Initial commit: Ollama Personal Trainer & Unsloth MoE Orchestrator Studio
This commit is contained in:
drjones
259
studio-ref/src/utils/codeGenerators.ts
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259
studio-ref/src/utils/codeGenerators.ts
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import { BaseModelInfo, GGUFConfig, MoEConfig, PruningConfig, TrainingHyperparameters } from "../types";
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export function generateUnslothPythonScript(
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model: BaseModelInfo,
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params: TrainingHyperparameters,
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ggufConfig: GGUFConfig,
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pruningConfig: PruningConfig,
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customDatasetPath: string = "./dataset.json",
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outputModelName: string = "fine-tuned-ollama-model"
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): string {
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const isDoRA = params.use_dora;
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const isORPO = params.neftune_noise_alpha > 0;
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const targetModulesStr = JSON.stringify(params.target_modules);
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let pruningCode = "";
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if (pruningConfig.enabled && pruningConfig.methods.includes("structured_layer")) {
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pruningCode = `
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# ==========================================
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# ✂️ STRUCTURED LAYER PRUNING (ShortGPT Fat-Shaving)
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# ==========================================
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print(">> Applying structured layer pruning on middle transformer blocks...")
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start_prune, end_prune = ${pruningConfig.layerPruningRange[0]}, ${pruningConfig.layerPruningRange[1]}
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pruned_layers = [i for i in range(model.config.num_hidden_layers) if not (start_prune <= i <= end_prune)]
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model.model.layers = torch.nn.ModuleList([model.model.layers[i] for i in pruned_layers])
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model.config.num_hidden_layers = len(pruned_layers)
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print(f">> Model layers pruned down to {len(pruned_layers)} layers! Shaved ~25% parameter fat.")
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`;
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}
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return `"""
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Ollama Unsloth Studio - State-of-the-Art Fine-Tuning & Quantization Pipeline
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Target Model: ${model.name} (${model.huggingFaceId})
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Hardware Target: NVIDIA RTX 4080 Super (16GB VRAM) / Windows CUDA
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"""
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import os
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import torch
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from unsloth import FastLanguageModel
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from datasets import load_dataset
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from trl import SFTTrainer
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from transformers import TrainingArguments
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# 1. Configuration & Hyperparameters
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max_seq_length = ${params.max_seq_length}
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dtype = None # Auto detection (Float16 / Bfloat16)
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load_in_4bit = True # 4-bit NF4 for max VRAM efficiency on RTX 4080 Super
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print(">> Initializing FastLanguageModel from Unsloth...")
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model, tokenizer = FastLanguageModel.from_pretrained(
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model_name="${model.huggingFaceId}",
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max_seq_length=max_seq_length,
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dtype=dtype,
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load_in_4bit=load_in_4bit,
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)
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${pruningCode}
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# 2. Configure PEFT / LoRA / DoRA Parameters
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print(">> Attaching optimized LoRA adapters...")
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model = FastLanguageModel.get_peft_model(
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model,
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r=${params.lora_r},
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target_modules=${targetModulesStr},
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lora_alpha=${params.lora_alpha},
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lora_dropout=${params.lora_dropout},
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bias="${params.bias}",
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use_gradient_checkpointing="unsloth", # Saves 70% VRAM with zero speed penalty
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random_state=3407,
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use_rslora=${params.use_rslora},
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use_dora=${isDoRA},
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)
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# 3. Format Dataset & Chat Template (Including MCP & Tool-Calling schemas)
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prompt_template = """<|begin_of_text|><|start_header_id|>system<|end_header_id|>
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${ggufConfig.systemPrompt || "You are an expert AI assistant specialized in precise reasoning and MCP tool execution."}<|eot_id|><|start_header_id|>user<|end_header_id|>
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{}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
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{}<|eot_id|>"""
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def formatting_prompts_func(examples):
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instructions = examples.get("instruction", [])
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inputs = examples.get("input", [""] * len(instructions))
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outputs = examples.get("output", [])
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texts = []
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for instruction, input_text, output in zip(instructions, inputs, outputs):
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user_content = f"{instruction}\\n{input_text}".strip() if input_text else instruction
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text = prompt_template.format(user_content, output)
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texts.append(text)
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return { "text" : texts }
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print(f">> Loading training dataset from ${customDatasetPath}...")
