End of training

README.md

@@ -1,13 +1,247 @@
 ---
-base_model: google/gemma-2-2b-it
 library_name: peft
-pipeline_tag: text-generation
+license: gemma
+base_model: google/gemma-2-2b-it
 tags:
 - axolotl
 - base_model:adapter:google/gemma-2-2b-it
 - lora
 - transformers
+datasets:
+- AiAF/conversations
+pipeline_tag: text-generation
+model-index:
+- name: rp-2b
+  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/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: `0.13.0.dev0`
+```yaml
+# 1. Base Model & Tokenizer
+base_model: google/gemma-2-2b-it
+model_type: AutoModelForCausalLM # Corrected from 'type_of_model' for axolotl
+tokenizer_type: AutoTokenizer
+hub_model_id: AiAF/rp-2b # New model ID for this finetune
+hub_strategy: checkpoint
+
+# 2. LoRA / QLoRA Configuration
+load_in_4bit: true
+adapter: qlora
+lora_r: 64
+lora_alpha: 128
+lora_dropout: 0.05
+lora_target_linear: true
+
+# 3. Dataset Configuration (TRAIN = streamed)
+streaming: true
+streaming_multipack_buffer_size: 10000
+sample_packing: true
+datasets:
+  - path: AiAF/conversations
+    data_files: conversations_V3.jsonl
+   # revision:
+    type: chat_template
+    split: train
+    field_messages: conversations
+    message_property_mappings:
+      role: from
+      content: value
+    chat_template: jinja
+    chat_template_jinja: |
+      {{ bos_token }}
+      {% for m in messages %}
+        {% set role = 'model' if m['role']=='assistant' else 'user' %}
+        {{ '<start_of_turn>' + role + '\n' + m['content'] | trim + '<end_of_turn>\n' }}
+      {% endfor %}
+      {% if add_generation_prompt %}
+      {{ '<start_of_turn>model\n' }}
+      {% endif %}
+
+#    chat_template_jinja: |
+#      {{ bos_token }}
+#      {% set last = None %}
+#      {% for m in messages %}
+#        {% set raw_role = 'model' if m['role']=='assistant' else m['role'] %}
+#        {% set role = 'user' if raw_role=='system' else raw_role %}
+#        {% if role == last and role == 'user' %}
+#          {{ m['content'] | trim }}
+#        {% else %}
+#          {{ '<start_of_turn>' + role + '\n' + m['content'] | trim + '<end_of_turn>\n' }}
+#        {% endif %}
+#        {% set last = role %}
+#      {% endfor %}
+#      {% if add_generation_prompt %}
+#      {{ '<start_of_turn>model\n' }}
+#      {% endif %}
+    roles_to_train: ["assistant"]
+    train_on_eos: "turn"
+# Use a fixed (non-streamed) eval file with the same schema/Jinja
+test_datasets:
+  - path: .
+    name: json
+    type: chat_template
+    data_files: eval-datasets/shuf-1000_conversations_V2.jsonl        # small, representative eval slice
+    split: train
+    field_messages: conversations
+    message_property_mappings:
+      role: from
+      content: value
+    chat_template: jinja
+    chat_template_jinja: |
+      {{ bos_token }}
+      {% for m in messages %}
+        {% set role = 'model' if m['role']=='assistant' else 'user' %}
+        {{ '<start_of_turn>' + role + '\n' + m['content'] | trim + '<end_of_turn>\n' }}
+      {% endfor %}
+      {% if add_generation_prompt %}
+      {{ '<start_of_turn>model\n' }}
+      {% endif %}
+#    chat_template_jinja: |
+#      {{ bos_token }}
+#      {% set last = None %}
+#      {% for m in messages %}
+#        {% set raw_role = 'model' if m['role']=='assistant' else m['role'] %}
+#        {% set role = 'user' if raw_role=='system' else raw_role %}
+#        {% if role == last and role == 'user' %}
+#          {{ m['content'] | trim }}
+#        {% else %}
+#          {{ '<start_of_turn>' + role + '\n' + m['content'] | trim + '<end_of_turn>\n' }}
+#        {% endif %}
+#        {% set last = role %}
+#      {% endfor %}
+#      {% if add_generation_prompt %}
+#      {{ '<start_of_turn>model\n' }}
+#      {% endif %}
+    roles_to_train: ["assistant"]
+
+# 4. Training Parameters
+sequence_len: 2048
+sample_packing: true
+eval_sample_packing: true
+# val_set_size: 0.05            #  remove for streaming
+# num_epochs: 10                 #  replace epochs with max_steps
+max_steps: 1000                 #  set your target steps
+dataset_prepared_path: last_run_prepared
+
+# 5. Saving and Evaluation Strategy (use steps with streaming)
+evaluation_strategy: steps
+save_strategy: steps
+eval_steps: 50
+save_steps: 50
+save_total_limit: 100
+
+resume_from_checkpoint:
+
+# 6. Output & Logging
+output_dir: ./outputs/sft/gemma-2-2b-it-rp-sft-qlora
+
+wandb_project: "rp-sft"
+wandb_name: "gemma-2-2b-it-rp-sft-qlora"
+wandb_log_model: "false"
+wandb_run_id: "gemma-2-2b-it-rp-sft-qlora"
+
+# 7. Batching & Optimizer
+gradient_accumulation_steps: 4
+micro_batch_size: 2
+optimizer: adamw_bnb_8bit
+lr_scheduler: cosine
+learning_rate: 0.0002
+weight_decay: 0.0
+
+# 8. Hardware & Performance
+bf16: true
+#fp16: true
+tf32: true
+
+flash_attention: true
+gradient_checkpointing: true
+logging_steps: 1
+
+# 9. Special Tokens
+eot_tokens: ["<end_of_turn>"]
+special_tokens:
+  bos_token: "<bos>"
+  eos_token: "<eos>"
+  pad_token: "<pad>"
+
+```
+
+</details><br>
+
+# rp-2b
+
+This model is a fine-tuned version of [google/gemma-2-2b-it](https://huggingface.co/google/gemma-2-2b-it) on the AiAF/conversations dataset.
+It achieves the following results on the evaluation set:
+- Loss: 2.2455
+- Memory/max Active (gib): 7.78
+- Memory/max Allocated (gib): 7.78
+- Memory/device Reserved (gib): 17.79
+
+## 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: 0.0002
+- train_batch_size: 1
+- eval_batch_size: 2
+- seed: 42
+- gradient_accumulation_steps: 4
+- total_train_batch_size: 4
+- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: cosine
+- lr_scheduler_warmup_steps: 30
+- training_steps: 1000
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Active (gib) | Allocated (gib) | Reserved (gib) |
+|:-------------:|:-----:|:----:|:---------------:|:------------:|:---------------:|:--------------:|
+| No log        | 0     | 0    | 3.1654          | 7.61         | 7.61            | 8.66           |
+| 2.7377        | 0.05  | 50   | 2.5978          | 7.78         | 7.78            | 17.75          |
+| 2.3997        | 0.1   | 100  | 2.5592          | 7.78         | 7.78            | 17.79          |
+| 2.6275        | 0.15  | 150  | 2.5410          | 7.78         | 7.78            | 17.79          |
+| 2.8182        | 0.2   | 200  | 2.5224          | 7.78         | 7.78            | 17.79          |
+| 2.4428        | 0.25  | 250  | 2.4962          | 7.78         | 7.78            | 17.79          |
+| 2.6206        | 0.3   | 300  | 2.4672          | 7.78         | 7.78            | 17.79          |
+| 2.4492        | 0.35  | 350  | 2.4435          | 7.78         | 7.78            | 17.79          |
+| 2.2787        | 0.4   | 400  | 2.4185          | 7.78         | 7.78            | 17.79          |
+| 2.541         | 0.45  | 450  | 2.3998          | 7.78         | 7.78            | 17.79          |
+| 2.5542        | 0.5   | 500  | 2.3640          | 7.78         | 7.78            | 17.79          |
+| 2.6825        | 0.55  | 550  | 2.3484          | 7.78         | 7.78            | 17.79          |
+| 2.6304        | 0.6   | 600  | 2.3278          | 7.78         | 7.78            | 17.79          |
+| 2.4854        | 0.65  | 650  | 2.3104          | 7.78         | 7.78            | 17.79          |
+| 2.3788        | 0.7   | 700  | 2.2877          | 7.78         | 7.78            | 17.79          |
+| 2.2126        | 0.75  | 750  | 2.2748          | 7.78         | 7.78            | 17.79          |
+| 2.4695        | 0.8   | 800  | 2.2662          | 7.78         | 7.78            | 17.79          |
+| 2.5086        | 0.85  | 850  | 2.2553          | 7.78         | 7.78            | 17.79          |
+| 2.404         | 0.9   | 900  | 2.2489          | 7.78         | 7.78            | 17.79          |
+| 2.4012        | 0.95  | 950  | 2.2460          | 7.78         | 7.78            | 17.79          |
+| 2.2586        | 1.0   | 1000 | 2.2455          | 7.78         | 7.78            | 17.79          |
+
+
 ### Framework versions
 
