End of training

README.md

@@ -1,55 +1,58 @@
----
-library_name: peft
-license: llama3.2
-base_model: meta-llama/Llama-3.2-1B
-tags:
-- generated_from_trainer
-model-index:
-- name: Llama3_CAPL_Model
-  results: []
----
+---
+base_model: meta-llama/Llama-3.1-8B-Instruct
+library_name: transformers
+model_name: Llama3_CAPL_Model
+tags:
+- generated_from_trainer
+- trl
+- sft
+licence: license
+---
 
-<!-- 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. -->
+# Model Card for Llama3_CAPL_Model
 
-# Llama3_CAPL_Model
+This model is a fine-tuned version of [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct).
+It has been trained using [TRL](https://github.com/huggingface/trl).
 
-This model is a fine-tuned version of [meta-llama/Llama-3.2-1B](https://huggingface.co/meta-llama/Llama-3.2-1B) on an unknown dataset.
+## Quick start
 
-## Model description
+```python
+from transformers import pipeline
 
-More information needed
+question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
+generator = pipeline("text-generation", model="DJseo/Llama3_CAPL_Model", device="cuda")
+output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
+print(output["generated_text"])
+```
 
-## Intended uses & limitations
-
-More information needed
-
-## Training and evaluation data
+## Training procedure
 
-More information needed
+ 
 
-## Training procedure
 
-### Training hyperparameters
+This model was trained with SFT.
 
-The following hyperparameters were used during training:
-- learning_rate: 2e-05
-- train_batch_size: 4
-- eval_batch_size: 8
-- seed: 42
-- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
-- lr_scheduler_type: linear
-- num_epochs: 3
-- mixed_precision_training: Native AMP
+### Framework versions
 
-### Training results
+- TRL: 0.15.2
+- Transformers: 4.49.0
+- Pytorch: 2.5.1+cu121
+- Datasets: 3.4.1
+- Tokenizers: 0.21.1
 
+## Citations
 
 
-### Framework versions
 
-- PEFT 0.14.0
-- Transformers 4.49.0
-- Pytorch 2.5.1+cu121
-- Datasets 3.4.1
-- Tokenizers 0.21.1
+Cite TRL as:
+    
+```bibtex
+@misc{vonwerra2022trl,
+	title        = {{TRL: Transformer Reinforcement Learning}},
+	author       = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou챕dec},
+	year         = 2020,
+	journal      = {GitHub repository},
+	publisher    = {GitHub},
+	howpublished = {\url{https://github.com/huggingface/trl}}
+}
+```

adapter_config.json

@@ -1,7 +1,7 @@
 {
   "alpha_pattern": {},
   "auto_mapping": null,
-  "base_model_name_or_path": "meta-llama/Llama-3.2-1B",
+  "base_model_name_or_path": "meta-llama/Llama-3.1-8B-Instruct",
   "bias": "none",
   "eva_config": null,
   "exclude_modules": null,
@@ -14,7 +14,7 @@
   "loftq_config": {},
   "lora_alpha": 32,
   "lora_bias": false,
-  "lora_dropout": 0.1,
+  "lora_dropout": 0.01,
   "megatron_config": null,
   "megatron_core": "megatron.core",
   "modules_to_save": null,
@@ -24,6 +24,7 @@
   "revision": null,
   "target_modules": [
     "q_proj",
+    "k_proj",
     "v_proj"
   ],
   "task_type": "CAUSAL_LM",

adapter_model.safetensors

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runs/Mar26_16-35-39_DESKTOP-OBIMAP9/events.out.tfevents.1742974540.DESKTOP-OBIMAP9

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training_args.bin

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