Instructions to use TOKETTER/Omegus with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use TOKETTER/Omegus with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("HuggingFaceTB/SmolLM2-135M-Instruct") model = PeftModel.from_pretrained(base_model, "TOKETTER/Omegus") - Notebooks
- Google Colab
- Kaggle
Publish Omegus LoRA adapter
Browse files- adapter_config.json +45 -0
- adapter_model.safetensors +3 -0
- chat_template.jinja +6 -0
- tokenizer.json +0 -0
- tokenizer_config.json +19 -0
- trainer_state.json +65 -0
adapter_config.json
ADDED
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{
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"alora_invocation_tokens": null,
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"alpha_pattern": {},
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"arrow_config": null,
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"auto_mapping": null,
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"base_model_name_or_path": "HuggingFaceTB/SmolLM2-135M-Instruct",
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"bias": "none",
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"corda_config": null,
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| 9 |
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"ensure_weight_tying": false,
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"eva_config": null,
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"exclude_modules": null,
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"fan_in_fan_out": false,
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"inference_mode": true,
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"init_lora_weights": true,
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"layer_replication": null,
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"layers_pattern": null,
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| 17 |
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"layers_to_transform": null,
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| 18 |
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"loftq_config": {},
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| 19 |
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"lora_alpha": 32,
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"lora_bias": false,
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"lora_dropout": 0.05,
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| 22 |
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"lora_ga_config": null,
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"megatron_config": null,
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"megatron_core": "megatron.core",
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"modules_to_save": null,
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"peft_type": "LORA",
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"peft_version": "0.19.1",
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"qalora_group_size": 16,
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"r": 16,
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"rank_pattern": {},
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"revision": null,
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"target_modules": [
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"v_proj",
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"o_proj",
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"k_proj",
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"q_proj"
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],
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"target_parameters": null,
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"task_type": "CAUSAL_LM",
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| 40 |
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"trainable_token_indices": null,
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| 41 |
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"use_bdlora": null,
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| 42 |
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"use_dora": false,
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| 43 |
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"use_qalora": false,
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"use_rslora": false
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}
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adapter_model.safetensors
ADDED
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version https://git-lfs.github.com/spec/v1
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oid sha256:8f8531b4c67e32a0e39e91af01ef35e45b4f9bf060dcd0d203d4efe66bfa186d
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size 7404368
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chat_template.jinja
ADDED
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{% for message in messages %}{% if loop.first and messages[0]['role'] != 'system' %}{{ '<|im_start|>system
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You are a helpful AI assistant named SmolLM, trained by Hugging Face<|im_end|>
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' }}{% endif %}{{'<|im_start|>' + message['role'] + '
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' + message['content'] + '<|im_end|>' + '
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'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant
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' }}{% endif %}
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tokenizer.json
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The diff for this file is too large to render.
See raw diff
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tokenizer_config.json
ADDED
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{
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"add_prefix_space": false,
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"backend": "tokenizers",
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"bos_token": "<|im_start|>",
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"clean_up_tokenization_spaces": false,
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"eos_token": "<|im_end|>",
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"errors": "replace",
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"extra_special_tokens": [
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"<|im_start|>",
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"<|im_end|>"
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],
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"is_local": false,
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"local_files_only": false,
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"model_max_length": 8192,
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"pad_token": "<|im_end|>",
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"tokenizer_class": "GPT2Tokenizer",
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"unk_token": "<|endoftext|>",
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"vocab_size": 49152
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}
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trainer_state.json
ADDED
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{
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| 2 |
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"best_global_step": null,
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| 3 |
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"best_metric": null,
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| 4 |
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"best_model_checkpoint": null,
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| 5 |
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"epoch": 1.0,
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| 6 |
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"eval_steps": 500,
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| 7 |
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"global_step": 5,
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| 8 |
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"is_hyper_param_search": false,
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| 9 |
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"is_local_process_zero": true,
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| 10 |
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"is_world_process_zero": true,
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| 11 |
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"log_history": [
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| 12 |
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{
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| 13 |
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"entropy": 2.919967442750931,
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| 14 |
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"epoch": 0.47058823529411764,
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| 15 |
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"grad_norm": 0.7388926148414612,
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| 16 |
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"learning_rate": 5e-05,
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| 17 |
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"loss": 3.528480052947998,
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| 18 |
+
"mean_token_accuracy": 0.39445167779922485,
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| 19 |
+
"num_tokens": 1330.0,
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| 20 |
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"step": 2
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| 21 |
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},
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| 22 |
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{
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| 23 |
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"entropy": 2.921311140060425,
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| 24 |
+
"epoch": 0.9411764705882353,
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| 25 |
+
"grad_norm": 0.7427535653114319,
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| 26 |
+
"learning_rate": 2.5e-05,
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| 27 |
+
"loss": 3.505293607711792,
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| 28 |
+
"mean_token_accuracy": 0.3824109882116318,
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| 29 |
+
"num_tokens": 2602.0,
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| 30 |
+
"step": 4
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| 31 |
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},
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| 32 |
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{
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| 33 |
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"epoch": 1.0,
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| 34 |
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"eval_entropy": 2.845902681350708,
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| 35 |
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"eval_loss": 3.5877914428710938,
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| 36 |
+
"eval_mean_token_accuracy": 0.38056680560112,
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| 37 |
+
"eval_num_tokens": 2814.0,
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| 38 |
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"eval_runtime": 0.8041,
|
| 39 |
+
"eval_samples_per_second": 3.731,
|
| 40 |
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"eval_steps_per_second": 1.244,
|
| 41 |
+
"step": 5
|
| 42 |
+
}
|
| 43 |
+
],
|
| 44 |
+
"logging_steps": 2,
|
| 45 |
+
"max_steps": 5,
|
| 46 |
+
"num_input_tokens_seen": 0,
|
| 47 |
+
"num_train_epochs": 1,
|
| 48 |
+
"save_steps": 500,
|
| 49 |
+
"stateful_callbacks": {
|
| 50 |
+
"TrainerControl": {
|
| 51 |
+
"args": {
|
| 52 |
+
"should_epoch_stop": false,
|
| 53 |
+
"should_evaluate": false,
|
| 54 |
+
"should_log": false,
|
| 55 |
+
"should_save": true,
|
| 56 |
+
"should_training_stop": true
|
| 57 |
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},
|
| 58 |
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"attributes": {}
|
| 59 |
+
}
|
| 60 |
+
},
|
| 61 |
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"total_flos": 1824259739904.0,
|
| 62 |
+
"train_batch_size": 1,
|
| 63 |
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"trial_name": null,
|
| 64 |
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"trial_params": null
|
| 65 |
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}
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