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