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if os.path.exists("${customDatasetPath}"):
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dataset = load_dataset("json", data_files="${customDatasetPath}", split="train")
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dataset = dataset.map(formatting_prompts_func, batched=True)
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else:
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print(">> Notice: Local dataset not found, using demo dataset fallback.")
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from datasets import Dataset
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dataset = Dataset.from_list([
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{"instruction": "Call the filesystem read_file tool on src/App.tsx", "input": "", "output": '{"name": "read_file", "arguments": {"path": "src/App.tsx"}}'}
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]).map(formatting_prompts_func, batched=True)
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# 4. Training Engine Initialization
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trainer = SFTTrainer(
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model=model,
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tokenizer=tokenizer,
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train_dataset=dataset,
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dataset_text_field="text",
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max_seq_length=max_seq_length,
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dataset_num_proc=2,
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packing=False, # True for up to 5x speedup for short sequences
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args=TrainingArguments(
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per_device_train_batch_size=${params.batch_size},
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gradient_accumulation_steps=${params.gradient_accumulation_steps},
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warmup_ratio=${params.warmup_ratio},
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num_train_epochs=${params.epochs},
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learning_rate=${params.learning_rate},
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fp16=not torch.cuda.is_bf16_supported(),
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bf16=torch.cuda.is_bf16_supported(),
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logging_steps=1,
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optim="${params.optimizer}",
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weight_decay=${params.weight_decay},
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lr_scheduler_type="${params.lr_scheduler}",
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seed=3407,
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output_dir="./outputs",
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report_to="none",
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),
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)
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print(">> Starting model training loop...")
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trainer_stats = trainer.train()
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print(">> Training complete! Peak VRAM used:", round(torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024, 3), "GB")
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# 5. Direct GGUF Quantization & Export for Ollama
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print(">> Quantizing and saving GGUF directly for Ollama (${ggufConfig.quantization.toLowerCase()})...")
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model.save_pretrained_gguf(
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"${outputModelName}",
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tokenizer,
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quantization_method="${ggufConfig.quantization.toLowerCase()}"
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)
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# 6. Generate Ollama Modelfile
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modelfile_content = f"""FROM ./${outputModelName}-${ggufConfig.quantization.toLowerCase()}.gguf
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TEMPLATE """ + '"""' + prompt_template + '"""' + f"""
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PARAMETER temperature ${ggufConfig.temperature}
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PARAMETER top_p ${ggufConfig.top_p}
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PARAMETER top_k ${ggufConfig.top_k}
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PARAMETER repeat_penalty ${ggufConfig.repeat_penalty}
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PARAMETER num_ctx ${ggufConfig.contextLength}
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PARAMETER num_gpu ${ggufConfig.num_gpu_layers}
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SYSTEM """ + '"""' + "${ggufConfig.systemPrompt}" + '"""'
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with open("Modelfile", "w") as f:
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f.write(modelfile_content)
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print(">> Modelfile created successfully!")
