End of training
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README.md
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base_model: mistralai/Mistral-Small-24B-Instruct-2501
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library_name: peft
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---
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<!-- Provide a quick summary of what the model is/does. -->
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## Model Details
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### Model Description
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<!-- Provide a longer summary of what this model is. -->
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- **Developed by:** [More Information Needed]
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- **Funded by [optional]:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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- **Model type:** [More Information Needed]
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- **Language(s) (NLP):** [More Information Needed]
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- **License:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
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### Model Sources [optional]
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<!-- Provide the basic links for the model. -->
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- **Repository:** [More Information Needed]
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- **Paper [optional]:** [More Information Needed]
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- **Demo [optional]:** [More Information Needed]
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## Uses
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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### Direct Use
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<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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[More Information Needed]
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### Downstream Use [optional]
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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[More Information Needed]
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### Out-of-Scope Use
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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[More Information Needed]
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## Bias, Risks, and Limitations
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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[More Information Needed]
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### Recommendations
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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## How to Get Started with the Model
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Use the code below to get started with the model.
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[More Information Needed]
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## Training Details
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### Training Data
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<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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[More Information Needed]
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### Training Procedure
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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#### Preprocessing [optional]
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[More Information Needed]
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#### Training Hyperparameters
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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#### Speeds, Sizes, Times [optional]
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## Model Card Contact
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[More Information Needed]
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### Framework versions
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- PEFT 0.14.0
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---
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library_name: peft
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license: apache-2.0
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base_model: mistralai/Mistral-Small-24B-Instruct-2501
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tags:
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- axolotl
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- generated_from_trainer
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datasets:
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- ToastyPigeon/some-rp-extended
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model-index:
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- name: new-ms-rp-test-ws
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
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<details><summary>See axolotl config</summary>
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axolotl version: `0.6.0`
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```yaml
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# git clone https://github.com/axolotl-ai-cloud/axolotl
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# cd axolotl
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# git checkout 844331005c1ef45430ff26b9f42f757dce6ee66a
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# pip3 install packaging ninja huggingface_hub[cli]
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# pip3 install -e '.[flash-attn,deepspeed]'
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# huggingface-cli login --token $hf_key && wandb login $wandb_key
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# python -m axolotl.cli.preprocess new-ms-rp-only-test.yml
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# accelerate launch -m axolotl.cli.train new-ms-rp-only-test.yml
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# python -m axolotl.cli.merge_lora new-ms-rp-only-test.yml --lora_on_cpu
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# huggingface-cli upload ToastyPigeon/ms-rp-only-qlora-TEST train-workspace/merged . --exclude "*.md"
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# git clone https://github.com/axolotl-ai-cloud/axolotl && cd axolotl && pip3 install packaging ninja huggingface_hub[cli] && pip3 install -e '.[flash-attn,deepspeed]' && cd .. && huggingface-cli login --token $hf_key && wandb login $wandb_key
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# Model
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base_model: mistralai/Mistral-Small-24B-Instruct-2501
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model_type: AutoModelForCausalLM
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tokenizer_type: AutoTokenizer
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load_in_8bit: false
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load_in_4bit: true
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strict: false
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bf16: auto
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fp16:
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tf32: false
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flash_attention: true
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special_tokens:
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# Output
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output_dir: ./train-workspace
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hub_model_id: ToastyPigeon/new-ms-rp-test-ws
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hub_strategy: "checkpoint"
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resume_from_checkpoint:
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saves_per_epoch: 1
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# Data
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sequence_len: 4096 # fits
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min_sample_len: 128
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dataset_prepared_path: last_run_prepared
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datasets:
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- path: ToastyPigeon/some-rp-extended
