Update README.md

README.md

@@ -51,6 +51,94 @@ This is the model card for EuroLLM-22B-Instruct. You can also check the pre-trai
 - **Language(s) (NLP):** Bulgarian, Croatian, Czech, Danish, Dutch, English, Estonian, Finnish, French, German, Greek, Hungarian, Irish, Italian, Latvian, Lithuanian, Maltese, Polish, Portuguese, Romanian, Slovak, Slovenian, Spanish, Swedish, Arabic, Catalan, Chinese, Galician, Hindi, Japanese, Korean, Norwegian, Russian, Turkish, and Ukrainian. 
 - **License:** Apache License 2.0.
 
+[<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)
+<details><summary>See axolotl config</summary>
+
+axolotl version: `0.12.2`
+```yaml
+auto_resume_from_checkpoints: true
+use_tensorboard: true
+
+base_model: utter-project/EuroLLM-2512
+model_type: AutoModelForCausalLM
+tokenizer_type: AutoTokenizer
+
+load_in_8bit: false
+load_in_4bit: false
+strict: false
+
+dataset_processes: 64
+datasets:
+  - path: utter-project/EuroBlocks-SFT-2512
+    type: chat_template
+    split: train
+    conversation: chatml
+    field_messages: conversations
+    message_field_role: role
+    message_field_content: content
+    roles_to_train: ["assistant"]
+    train_on_eos: all
+
+
+chat_template_jinja: "{% for message in messages %}{% if message['role'] == 'assistant' %}{% set role = 'assistant' %}{% else %}{% set role = message['role'] %}{% endif %}<|im_start|>{{ role }}\n{{ message['content'] | trim }}<|im_end|>\n{% endfor %}{% if add_generation_prompt %}{{'<|im_start|>assistant\n'}}{% endif %}"
+ 
+output_dir: checkpoints
+val_set_size: 0
+
+sequence_len: 32768
+sample_packing: true
+pad_to_sequence_len: true
+
+# sequence_parallel_degree: 4
+# heads_k_stride: 1
+# ring_attn_func:
+
+plugins:
+  - axolotl.integrations.liger.LigerPlugin
+liger_rope: true
+liger_rms_norm: true
+liger_glu_activation: true
+liger_layer_norm: true
+liger_fused_linear_cross_entropy: true
+
+# N_GPUS * GRAD_ACC_STEPS * MICRO_BATCH_SIZE * SEQ_LEN = tokens/step ->
+# Assuming 32 gpus (32 * 2 * 2 * 32k = 4 096 000 tokens/step)
+gradient_accumulation_steps: 2
+micro_batch_size: 2
+
+eval_batch_size: 1
+num_epochs: 5
+optimizer: adamw_torch
+lr_scheduler: cosine
+learning_rate: 1e-5
+
+train_on_inputs: false
+group_by_length: false
+bf16: true
+fp16: false
+tf32: false
+
+gradient_checkpointing: true
+logging_steps: 1
+flash_attention: true
+flash_attn_cross_entropy: false
+flash_attn_rms_norm: false
+flash_attn_fuse_qkv: false
+flash_attn_fuse_mlp: false
+
+warmup_steps: 125
+eval_sample_packing: False
+save_steps: 500
+save_total_limit: 2
+deepspeed: deepspeed_configs/zero3_bf16.json
+weight_decay: 0.01
+
+special_tokens:
+  eos_token: "<|im_end|>"
+
+```
+</details><br>
+
 ## Model Details
 
 The EuroLLM project has the goal of creating a suite of LLMs capable of understanding and generating text in all European Union languages as well as some additional relevant languages.