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Model: Gianloko/apex-coder-1.5b
Source: Original Platform

README.md

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+---
+license: apache-2.0
+base_model: unsloth/Qwen2.5-Coder-3B-Instruct-bnb-4bit
+tags: [apex, salesforce, lwc, visualforce, aura, soql, sfdx, code, fine-tuned, qlora, unsloth]
+datasets: [Gianloko/apex-coder-training-data]
+language: [en]
+pipeline_tag: text-generation
+---
+
+# ApexCoder-1.5B · Merged 16-bit Model
+
+*Last updated: 2026-03-20 — Cycle 2*
+
+Production-ready merged model (base + LoRA fused into 16-bit weights).
+Trained on a single NVIDIA A40 (44 GB) using Unsloth QLoRA + TRL SFTTrainer.
+
+> **Looking for a smaller download?**
+> Use the [LoRA adapter](Gianloko/apex-coder-1.5b-lora) (~150 MB) or the
+> [GGUF Q4_K_M](Gianloko/apex-coder-1.5b-GGUF) (~986 MB) for Ollama.
+
+## 📊 Evaluation — Cycle 2
+
+| Metric | Value |
+|---|---|
+| **LLM-as-judge (avg)** | **12.6/15** |
+| **Perplexity** | **1.14** |
+| **Δ vs previous cycle** | **+12.6** |
+| Training loss | 0.2274 |
+| Training samples | 8,990 |
+| Training steps | 1100 |
+
+### By reasoning type
+
+| Type | Status | Score | Progress |
+|---|---|---|---|
+
+
+### Cycle history
+
+| Cycle | Date | Score | PPL | Δ | vs Published |
+|---|---|---|---|---|---|
+| 1 | 2026-03-20 | 12.9/15 | 1.17 | +12.9 | 12.9 |
+| 2 | 2026-03-20 | 12.6/15 | 1.14 | +12.6 | 13.2 |
+
+
+## 🚀 Quick start
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+model = AutoModelForCausalLM.from_pretrained(
+    "Gianloko/apex-coder-1.5b",
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+)
+tokenizer = AutoTokenizer.from_pretrained("Gianloko/apex-coder-1.5b")
+
+messages = [
+    {"role": "system", "content": "You are ApexCoder, a world-class Salesforce expert."},
+    {"role": "user",   "content": "Write a bulkified Apex trigger on Opportunity that prevents status changes to Closed Won if no related Products exist."},
+]
+inputs = tokenizer.apply_chat_template(
+    messages, return_tensors="pt", add_generation_prompt=True
+).to(model.device)
+
+output = model.generate(inputs, max_new_tokens=512, temperature=0.1, do_sample=False)
+print(tokenizer.decode(output[0][inputs.shape[1]:], skip_special_tokens=True))
+```
+
+## 🦙 Ollama (GGUF — recommended for local use)
+
+```bash
+ollama pull hf.co/Gianloko/apex-coder-1.5b-GGUF:Q4_K_M
+ollama run hf.co/Gianloko/apex-coder-1.5b-GGUF:Q4_K_M
+```
+
+## 🔧 LoRA adapter
+
+If you already have the base model loaded, use the
+[LoRA adapter](Gianloko/apex-coder-1.5b-lora) (~150 MB) instead:
+
+```python
+from peft import PeftModel
+model = PeftModel.from_pretrained(base_model, "Gianloko/apex-coder-1.5b-lora")
+```
+
+## ⚙️ V6 pipeline notes
+
+- **Warm-start training** — cycle 2+ initialises from previous LoRA adapter
+- **Best-ever gate** — publish blocked if new model regresses vs published model
+- **Data quality** — validated with langdetect + non-ASCII ratio filter
+- **CanaryCallback** — 3 probes per epoch, majority-fail aborts training
+- **Post-merge validation** — 3 sanity + 3 hallucination probes gate every push
+- **Dataset versioned** — cycle tags on HuggingFace for full rollback capability
+
+## License
+
+Apache 2.0