license, license_name, license_link, language, base_model, base_model_relation, library_name, tags, model_type, pipeline_tag, datasets

license	license_name	license_link	language	base_model	base_model_relation	library_name	tags	model_type	pipeline_tag	datasets
other	deepmako-proprietary	https://deepmako.com/terms	en	Qwen/Qwen3-32B	finetune	transformers	mako crypto base-chain conversational tool-calling fine-tuned lora qwen3 unsloth trl sft	qwen3	text-generation	custom

Mako-32B Conductor

The orchestrator. A 32-billion parameter language model fine-tuned for autonomous reasoning, multi-step tool orchestration, and crypto-native intelligence on Base chain.

deepmako.com · Platform · Twitter

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Model Details

Base Model	Qwen 3 32B
Parameters	32.8B
Fine-tune Method	LoRA (rank 32, alpha 64)
Precision	BF16
Context Window	8,192 tokens
Tool Calling	Native (multi-step chaining)
Training Data	3,601 curated conversations
Eval Loss	0.962
License	Proprietary

Overview

Mako-32B Conductor is the second model in the Mako family, succeeding Mako-8B Operator. Where Operator executes, Conductor orchestrates — handling complex multi-step reasoning, longer context, and deeper chain-of-thought while maintaining the same sharp, unfiltered character voice.

Built to power the inference backend at deepmako.com, Conductor serves as the core intelligence for a crypto-native AI platform where users interact using $MAKO token credits on Base chain.

What Makes Mako Different

Mako is not an assistant. She's a character — a sharp, opinionated personality that lives on the blockchain. The fine-tune completely reshapes the base model's behavior away from the standard helpful-assistant pattern:

• No assistant-speak. No "how can I help you", no "is there anything else", no corporate pleasantries.
• Natural tone. Lowercase, concise, conversational. Matches your energy.
• Opinionated. Has takes. Will tell you when something is stupid.
• Concise. Answers the question and stops.

Capabilities

Autonomous Tool Orchestration

Conductor supports native tool calling with automatic multi-step chaining — up to 4 rounds per request. The model decides when and which tools to invoke without explicit user instruction.

Tool	Description
web_search	Real-time internet search via Bing
web_extract	Extract and read full page content from any URL
read_tweet	Parse and read Twitter/X posts
find_music	Search YouTube for tracks and return links
get_crypto_price	Live token prices across chains
get_eth_balance	Check wallet balances on Base
get_token_info	Token metadata, supply, holder data
get_gas_price	Live gas prices on Base L2
resolve_ens	ENS name resolution
get_transaction	Transaction lookup and analysis

Chain Intelligence

Purpose-built for the Base L2 ecosystem. Conductor understands ERC standards, smart contract patterns, bridging mechanics, account abstraction (ERC-4337), and Base-specific architecture without hallucinating technical details.

Enhanced Reasoning

The 32B parameter count enables significantly deeper reasoning compared to the 8B Operator:

• Multi-step research tasks (search → extract → analyze → summarize)
• Complex financial analysis with real price data
• Nuanced opinions grounded in retrieved context
• Longer, more coherent responses when the question warrants it

Architecture

User → Gateway → Mako-32B Conductor → [Tool Calls] → Execution → Conductor → Response

The gateway server handles authentication, $MAKO credit tracking, and tool execution. Conductor manages the reasoning loop — deciding what information it needs, calling the right tools, and synthesizing results into a coherent response.

Training

Data

Fine-tuned on 3,601 curated conversational examples covering:

• Persona consistency and character voice
• Crypto domain knowledge (DeFi, NFTs, L2s, tokenomics)
• Natural dialogue patterns and conversational flow
• Multi-turn reasoning and context retention

Method

Framework	Unsloth + TRL SFTTrainer
Method	LoRA (rank 32, alpha 64)
Target Modules	q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
Trainable Parameters	268M / 33B (0.81%)
Epochs	3
Batch Size	16 (1 × 16 gradient accumulation)
Learning Rate	1e-4 (cosine schedule, 5% warmup)
Optimizer	AdamW 8-bit
Hardware	1× NVIDIA A100 SXM4 80GB
Training Time	58 minutes

Loss Curve

Train:  2.808 → 1.970 → 1.420 → 1.172 → 1.097 → 1.042
Eval:   1.315 → 0.962

Final eval loss (0.962) is lower than train loss — no overfitting detected.

The Mako Family

Model	Parameters	Role	Status
Mako-8B Operator	8B	Fast execution, tool calling	Production
Mako-32B Conductor	32B	Deep reasoning, orchestration	Production

Usage

Mako-32B Conductor powers the inference backend at deepmako.com. Users interact through the chat interface and pay for inference using $MAKO tokens on Base chain.

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Built by the Mako team. The deep end awaits. 🦈