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brello-thinking

Text Generation
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English
reasoning
mathematics
programming
creative-writing
chain-of-thought
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metadata 
license: mit
language:
  - en
library_name: transformers
pipeline_tag: text-generation
tags:
  - reasoning
  - mathematics
  - programming
  - creative-writing
  - chain-of-thought
  - interpretability
  - fairness
  - security
  - deployment
  - sustainability
  - monitoring
  - plugin

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

  1. Model emits:
    {"tool_call": {"name": "weather_fetch", "args": {"location":"Mumbai"}}}
  2. Host executes plugin, returns:
    {"tool_result": {"forecast":"Sunny, 32°C"}}
  3. 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