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README.md

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+---
+license: llama3.2
+language:
+- en
+base_model: meta-llama/Llama-3.2-1B
+pipeline_tag: text-classification
+library_name: peft
+tags:
+- regression
+- story-point-estimation
+- software-engineering
+datasets:
+- datamanagement
+metrics:
+- mae
+- mdae
+model-index:
+- name: llama-3.2-1b-story-point-estimation
+  results:
+  - task:
+      type: regression
+      name: Story Point Estimation
+    dataset:
+      name: datamanagement Dataset
+      type: datamanagement
+      split: test
+    metrics:
+    - type: mae
+      value: 7.25
+      name: Mean Absolute Error (MAE)
+    - type: mdae
+      value: 4.142
+      name: Median Absolute Error (MdAE)
+---
+# LLAMA 3 Story Point Estimator - datamanagement
+
+This model is fine-tuned on issue descriptions from datamanagement and tested on datamanagement for story point estimation.
+
+## Model Details
+- Base Model: LLAMA 3.2 1B
+- Training Project: datamanagement
+- Test Project: datamanagement
+- Task: Story Point Estimation (Regression)
+- Architecture: PEFT (LoRA)
+- Tokenizer: SP Word Level
+
+- Input: Issue titles
+- Output: Story point estimation (continuous value)
+
+## Usage
+```python
+from transformers import AutoModelForSequenceClassification
+from peft import PeftConfig, PeftModel
+from tokenizers import Tokenizer
+
+# Load peft config model
+config = PeftConfig.from_pretrained("DEVCamiloSepulveda/2-LLAMA3SP-datamanagement")
+
+# Load tokenizer and model
+tokenizer = Tokenizer.from_pretrained("DEVCamiloSepulveda/2-LLAMA3SP-datamanagement")
+base_model = AutoModelForSequenceClassification.from_pretrained(
+    config.base_model_name_or_path,
+    num_labels=1,
+    torch_dtype=torch.float16,
+    device_map='auto'
+)
+model = PeftModel.from_pretrained(base_model, "DEVCamiloSepulveda/2-LLAMA3SP-datamanagement")
+
+# Prepare input text
+text = "Your issue description here"
+inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=20, padding="max_length")
+
+# Get prediction
+outputs = model(**inputs)
+story_points = outputs.logits.item()
+```
+
+## Training Details
+- Fine-tuning method: LoRA (Low-Rank Adaptation)
+- Sequence length: 20 tokens
+- Best training epoch: 17 / 20 epochs
+- Batch size: 32
+- Training time: 2414.499 seconds
+- Mean Absolute Error (MAE): 7.250
+- Median Absolute Error (MdAE): 4.142
+### Framework versions
+
+- PEFT 0.14.0

adapter_config.json

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+{
+  "alpha_pattern": {},
+  "auto_mapping": null,
+  "base_model_name_or_path": "meta-llama/Llama-3.2-1B",
+  "bias": "none",
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": true,
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 16,
+  "lora_bias": false,
+  "lora_dropout": 0.1,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": [
+    "classifier",
+    "score"
+  ],
+  "peft_type": "LORA",
+  "r": 8,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "k_proj",
+    "q_proj",
+    "o_proj",
+    "v_proj"
+  ],
+  "task_type": "SEQ_CLS",
+  "use_dora": false,
+  "use_rslora": false
+}

adapter_model.safetensors

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pytorch_model.bin

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