praxisresearch/hf_seed_36b_medcorr_3
09
<!-- 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: 0.16.2.dev0
adapter: lora
bf16: auto
message_field_role: role
path: data/finetuning/correct/health_correct_simplified.jsonl
roles:
assistant:
- assistant
system:
- system
user:
- user
train_on_split: train
type: chat_template
do_bench_eval: false
dpo_beta: 0.1
eval_batch_size: null
eval_sample_packing: false
eval_steps: null
flash_attention: true
fp16: false
gradient_accumulation_steps: 8
gradient_checkpointing: true
gradient_checkpointing_kwargs:
use_reentrant: false
group_by_length: false
hub_model_id: praxisresearch/hf_seed_36b_medcorr_3
hub_strategy: every_save
learning_rate: 1.0e-05
logging_steps: 1
lora_alpha: 64
lora_dropout: 0.0
lora_fan_in_fan_out: false
lora_model_dir: null
lora_r: 32
lora_target_modules:
- q_proj
- k_proj
- v_proj
- o_proj
- gate_proj
- up_proj
- down_proj
lr_scheduler: linear
micro_batch_size: 2
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_8bit
output_dir: models/hf_seed_36b_medcorr_3
pad_to_sequence_len: false
peft_use_dora: false
peft_use_rslora: true
push_to_hub: true
save_safetensors: true
saves_per_epoch: 1
seed: 3
sequence_len: 2048
special_tokens: null
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
val_set_size: 0
wandb_entity: tagadearush
wandb_log_model: null
wandb_project: hf_seed_36b_medcorr_3
wandb_run_id: null
wandb_watch: null
warmup_steps: 5
weight_decay: 0.01
</details><br>
hfseed36bmedcorr3
This model is a fine-tuned version of unsloth/Seed-OSS-36B-Instruct on the data/finetuning/correct/healthcorrectsimplified.jsonl 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: 1e-05
- trainbatchsize: 2
- evalbatchsize: 2
- seed: 3
- gradientaccumulationsteps: 8
- totaltrainbatch_size: 16
- optimizer: Use OptimizerNames.ADAMW8BIT with betas=(0.9,0.999) and epsilon=1e-08 and optimizerargs=No additional optimizer arguments
- lrschedulertype: linear
- lrschedulerwarmup_steps: 5
- training_steps: 125
Training results
Framework versions
- PEFT 0.19.1
- Transformers 5.5.4
- Pytorch 2.10.0+cu128
- Datasets 4.8.5
- Tokenizers 0.22.2
