Brello Thinking

Model Description

Brello Thinking is an advanced language model created by Epic Systems as a part of Brello AI Family. Built on the robust Tencent Hunyuan base model, Brello Thinking specializes in deep reasoning, mathematical problem-solving, coding, and creative thinking with enhanced chain-of-thought capabilities.

Key Features

Advanced Reasoning: Enhanced chain-of-thought with both fast and slow thinking modes
Mathematical Excellence: Superior at math and symbolic computation
Programming Prowess: Strong coding abilities across Python, JS, C++, SQL, and more
Long Context Understanding: Handles up to 256K tokens, long docs, and codebases
Creative Problem Solving: Generates new solutions and approaches
Multi-language Support: Fluent in English and Chinese, robust cross-lingual transfer

1. Executive Summary

Brello Thinking v1.1.0 (2025-08-07) is a 1.8B-parameter causal language model engineered for complex reasoning, mathematics, and creative tasks. It combines ultra-long context, dual “fast”/“deep” thinking modes, and a plugin SDK for live tool integration. It is designed for safe, sustainable, and fair production deployments.

Highlights in this Release

Mixed-precision quantization (BF16 & INT8)
Plugin SDK (JSON-RPC, HMAC auth, dynamic tool routing)
Monitoring (Prometheus, Grafana, carbon tracking)
Sustainability Dashboard (gCO₂eq/token metrics, CodeCarbon SDK)

2. Model Architecture

Component	Specification
Base Model	Tencent Hunyuan / EpicBrelloV1ForCausalLM
Parameters	1.8B (BF16/INT8 quantization; LoRA adapters optional)
Context Window	256,000 tokens (rotary cache, sliding window, eviction logic)
Attention	Grouped-Query + Multi-Head FlashAttention (16 heads, 4 KV heads)
Feed-Forward	Two-stage (SiLU → Linear → SiLU) with RMSNorm, hidden size 6144
Depth	32 transformer blocks + 4 “Safety Adapter” blocks
Adapters	LoRA for math, code, creative, and domain fine-tuning (10–18M params each)
Inference Modes	Autoregressive sampling (top-k, top-p), beam, contrastive decoding
Sharding	ZeRO-3 / tensor-parallel / model-parallel combinations

3. Training & Tuning

3.1 Pretraining Corpus

Web General: 400B tokens (CommonCrawl, CC-100, curated news)
Science/Technical: 50B tokens (arXiv, PubMed, patents)
Code: 20B tokens (public GitHub, CodeSearchNet, MBPP)
Multilingual: 30B tokens (Chinese, Spanish, German, Arabic)
Augmentations: 15% span corruption, zh–en back-translation, dynamic masking

3.2 Optimization

Optimizer: AdamW (β₁=0.9, β₂=0.95, weight_decay=0.01)
LR Schedule: Linear warmup (10K steps), cosine decay (500K steps)
Batch: 2M tokens/step, grad accumulation ×8

3.3 Instruction/RLHF Tuning

Instruction Pairs: 1.2M human-annotated QA/reasoning
Reward Model: Dual human-preference ranking (5K raters, Elo)
Algorithm: PPO w/ KL penalty (target KL=0.1), reward clipping

4. Specialized Modules

Adapter Name	Data Source	Params (M)	Use Case
math-adapter	GSM8K, MATH, AIME datasets	12	Math proof, step-by-step logic
code-adapter	MBPP, MultiPL-E, GitHub repos	18	Coding, debugging, codegen
creative-adapter	Gutenberg, story corpora	10	Narrative, dialogue, ideation

5. Plugin & Tooling SDK

Interface: JSON-RPC (Unix socket or REST), HMAC-SHA256 auth
Plugins:
- DB connectors: PostgreSQL, MySQL, Snowflake
- HTTP client: retry/backoff
- Vector DB: FAISS, Pinecone

Tool Call Example

Model emits:

{"tool_call": {"name": "weather_fetch", "args": {"location":"Mumbai"}}}

Host executes plugin, returns:

{"tool_result": {"forecast":"Sunny, 32°C"}}

Model resumes reasoning with tool result in context.

6. Inference, Monitoring & Scaling

6.1 Endpoint Performance

Mode	Batch	Seq Len	Throughput (tok/s)	Latency (p50)
Fast-Think	8	4,096	250,000	15 ms
Deep-Think	1	256,000	18,000	120 ms
INT8 Quant	16	2,048	320,000	12 ms

6.2 Observability

Prometheus Metrics:
- brello_inference_latency_seconds
- brello_generated_tokens_total
- brello_cache_evictions_total
Grafana:
- Token latency histograms, CO₂ per generation

7. Sustainability & Carbon Tracking

Data Center PUE: 1.2
Carbon Emission: ~0.0008 gCO₂eq/token (tracked with CodeCarbon)
Offset: Epic Systems funds VER 2.0 credits

8. Robustness, Safety & Fairness

Adapters: Real-time adversarial input filtering, personal data redaction, toxicity classifier (fine-tuned BERT-tox)
Bias Audits:
- Toxicity variation <1.8% (12 demographic axes)
- Gender parity ±2%
- Dialect coverage 98% (EN & ZH)

