Modelpublic

Gianloko/apex-coder-1.5b

sourceHugging Faceapache-2.0updated 6mo agoView on Hugging Face

3likes63downloads

Model Card

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 (~150 MB) or the GGUF Q4_K_M (~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 (~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