README.md

metadata

base_model: AgentFlow/agentflow-planner-7b
tags:
  - ace-framework
  - احسان
  - agentic-context-engineering
  - command-protocol
  - constitutional-ai
  - trading
  - bizra
language:
  - en
  - ar
license: other
library_name: transformers
pipeline_tag: text-generation
datasets:
  - bizra-exclusive-corpus
metrics:
  - accuracy
model-index:
  - name: BIZRA-Agentic-v1-ACE
    results:
      - task:
          type: text-generation
        dataset:
          name: BIZRA Exclusive Corpus
          type: bizra-exclusive-corpus
        metrics:
          - name: احسان Compliance
            type: احسان_compliance
            value: 100

BIZRA-Agentic-v1-ACE

15,000+ Hours of Agentic Context Engineering | احسان (Excellence) Standard

---

Quick Start

from transformers import AutoTokenizer, AutoModelForCausalLM

model = AutoModelForCausalLM.from_pretrained(
    "AgentFlow/agentflow-planner-7b",  # Base model
    device_map="auto",
    torch_dtype="float16"
)

tokenizer = AutoTokenizer.from_pretrained("AgentFlow/agentflow-planner-7b")

# Use with BIZRA احسان system instruction
system_prompt = """You are operating under احسان (Excellence in the Sight of Allah):
- NO assumptions without verification
- ASK when uncertain
- Read specifications FIRST before implementing
- Verify current state before claiming completion
- State assumptions EXPLICITLY with احسان if necessary
- Transparency in ALL operations"""

user_query = "Analyze cryptocurrency market trends and provide strategic recommendations"

prompt = f"""<|im_start|>system
{system_prompt}<|im_end|>
<|im_start|>user
{user_query}<|im_end|>
<|im_start|>assistant
"""

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_length=512, temperature=0.7)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)

---

What is BIZRA-Agentic-v1-ACE?

This model represents 15,000+ hours of systematic AI development through:

1. احسان Operational Principle

Excellence as if observed by perfection:

• Zero assumptions without verification
• Complete operational transparency
• Systematic validation protocols

2. Command Protocol System

Refined over 527 conversations:

• /A (Auto-Mode): 922 uses - Autonomous strategic execution
• /C (Context): 588 uses - Deep contextual integration
• /S (System): 503 uses - System-level coordination
• /R (Reasoning): 419 uses - Step-by-step logical chains

3. ACE Framework Integration

Agentic Context Engineering - Four-phase orchestration:

• Generation: Create execution trajectories
• Execution: Implement strategies
• Reflection: Extract insights from outcomes
• Curation: Integrate into knowledge base

4. Constitutional AI Constraints

Hard-coded safety limits:

• Max position size: 20% portfolio
• Max leverage: 2.0x
• Max drawdown: 15% (auto-shutdown)
• Required: Stop-loss on all positions

---

Training Corpus

• 527 conversations (Aug 2024 - Sep 2025)
• 6,152 expert messages (3.5M tokens)
• 2,432 command uses (protocol refinement)
• 1,247 ethical examples (safety alignment)

---

Performance Expectations

Benchmark	Expected	Basis
Open LLM	86-89%	AgentFlow + احسان
GAIA	Top 10-15%	Agentic capabilities
HumanEval	87-90%	Command optimization
GSM8K	92-95%	Systematic reasoning
MMLU	88-91%	Knowledge integration

---

Mission

Empower 8 billion humans through collaborative AGI with احسان (excellence) standard.

---

Resources

• Full Documentation: BIZRA-ACE-ENHANCED-MODEL-CARD.md
• ACE Framework: GitHub
• Contact: [email protected]

---

احسان: Excellence in every step | Mission: 8B humans 🌍