Update README.md (#2)

- Update README.md (f279f3ae402f205c1ce597809addb1bad3f84607)


Co-authored-by: Souvik Datta <souvik0306@users.noreply.huggingface.co>

README.md

@@ -1,74 +1,43 @@
 ---
 library_name: peft
-license: apache-2.0
----
-## 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: True
-- bnb_4bit_compute_dtype: bfloat16
-### Framework versions
-
-
-- PEFT 0.5.0
-
-- ---
-library_name: peft
 tags:
 - code
 - instruct
-- gpt2
+- mistral
 datasets:
-- HuggingFaceH4/no_robots
-base_model: gpt2
+- cognitivecomputations/dolphin-coder
+base_model: mistralai/Mistral-7B-v0.1
 license: apache-2.0
 ---
 
 ### Finetuning Overview:
 
-**Model Used:** gpt2
+**Model Used:** mistralai/Mistral-7B-v0.1 
 
-**Dataset:** HuggingFaceH4/no_robots  
+**Dataset:** cognitivecomputations/dolphin-coder 
 
 #### Dataset Insights:
 
-[No Robots](https://huggingface.co/datasets/HuggingFaceH4/no_robots) is a high-quality dataset of 10,000 instructions and demonstrations created by skilled human annotators. This data can be used for supervised fine-tuning (SFT) to make language models follow instructions better.
+[Dolphin-Coder](https://huggingface.co/datasets/cognitivecomputations/dolphin-coder) Dolphin-Coder dataset – a high-quality collection of 100,000+ coding questions and responses. It's perfect for supervised fine-tuning (SFT), and teaching language models to improve on coding-based tasks.
 
 #### Finetuning Details:
 
-With the utilization of [MonsterAPI](https://monsterapi.ai)'s [LLM finetuner](https://docs.monsterapi.ai/fine-tune-a-large-language-model-llm), this finetuning:
+With the utilization of [MonsterAPI](https://monsterapi.ai)'s [no-code LLM finetuner](https://monsterapi.ai/finetuning), this finetuning:
 
 - Was achieved with great cost-effectiveness.
-- Completed in a total duration of 3mins 40s for 1 epoch using an A6000 48GB GPU.
-- Costed `$0.101` for the entire epoch.
+- Completed in a total duration of 15hr 36mins for 1 epochs using an A6000 48GB GPU.
+- Costed `$31.51` for the entire 1 epoch.
 
 #### Hyperparameters & Additional Details:
 
 - **Epochs:** 1
-- **Cost Per Epoch:** $0.101
-- **Total Finetuning Cost:** $0.101
-- **Model Path:** gpt2
+- **Cost Per Epoch:** $31.51
+- **Model Path:** mistralai/Mistral-7B-v0.1
 - **Learning Rate:** 0.0002
 - **Data Split:** 100% train 
-- **Gradient Accumulation Steps:** 4
-- **lora r:** 32
-- **lora alpha:** 64
-
-#### Prompt Structure
-```
-<|system|> <|endoftext|> <|user|> [USER PROMPT]<|endoftext|> <|assistant|> [ASSISTANT ANSWER] <|endoftext|>
-```
-#### Training loss :
-
-![training loss](https://cdn-uploads.huggingface.co/production/uploads/63ba46aa0a9866b28cb19a14/9bgb518kFwtDsFtrHzmTu.png)
+- **Gradient Accumulation Steps:** 64
+- **lora r:** 64
+- **lora alpha:** 16
 
+---
 license: apache-2.0