Update root README — checkpoint-14769 logged

README.md

@@ -1,28 +1,3 @@
----
-language:
-- en
-license: cc-by-nc-4.0
-tags:
-- code
-- python
-- django
-- flask
-- fastapi
-- react
-- nextjs
-- typescript
-- sql
-- docker
-- qlora
-- fine-tuned
-- coding-assistant
-- web-development
-base_model: Qwen/Qwen2.5-Coder-7B-Instruct
-pipeline_tag: text-generation
-model_type: qwen2
-library_name: transformers
----
-
 # ScriptSwiftAI v12 (Dikshan)
 
 > QLoRA fine-tuned full-stack code generation model by **Dikshan** (India).
@@ -47,101 +22,19 @@ https://creativecommons.org/licenses/by-nc/4.0/
 | **Method** | QLoRA (4-bit, r=64) |
 | **Hardware** | NVIDIA RTX 5070 Ti 16GB |
 | **Target Steps** | 40,000 |
-| **Dataset** | ~570k samples (17 datasets) |
+| **Dataset** | ~450-500k samples (17 datasets) |
 | **Max Length** | 1536 tokens |
 | **Developer** | Dikshan (India) |
 | **Training Started** | June 2026 |
 
 ---
 
-## How to Use
-
-Load the adapter on top of the base model:
-
-```python
-from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
-from peft import PeftModel
-import torch
-
-bnb_config = BitsAndBytesConfig(
-    load_in_4bit=True,
-    bnb_4bit_use_double_quant=True,
-    bnb_4bit_quant_type="nf4",
-    bnb_4bit_compute_dtype=torch.bfloat16
-)
-
-base = AutoModelForCausalLM.from_pretrained(
-    "Qwen/Qwen2.5-Coder-7B-Instruct",
-    quantization_config=bnb_config,
-    device_map="auto"
-)
-model = PeftModel.from_pretrained(base, "Dikshan1234/ScriptSwiftAIV2/checkpoint-14000")
-tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-Coder-7B-Instruct")
-
-prompt = """You are ScriptSwiftAI, a professional full-stack and Python web development assistant made by Dikshan.
-
-### Instruction:
-Build a Django REST API with JWT authentication.
-
-### Response:
-"""
-
-inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
-output = model.generate(**inputs, max_new_tokens=1536, temperature=0.3, top_p=0.85, top_k=40)
-print(tokenizer.decode(output[0], skip_special_tokens=True))
-```
-
-> GGUF version coming soon for easy local use with Ollama.
-
----
-
-## Dataset Sources (17 datasets, ~570k samples)
-
-| # | Dataset | Focus |
-|---|---------|-------|
-| 1 | sahil2801/CodeAlpaca-20k | General coding |
-| 2 | TokenBender/code_instructions_122k_alpaca_style | Python |
-| 3 | flytech/python-codes-25k | Python |
-| 4 | AhmedSSoliman/DJANGO | Django |
-| 5 | nickrosh/Evol-Instruct-Code-80k-v1 | Web dev |
-| 6 | iamtarun/python_code_instructions_18k_alpaca | Python |
-| 7 | ise-uiuc/Magicoder-OSS-Instruct-75K | General coding |
-| 8 | glaiveai/glaive-code-assistant-v3 | Web dev |
-| 9 | ajibawa-2023/Python-Code-23k-ShareGPT | Python |
-| 10 | m-a-p/CodeFeedback-Filtered-Instruction | Web dev |
-| 11 | cfahlgren1/react-code-instructions | React |
-| 12 | Tesslate/Next.js-Dataset | Next.js |
-| 13 | mhhmm/typescript-instruct-20k | TypeScript |
-| 14 | grenishrai/typescript-dataset | TypeScript |
-| 15 | NumbersStation/NSText2SQL | SQL |
-| 16 | nvidia/OpenCodeInstruct | General coding |
-| 17 | Identity QA | ScriptSwiftAI identity |
-
----
-
-## Training Config
-
-| Setting | Value |
-|---|---|
-| **Base Model** | Qwen2.5-Coder-7B-Instruct |
-| **Method** | QLoRA (4-bit NF4) |
-| **LoRA Rank** | r=64, alpha=128 |
-| **Target Steps** | 40,000 |
-| **Batch Size** | 1 (effective 16 with grad accum) |
-| **Learning Rate** | 2e-4 cosine, 1000 warmup steps |
-| **Optimizer** | paged_adamw_8bit |
-| **Max Length** | 1536 tokens |
-| **Hardware** | RTX 5070 Ti 16GB |
-
----
-
----
-
 ## Changelog / Training Timeline
 
 *Auto-updated by train_v12.py on every checkpoint save.*
 *Append-only — entries never overwritten. HuggingFace commit history provides timestamps.*
 
+
 ---
 
 ### 🔵 Checkpoint — Step 13
@@ -459,4 +352,15 @@ print(tokenizer.decode(output[0], skip_special_tokens=True))
 **Loss:** 0.2634
 **Epoch:** 0.409
 **Zone:** 📈 Good
-**Status:** Training ongoing — auto-uploaded via train_v12.py
+**Status:** Training ongoing — auto-uploaded via train_v12.py
+
+---
+
+### 🔵 Checkpoint — Step 14,769
+**Date:** June 27, 2026 at 10:48 IST
+**Checkpoint:** checkpoint-14769
+**Step:** 14,769 / 40,000 (36.9% complete)
+**Loss:** 0.1741
+**Epoch:** 0.431
+**Zone:** 🎯 Sweet Spot
+**Status:** Training ongoing — auto-uploaded via train_v12.py