Update README.md

README.md

@@ -1,20 +1,36 @@
 ---
+datasets:
+- ewof/code-alpaca-instruct-unfiltered
 library_name: peft
+tags:
+- llama2-7b
+- code
+- instruct
+- instruct-code
+- code-alpaca
+- alpaca-instruct
+- alpaca
+- llama7b
+- gpt2
 ---
-## Training procedure
 
+We finetuned Llama2-7B on Code-Alpaca-Instruct Dataset (ewof/code-alpaca-instruct-unfiltered) for 5 epochs or ~ 25,000 steps using [MonsterAPI](https://monsterapi.ai) no-code [LLM finetuner](https://docs.monsterapi.ai/fine-tune-a-large-language-model-llm).
 
-The following `bitsandbytes` quantization config was used during training:
-- load_in_8bit: True
-- load_in_4bit: False
-- 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: fp4
-- bnb_4bit_use_double_quant: False
-- bnb_4bit_compute_dtype: float32
-### Framework versions
+This dataset is HuggingFaceH4/CodeAlpaca_20K unfiltered, removing 36 instances of blatant alignment. 
 
+The finetuning session got completed in 4  hours and costed us only `$16` for the entire finetuning run!
 
-- PEFT 0.4.0
+#### Hyperparameters & Run details:
+- Model Path: meta-llama/Llama-2-7b
+- Dataset: ewof/code-alpaca-instruct-unfiltered
+- Learning rate: 0.0003
+- Number of epochs: 5
+- Data split: Training: 90% / Validation: 10%
+- Gradient accumulation steps: 1
+
+Loss metrics:
+![training loss](train-loss.png "Training loss")
+
+---
+license: apache-2.0
+---