You can deploy this model and start earning money today!

True2456/
Gemma-4-12B-Agentic-LoRA

Name: Gemma-4-12B-Agentic-LoRA
Availability: InStock

Views

No views yet

Gemma 4 12B — Agentic Specialist LoRA (Expert 1)

Specialist LoRA adapter fine-tuned on top of mlx-community/gemma-4-12b-it-bf16 for multi-step autonomous tool calling, structured JSON execution, and multi-turn software engineering workflows.

Designed as Expert 1 within multi-specialist MoE fusion architectures or for standalone high-precision agent execution on Apple Silicon via Apple MLX (mlx_lm).

Model Specifications

Parameter	Specification
Base Model	`mlx-community/gemma-4-12b-it-bf16`
Adapter Architecture	LoRA (Low-Rank Adaptation)
Target Layers	48 Transformer Layers (`q_proj`, `k_proj`, `v_proj`, `o_proj`, `gate_proj`, `up_proj`, `down_proj`)
LoRA Rank (`r`)	`16`
LoRA Alpha (`α`)	`32`
LoRA Scale	`10.0`
Dropout	`0.05`
Max Sequence Length	`8192` tokens
Training Framework	`mlx-lm` on Apple Silicon Metal

Training Dataset Composition

Trained on the curated gemma12b_agentic_specialist pack containing 107,761 training steps organized into three quality tiers:

Tier S (Personal Gold Trajectories — 40,080 steps):
- Curated multi-turn reasoning and tool-orchestration trajectories.
- Oversampled 40x (2,240 base samples / 56 long-context trajectories) to enforce precise tool call formatting and execution discipline.
Tier A (Flawless Native Pack — 7,782 steps):
- Verified native tool calling traces with strict JSON schema adherence and error recovery patterns.
Tier B (Open SWE Trajectories — 59,899 steps):
- Real-world software engineering execution traces covering file inspection, patch generation, test execution, and debugging loops.

Usage with Apple MLX (`mlx-lm`)

Installation

pip install mlx mlx-lm

Python Inference

python

from mlx_lm import load, generate

model_path = "mlx-community/gemma-4-12b-it-bf16"
adapter_path = "True2456/Gemma-4-12B-Agentic-LoRA"

model, tokenizer = load(
    model_path,
    adapter_path=adapter_path
)

prompt = tokenizer.apply_chat_template([
    {"role": "system", "content": "You are an autonomous engineering agent with tool access."},
    {"role": "user", "content": "Inspect the repository structure and list files in src/."}
], tokenize=False, add_generation_prompt=True)

output = generate(
    model,
    tokenizer,
    prompt=prompt,
    max_tokens=512,
    verbose=True
)
print(output)

Training Configuration & Hardware Notes

Hardware: Apple Silicon M5 Max / M4 Max (128 GB Unified Memory).
Optimization: Trained with MLX gradient checkpointing enabled (grad_checkpoint: true) at --max-seq-length 8192 to prevent activation spilling and memory swap.
Intended Use: Autonomous coding workflows, structured API tool invocation, and multi-turn agent execution loops.

True2456/Gemma-4-12B-Agentic-LoRA