-- PEFT 0.17.1
+- PEFT 0.17.1
+- Transformers 4.57.0
+- Pytorch 2.7.1+cu126
+- Datasets 4.0.0
+- Tokenizers 0.22.1

debug.log

@@ -5437,4 +5437,196 @@ trainable params: 83,066,880 || all params: 2,697,408,768 || trainable%: 3.0795
 
 
                                                               [2025-10-19 01:14:15,108] [INFO] [axolotl.core.trainers.base._save:664] [PID:42363] Saving model checkpoint to ./outputs/sft/gemma-2-2b-it-rp-sft-qlora/checkpoint-1000
 
                                                               
-
+
+[2025-10-19 01:14:39,753] [INFO] [axolotl.train.save_trained_model:218] [PID:42363] Training completed! Saving trained model to ./outputs/sft/gemma-2-2b-it-rp-sft-qlora.
+[2025-10-19 01:14:40,608] [INFO] [axolotl.train.save_trained_model:336] [PID:42363] Model successfully saved to ./outputs/sft/gemma-2-2b-it-rp-sft-qlora
+[2025-10-19 01:14:41,005] [INFO] [axolotl.core.trainers.base._save:664] [PID:42363] Saving model checkpoint to ./outputs/sft/gemma-2-2b-it-rp-sft-qlora
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