Model save

README.md

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+---
+library_name: peft
+tags:
+- trl
+- dpo
+- generated_from_trainer
+base_model: alexredna/Tukan-1.1B-Chat-v0.6
+model-index:
+- name: Tukan-1.1B-Chat-v0.6_dpo
+  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. -->
+
+# Tukan-1.1B-Chat-v0.6_dpo
+
+This model is a fine-tuned version of [alexredna/Tukan-1.1B-Chat-v0.6](https://huggingface.co/alexredna/Tukan-1.1B-Chat-v0.6) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.6664
+- Rewards/chosen: -0.0698
+- Rewards/rejected: -0.1297
+- Rewards/accuracies: 0.6667
+- Rewards/margins: 0.0600
+- Logps/rejected: -364.0182
+- Logps/chosen: -408.6165
+- Logits/rejected: -2.1219
+- Logits/chosen: -2.2568
+
+## 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: 3e-07
+- train_batch_size: 5
+- eval_batch_size: 3
+- seed: 42
+- distributed_type: multi-GPU
+- gradient_accumulation_steps: 32
+- total_train_batch_size: 160
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_ratio: 0.1
+- num_epochs: 3
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
+|:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|
+| 0.6819        | 0.99  | 61   | 0.6804          | -0.0465        | -0.0746          | 0.6318             | 0.0281          | -363.4672      | -408.3842    | -2.1356         | -2.2671       |
+| 0.6718        | 2.0   | 123  | 0.6719          | -0.0689        | -0.1170          | 0.6269             | 0.0482          | -363.8911      | -408.6075    | -2.1242         | -2.2588       |
+| 0.6687        | 2.97  | 183  | 0.6664          | -0.0698        | -0.1297          | 0.6667             | 0.0600          | -364.0182      | -408.6165    | -2.1219         | -2.2568       |
+
+
+### Framework versions
+
+- Transformers 4.36.2
+- Pytorch 2.1.1+cu121
+- Datasets 2.14.6
+- Tokenizers 0.15.0
+## Training procedure
+
+
+The following `bitsandbytes` quantization config was used during training:
+- quant_method: bitsandbytes
+- load_in_8bit: False
+- load_in_4bit: True
+- llm_int8_threshold: 6.0
+- llm_int8_skip_modules: None
+- llm_int8_enable_fp32_cpu_offload: False
+- llm_int8_has_fp16_weight: False
+- bnb_4bit_quant_type: nf4
+- bnb_4bit_use_double_quant: False
+- bnb_4bit_compute_dtype: float16
+
+### Framework versions
+
+
+- PEFT 0.6.1

adapter_config.json

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+{
+  "alpha_pattern": {},
+  "auto_mapping": null,
+  "base_model_name_or_path": "alexredna/Tukan-1.1B-Chat-v0.6",
+  "bias": "none",
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": true,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "lora_alpha": 64,
+  "lora_dropout": 0.05,
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "r": 64,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
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+    "k_proj",
+    "lm_head",
+    "o_proj",
+    "gate_proj",
+    "down_proj",
+    "v_proj",
+    "up_proj"
+  ],
+  "task_type": "CAUSAL_LM"
+}

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all_results.json

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+}

eval_results.json

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+    "eval_steps_per_second": 1.71
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special_tokens_map.json

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tokenizer_config.json

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+  "chat_template": "{% for message in messages %}\n{% if message['role'] == 'user' %}\n{{ '<|user|>\n' + message['content'] + eos_token }}\n{% elif message['role'] == 'system' %}\n{{ '<|system|>\n' + message['content'] + eos_token }}\n{% elif message['role'] == 'assistant' %}\n{{ '<|assistant|>\n'  + message['content'] + eos_token }}\n{% endif %}\n{% if loop.last and add_generation_prompt %}\n{{ '<|assistant|>' }}\n{% endif %}\n{% endfor %}",
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train_results.json

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