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print(">> To run in Ollama, execute:")
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print(f" ollama create ${outputModelName} -f Modelfile")
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print(f" ollama run ${outputModelName}")
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`;
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}
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export function generateModelfile(
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model: BaseModelInfo,
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ggufConfig: GGUFConfig,
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modelTag: string = "my-custom-model"
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): string {
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const quantSuffix = ggufConfig.quantization.toLowerCase();
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return `# Modelfile generated by Ollama Unsloth Studio
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# Optimized for RTX 4080 Super (16GB VRAM) & MCP Plugins
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FROM ./${modelTag}-${quantSuffix}.gguf
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# Model Parameters
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PARAMETER temperature ${ggufConfig.temperature}
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PARAMETER top_p ${ggufConfig.top_p}
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PARAMETER top_k ${ggufConfig.top_k}
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PARAMETER repeat_penalty ${ggufConfig.repeat_penalty}
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PARAMETER num_ctx ${ggufConfig.contextLength}
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PARAMETER num_gpu ${ggufConfig.num_gpu_layers}
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# Chat & Tool Template
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TEMPLATE """<|begin_of_text|><|start_header_id|>system<|end_header_id|>
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{{ .System }}<|eot_id|><|start_header_id|>user<|end_header_id|>
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{{ .Prompt }}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
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{{ .Response }}<|eot_id|>"""
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# System Prompt & MCP Tool Declarations
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SYSTEM """${ggufConfig.systemPrompt || "You are an ultra-fast, fine-tuned AI model optimized for local execution and MCP tool harness execution."}"""
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`;
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}
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export function generateWindowsPowerShellScript(
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modelTag: string = "my-custom-model"
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): string {
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return `# Windows RTX 4080 Super One-Click Training & Ollama Deployer
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# PowerShell Script for Windows 10/11 with CUDA 12.x
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Write-Host "=======================================================" -ForegroundColor Cyan
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Write-Host " Ollama Unsloth Studio - Windows RTX 4080 Super Runner" -ForegroundColor Green
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Write-Host "=======================================================" -ForegroundColor Cyan
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# Check Python environment
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if (!(Get-Command python -ErrorAction SilentlyContinue)) {
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Write-Host "[-] Python not found. Please install Python 3.10 or 3.11 with PATH enabled." -ForegroundColor Red
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Exit
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}
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# Ensure Virtual Environment
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if (!(Test-Path "./venv")) {
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Write-Host "[+] Creating virtual environment 'venv'..." -ForegroundColor Yellow
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python -m venv venv
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}
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Write-Host "[+] Activating Virtual Environment..." -ForegroundColor Yellow
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& ./venv/Scripts/Activate.ps1
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Write-Host "[+] Installing/Updating Unsloth & CUDA PyTorch..." -ForegroundColor Yellow
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pip install --upgrade pip
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pip install "unsloth[cu121-ampere-torch240] @ git+https://github.com/unslothai/unsloth.git"
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pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121
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pip install bitsandbytes trl peft datasets transformers xformers
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Write-Host "[+] Starting Unsloth Fine-Tuning..." -ForegroundColor Green
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python train_unsloth.py
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if (Test-Path "./Modelfile") {
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Write-Host "[+] Building Ollama Model '${modelTag}'..." -ForegroundColor Green
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ollama create ${modelTag} -f Modelfile
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Write-Host "[+] SUCCESS! Model is registered in Ollama." -ForegroundColor Cyan
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Write-Host ">> Launching test session with Ollama..." -ForegroundColor Green
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ollama run ${modelTag} "Hello! Check your MCP tool calling capabilities."
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} else {
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Write-Host "[-] Modelfile not generated. Please check training logs." -ForegroundColor Red
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}
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`;
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}
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export function generateMergeKitConfig(moeConfig: MoEConfig, baseModel: string): string {
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if (moeConfig.method === "moefication") {
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return `base_model: ${baseModel}
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gate_mode: ${moeConfig.routerType}
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dtype: bfloat16
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experts:
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${moeConfig.expertSources.map((exp) => ` - source_model: ${exp.modelId}
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positive_prompts:
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- "${exp.specialization}"
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parameters:
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weight: ${exp.weight}`).join("\n")}
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`;
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}
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return `merge_method: ${moeConfig.method}
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base_model: ${baseModel}
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models:
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${moeConfig.expertSources.map((exp) => ` - model: ${exp.modelId}
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parameters:
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weight: ${exp.weight}
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density: 0.2`).join("\n")}
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dtype: bfloat16
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`;
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}
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