_batch_size: 4
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: cosine
• lr_scheduler_warmup_steps: 100
• num_epochs: 1

Training results

Framework versions

• Transformers 4.40.2
• Pytorch 2.3.0
• Datasets 2.19.1
• Tokenizers 0.19.1