9. Interpretability

Chain-of-Thought logs: Token-level reasoning trace
Integrated Gradients: Span attribution
Attention Rollouts: Layer-wise visualization (custom plugin)

10. Hyperparameters

Parameter	Value
num_layers	32
d_model	2048
d_hidden	6144
num_heads	16
kv_heads	4
rotary_pct	0.25
lr_warmup_steps	10,000
weight_decay	0.01
batch_size	2M
dropout_rate	0.1

11. Evaluation & Error Analysis

Benchmarks: GSM8K, MBPP, BBH, LongBench, MATH
Analysis: Math/logic confusion matrix, hallucination drift cluster analysis

12. Roadmap

Version	Highlights	ETA
v1.1.0	Plugins, carbon tracking, INT8 quantization	Released
v1.2.0	Vision-language, adapter expansion	Nov 2025
v1.3.0	Audio, multilingual tuning	Feb 2026
v2.0	Federated RAG, continuous learning	Q4 2026

13. Licensing & Compliance

License: Proprietary, Epic Systems
Privacy: GDPR, CCPA compliant
Certifications: ISO 27001, SOC 2 Type II, HIPAA (BAA on request)
Restrictions: No redistribution or large-scale rehosting

14. Usage Example

import os
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel   # For LoRA adapters
from brello_sdk import BrelloPluginManager  # Hypothetical SDK
from codecarbon import EmissionsTracker
from prometheus_client import CollectorRegistry, Counter, Histogram, push_to_gateway

def setup_model(
    model_id: str = "BrelloES/brello-thinking",
    use_bf16: bool = True,
    load_int8: bool = True,
):
    tokenizer = AutoTokenizer.from_pretrained(model_id)
    model = AutoModelForCausalLM.from_pretrained(
        model_id,
        device_map="auto",
        torch_dtype=torch.bfloat16 if use_bf16 else torch.float32,
        load_in_8bit=load_int8,
    )
    # Attach LoRA adapters
    model = PeftModel.from_pretrained(model, "adapters/math-adapter")
    model = PeftModel.from_pretrained(model, "adapters/code-adapter")
    return tokenizer, model

def setup_plugins():
    pm = BrelloPluginManager()
    pm.register(
        name="weather_fetch",
        path="/opt/brello/plugins/weather_plugin.so",
        auth_key=os.getenv("WEATHER_PLUGIN_KEY", "CHANGE_ME"),
    )
    pm.register(
        name="db_query",
        path="/opt/brello/plugins/db_query_plugin.so",
        auth_key=os.getenv("DB_PLUGIN_KEY", "CHANGE_ME"),
    )
    return pm

def setup_metrics():
    registry = CollectorRegistry()
    Histogram(
        "brello_inference_latency_seconds",
        "Inference latency (seconds) per request",
        registry=registry,
        buckets=(0.01, 0.05, 0.1, 0.2, 0.5, 1.0),
    )
    Counter(
        "brello_generated_tokens_total",
        "Total number of tokens generated by Brello",
        registry=registry,
    )
    return registry

def generate_response(tokenizer, model, plugin_mgr, registry, messages, mode: str = "deep"):
    inputs = tokenizer.apply_chat_template(
        messages,
        tokenize=True,
        add_generation_prompt=True,
        enable_thinking=True if mode == "deep" else False,
    )
    tracker = EmissionsTracker(project_name="brello_inference", output_dir="carbon_logs")
    tracker.start()
    # (Metrics update simplified for clarity)
    outputs = model.generate(
        inputs.to(model.device),
        max_new_tokens=512,
        top_p=0.9,
        temperature=0.6,
        plugin_manager=plugin_mgr,
        return_dict_in_generate=True,
        output_scores=True,
    )
    emissions_kg = tracker.stop()
    text = tokenizer.decode(outputs.sequences[0], skip_special_tokens=True)
    return text, emissions_kg

def main():
    tokenizer, model = setup_model()
    plugin_mgr = setup_plugins()
    registry = setup_metrics()
    messages = [
        {"role": "system", "content": "You are Brello Thinking in Deep-Think mode."},
        {"role": "user", "content": "Explain why prime factorization is unique."},
    ]
    response, co2 = generate_response(tokenizer, model, plugin_mgr, registry, messages, mode="deep")
    print("=== Deep-Think Output ===\n", response)
    print(f"CO₂ Emitted: {co2:.6f} kg")
    # Fast-Think comparison
    messages[0]["content"] = "You are Brello Thinking in Fast-Think mode."
    response_fast, co2_fast = generate_response(tokenizer, model, plugin_mgr, registry, messages, mode="fast")
    print("\n=== Fast-Think Output ===\n", response_fast)
    print(f"CO₂ Emitted: {co2_fast:.6f} kg")

if __name__ == "__main__":
    main()

Otvd

Creator: Epic Systems
Engineer: Rehan Temkar
Model: Brello Thinking v1.0.0

Brello Thinking - Advanced AI Reasoning by Epic Systems

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Safetensors

Model size

2B params

Tensor type

BF16