ahmedheakl/asm2asm-qwen2.5coder-0.5b-500k-2ep

README.md

@@ -1,57 +1,57 @@
 ---
-base_model: Qwen/Qwen2.5-Coder-0.5B-Instruct
 library_name: transformers
-model_name: asm2asm-qwen2.5coder-0.5b-500k-2ep
+license: apache-2.0
+base_model: Qwen/Qwen2.5-Coder-0.5B-Instruct
 tags:
-- generated_from_trainer
 - trl
 - sft
-licence: license
+- generated_from_trainer
+model-index:
+- name: asm2asm-qwen2.5coder-0.5b-500k-2ep
+  results: []
 ---
 
-# Model Card for asm2asm-qwen2.5coder-0.5b-500k-2ep
+<!-- 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. -->
 
-This model is a fine-tuned version of [Qwen/Qwen2.5-Coder-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-0.5B-Instruct).
-It has been trained using [TRL](https://github.com/huggingface/trl).
+# asm2asm-qwen2.5coder-0.5b-500k-2ep
 
-## Quick start
+This model is a fine-tuned version of [Qwen/Qwen2.5-Coder-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-0.5B-Instruct) on an unknown dataset.
 
-```python
-from transformers import pipeline
+## Model description
 
-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="ahmedheakl/asm2asm-qwen2.5coder-0.5b-500k-2ep", device="cuda")
-output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
-print(output["generated_text"])
-```
+More information needed
 
-## Training procedure
+## Intended uses & limitations
 
-[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/ahmed-heakl/huggingface/runs/6vagatxa)
+More information needed
 
-This model was trained with SFT.
+## Training and evaluation data
 
-### Framework versions
+More information needed
 
-- TRL: 0.12.1
-- Transformers: 4.46.3
-- Pytorch: 2.5.1+cu124
-- Datasets: 3.1.0
-- Tokenizers: 0.20.3
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 0.0002
+- train_batch_size: 1
+- eval_batch_size: 8
+- seed: 42
+- gradient_accumulation_steps: 8
+- total_train_batch_size: 8
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 2
 
-## Citations
+### Training results
 
 
 
-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 Gallouédec},
-	year         = 2020,
-	journal      = {GitHub repository},
-	publisher    = {GitHub},
-	howpublished = {\url{https://github.com/huggingface/trl}}
-}
-```
+### Framework versions
+
+- Transformers 4.44.2
+- Pytorch 2.4.1+cu118
+- Datasets 3.0.0
+- Tokenizers 0.19.1

generation_config.json

@@ -10,5 +10,5 @@
   "temperature": 0.7,
   "top_k": 20,
   "top_p": 0.8,
-  "transformers_version": "4.46.3"
+  "transformers_version": "4.44.2"
 }