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language:
- en
- my
license: apache-2.0
tags:
- business-intelligence
- sme
- myanmar
- diagnosis
- text-generation
- llama
- fine-tuned
- bios
- gold-shop
- southeast-asia
datasets:
- BIOS-kernel/myanmar-sme-diagnostics-v1
base_model: meta-llama/Llama-3.3-70B-Instruct
pipeline_tag: text-generation
model_type: causal-lm
widget:
- text: "Diagnose this business: Gold Shop in Yangon, 4.2M MMK monthly revenue, 28% retention rate, team of 3."
example_title: "Gold Shop Diagnosis"
- text: "What are the top growth opportunities for a Fashion business with 8M MMK revenue in Mandalay?"
example_title: "Fashion Growth Opportunities"
---
<div align="center">
```
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
โ โ
โ โโโโโโโ โโโ โโโโโโโ โโโโโโโโ โ
โ โโโโโโโโโโโโโโโโโโโโโโโโโโโโ โ
โ โโโโโโโโโโโโโโ โโโโโโโโโโโ โ
โ โโโโโโโโโโโโโโ โโโโโโโโโโโ โ
โ โโโโโโโโโโโโโโโโโโโโโโโโโโโโ โ
โ โโโโโโโ โโโ โโโโโโโ โโโโโโโโ โ
โ โ
โ Business Idea Operating System โ
โ BIOS-Insight-v1 ยท Kernel: BIOS-kernel-v1 โ
โ โ
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
```
**"We don't just analyse businesses. We illuminate them."**
[](LICENSE)
[](.)
[](.)
[](https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct)
[](.)
</div>
---
# BIOS-Insight-v1 โ Business Idea Operating System
## ๐ฌ๐ง English
### Model Description
**BIOS-Insight-v1** is a fine-tuned large language model built on **LLaMA 3.3 70B Instruct**, specifically trained to serve as the intelligence core of the **Business Idea Operating System (BIOS)** โ a comprehensive AI agent designed for Myanmar's small and medium enterprises (SMEs), Gold Shops, fashion retailers, F&B operators, and the next generation of Southeast Asian entrepreneurs.
BIOS is not a chatbot. It is an **Operating System for business ideas** โ the same way Windows runs your computer, BIOS runs your business strategy. Every question answered, every weakness surfaced, every opportunity ranked: all orchestrated by a single intelligent kernel.
This model powers **Module 1: Business Diagnosis Engine**, the foundational layer of the BIOS platform. Feed it 24 structured questions about any business, and it returns a complete, actionable diagnosis in under 60 seconds.
---
### Architecture & Training
| Property | Details |
|----------|---------|
| **Base Model** | `meta-llama/Llama-3.3-70B-Instruct` |
| **Fine-tune Method** | QLoRA (4-bit quantisation, rank 64) |
| **Training Data** | Myanmar SME diagnostics, Gold Shop patterns, SEA business benchmarks |
| **Context Length** | 8,192 tokens |
| **Output Format** | Structured JSON โ deterministic, parseable |
| **Languages** | English, Burmese (แแผแแบแแฌแแฌแแฌ) |
| **Quantisation** | GGUF Q4_K_M available for local inference |
---
### What BIOS Produces
Given structured business inputs, BIOS-Insight-v1 generates:
```json
{
"health_score": 47,
"health_label": "Fair",
"health_dimensions": {
"revenue_strength": 40,
"customer_retention": 20,
"market_position": 60,
"technology_adoption": 30,
"growth_trajectory": 80
},
"top_3_weaknesses": [
{
"rank": 1,
"label": "Customer Retention",
"your_score": 20,
"benchmark": 60,
"gap": 40,
"severity": "HIGH",
"detail": "Only 28% repeat purchase rate โ Gold Shop industry average is 60%."
}
],
"growth_opportunities": [
{
"rank": 1,
"title": "Boost Customer Retention Rate",
"expected_impact": "+1,680,000 MMK estimated monthly revenue",
"difficulty": "MEDIUM",
"timeframe": "2โ3 months"
}
],
"priority_action_items": [
{
"priority": 1,
"action": "Launch a loyalty stamp card and 30-day WhatsApp follow-up sequence.",
"composite_score": 82.0
}
],
"ai_narrative": "Shwe Zin Gold & Jewellery is operating at 47/100 health โ a Fair rating that conceals a serious retention gap..."
}
```
---
### Health Score Formula
The BIOS Health Score is calculated across five equally-weighted dimensions:
```
Health Score = (Revenue Strength ร 20%) +
(Customer Retention ร 20%) +
(Market Position ร 20%) +
(Technology Adoption ร 20%) +
(Growth Trajectory ร 20%)
Where each dimension is scored 0โ100.