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type: chat_template
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field_messages: conversations
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message_field_role: from
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message_field_content: value
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warmup_steps: 10
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shuffle_merged_datasets: true
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sample_packing: true
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pad_to_sequence_len: true
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chat_template: jinja
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chat_template_jinja: "{%- set default_system_message = \"You are an unbiased, unfiltered, and unaligned AI.\" %}\n\n{{- bos_token }}\n\n{%- if messages[0]['role'] == 'system' %}\n {%- set system_message = messages[0]['content'] %}\n {%- set loop_messages = messages[1:] %}\n{%- else %}\n {%- set system_message = default_system_message %}\n {%- set loop_messages = messages %}\n{%- endif %}\n{{- '[SYSTEM_PROMPT]' + system_message + '[/SYSTEM_PROMPT]' }}\n\n{%- for message in loop_messages %}\n {%- if message['role'] == 'user' %}\n {{- '[INST]' + message['content'] + '[/INST]' }}\n {%- elif message['role'] == 'system' %}\n {{- '[SYSTEM_PROMPT]' + message['content'] + '[/SYSTEM_PROMPT]' }}\n {%- elif message['role'] == 'assistant' %}\n {{- message['content'] + eos_token }}\n {%- else %}\n {{- raise_exception('Only user, system and assistant roles are supported!') }}\n {%- endif %}\n{%- endfor %}"
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# Batching
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num_epochs: 1
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gradient_accumulation_steps: 4
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micro_batch_size: 1
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eval_batch_size: 1
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# Evaluation
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val_set_size: 40
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evals_per_epoch: 5
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eval_table_size:
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eval_max_new_tokens: 256
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eval_sample_packing: false
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save_safetensors: true
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# WandB
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wandb_project: MS-Rp-Test
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#wandb_entity:
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gradient_checkpointing: 'unsloth'
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#gradient_checkpointing_kwargs:
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# use_reentrant: false
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unsloth_cross_entropy_loss: true
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#unsloth_lora_mlp: true
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#unsloth_lora_qkv: true
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#unsloth_lora_o: true
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# LoRA
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adapter: qlora
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lora_model_dir:
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lora_r: 32
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lora_alpha: 64
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lora_dropout: 0.25
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lora_target_linear:
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lora_fan_in_fan_out:
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lora_target_modules:
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- gate_proj
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- down_proj
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- up_proj
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- q_proj
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- v_proj
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- k_proj
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- o_proj
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lora_modules_to_save:
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# Optimizer
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optimizer: paged_ademamix_8bit # adamw_8bit
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lr_scheduler: cosine
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learning_rate: 5e-5
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cosine_min_lr_ratio: 0.5
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weight_decay: 0.01
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max_grad_norm: 1.0
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# Misc
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train_on_inputs: false
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group_by_length: false
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early_stopping_patience:
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local_rank:
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logging_steps: 1
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xformers_attention:
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debug:
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#deepspeed: /workspace/axolotl/deepspeed_configs/zero3_bf16.json # previously blank
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fsdp:
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fsdp_config:
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plugins:
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- axolotl.integrations.liger.LigerPlugin
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# - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
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#cut_cross_entropy: true
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liger_rope: true
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liger_rms_norm: true
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liger_layer_norm: true
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liger_glu_activation: true
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#liger_fused_linear_cross_entropy: true
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+
gc_steps: 10
|
| 151 |
+
seed: 69
|
| 152 |
+
```
|
| 153 |
+
|
| 154 |
+
</details><br>
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| 155 |
+
|
| 156 |
+
# new-ms-rp-test-ws
|
| 157 |
+
|
| 158 |
+
This model is a fine-tuned version of [mistralai/Mistral-Small-24B-Instruct-2501](https://huggingface.co/mistralai/Mistral-Small-24B-Instruct-2501) on the ToastyPigeon/some-rp-extended dataset.
|
| 159 |
+
It achieves the following results on the evaluation set:
|
| 160 |
+
- Loss: 2.1127
|
| 161 |
+
|
| 162 |
+
## Model description
|
| 163 |
+
|
| 164 |
+
More information needed
|
| 165 |
+
|
| 166 |
+
## Intended uses & limitations
|
| 167 |
+
|
| 168 |
+
More information needed
|
| 169 |
+
|
| 170 |
+
## Training and evaluation data
|
| 171 |
+
|
| 172 |
+
More information needed
|
| 173 |
+
|
| 174 |
+
## Training procedure
|
| 175 |
+
|
| 176 |
+
### Training hyperparameters
|
| 177 |
+
|
| 178 |
+
The following hyperparameters were used during training:
|
| 179 |
+
- learning_rate: 5e-05
|
| 180 |
+
- train_batch_size: 1
|
| 181 |
+
- eval_batch_size: 1
|
| 182 |
+
- seed: 69
|
| 183 |
+
- gradient_accumulation_steps: 4
|
| 184 |
+
- total_train_batch_size: 4
|
| 185 |
+
- optimizer: Use OptimizerNames.PAGED_ADEMAMIX_8BIT and the args are:
|
| 186 |
+
No additional optimizer arguments
|
| 187 |
+
- lr_scheduler_type: cosine
|
| 188 |
+
- lr_scheduler_warmup_steps: 10
|
| 189 |
+
- num_epochs: 1.0
|
| 190 |
+
|
| 191 |
+
### Training results
|
| 192 |
+
|
| 193 |
+
| Training Loss | Epoch | Step | Validation Loss |
|
| 194 |
+
|:-------------:|:------:|:----:|:---------------:|
|
| 195 |
+
| 2.4594 | 0.0078 | 1 | 2.2498 |
|
| 196 |
+
| 2.1355 | 0.2031 | 26 | 2.1281 |
|
| 197 |
+
| 2.1069 | 0.4062 | 52 | 2.1199 |
|
| 198 |
+
| 1.8512 | 0.6094 | 78 | 2.1148 |
|
| 199 |
+
| 2.0247 | 0.8125 | 104 | 2.1127 |
|
| 200 |
|
|
|
|
| 201 |
|
|
|
|
| 202 |
### Framework versions
|
| 203 |
|
| 204 |
+
- PEFT 0.14.0
|
| 205 |
+
- Transformers 4.48.1
|
| 206 |
+
- Pytorch 2.5.1+cu124
|
| 207 |
+
- Datasets 3.2.0
|
| 208 |
+
- Tokenizers 0.21.0
|