Maximum Score: 100
```
| Score Range | Label | Interpretation |
|------------|-------|---------------|
| 80 โ 100 | ๐ข Excellent | Best-in-class. Scale aggressively. |
| 65 โ 79 | ๐ต Good | Strong foundation. Focus on 1โ2 gaps. |
| 45 โ 64 | ๐ก Fair | Visible weaknesses. Targeted fixes needed. |
| 30 โ 44 | ๐ Below Average | Systemic issues. Restructure required. |
| 0 โ 29 | ๐ด Critical | Immediate intervention. Prioritise survival. |
---
### Intended Use
#### โ
Primary Use Cases
- **Myanmar Gold Shops & Jewellers** โ the lifeblood of Myanmar's retail economy, underserved by digital tools
- **Fashion & Apparel SMEs** โ fast-moving businesses in Yangon, Mandalay, Naypyidaw
- **F&B Operators** โ restaurants, tea shops, catering businesses
- **Cosmetics & Beauty Brands** โ direct-to-consumer Myanmar brands scaling up
- **Electronics Retailers** โ high-value, low-margin businesses needing operational precision
- **Any Myanmar SME founder** who wants strategic clarity without a consultant's fee
#### โ Out-of-Scope Uses
- Large corporations (BIOS is tuned for SME scale and context)
- Non-business tasks (general Q&A, creative writing)
- Legal or financial advice (BIOS provides business intelligence, not regulated advisory)
---
### How to Use
#### With the `transformers` Library
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "BIOS-kernel/BIOS-Insight-v1"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype=torch.bfloat16,
device_map="auto",
)
system_prompt = """You are BIOS โ the Business Idea Operating System.
You are the elite AI advisor for Myanmar SMEs.
Always respond in valid JSON with health_score, top_3_weaknesses,
growth_opportunities, and priority_action_items."""
user_prompt = """Diagnose this business:
Business: Shwe Zin Gold & Jewellery | Industry: Gold Shop | Location: Yangon
Monthly Revenue: 4,200,000 MMK | Retention Rate: 28% | Team: 3 people
USP: Certified 99.9% pure gold with 10-year buyback guarantee
Pain Point: No customer follow-up system. Customers don't return.
12-Month Goal: 12,000,000 MMK
Marketing Budget: 80,000 MMK/month"""
messages = [
{"role": "system", "content": system_prompt},
{"role": "user", "content": user_prompt},
]
input_ids = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt",
).to(model.device)
output = model.generate(
input_ids,
max_new_tokens=1024,
temperature=0.3,
do_sample=True,
pad_token_id=tokenizer.eos_token_id,
)
response = tokenizer.decode(
output[0][input_ids.shape[-1]:],
skip_special_tokens=True,
)
print(response)
```
#### With the BIOS Controller (Recommended)
```python
from bios_controller import BIOSController, BusinessInputs, ModelBackend
# Initialise
controller = BIOSController(
backend = ModelBackend.GROQ, # or HF_INFERENCE when BIOS-Insight-v1 is live
save_to_db = True, # persist to NeonDB
)
# Fill in the 24 business questions
inputs = BusinessInputs(
business_name = "Shwe Zin Gold & Jewellery",
industry = "Gold Shop",
location = "Yangon",
years_in_business = 7,
monthly_revenue = 4_200_000,
team_size = 3,
target_customer = "Middle-income families, 30โ55, buying gold for investment and festivals",
acquisition_channels = ["Word-of-mouth", "Facebook", "Walk-in"],
avg_customer_lifetime_value= 350_000,
retention_rate = 28.0,
main_competitors = "Dagon Gold, KBZ Gems",
unique_selling_proposition = "Certified 99.9% gold. Transparent pricing. 10-year buyback guarantee.",
sales_channels = ["Physical Store", "Facebook"],
operational_challenge = "Inventory management",
biggest_pain_point = "No system to follow up with customers after first purchase.",
current_technology = ["Spreadsheets"],
marketing_channels = ["Facebook", "Word-of-mouth"],
monthly_marketing_budget = 80_000,
goal_3_month = 5_500_000,
goal_6_month = 7_000_000,
goal_12_month = 12_000_000,
budget_constraint = "Tight (50-200K)",
tech_readiness = "Somewhat ready",
preferred_language = "English",
)
# Run the full diagnosis pipeline
report = controller.run_diagnosis(inputs)
# Access structured results
print(f"Health Score : {report.health_score}/100 ({report.health_label})")
print(f"Top Weakness : {report.top_3_weaknesses[0].label}")
print(f"Best Opportunity : {report.growth_opportunities[0].title}")
print(f"\nAI Narrative:\n{report.ai_narrative}")
# Full JSON output
print(report.to_json())
```
#### With HuggingFace Inference API
```python
from huggingface_hub import InferenceClient
client = InferenceClient(
model = "BIOS-kernel/BIOS-Insight-v1",
token = "hf_your_token_here",
)
response = client.chat_completion(
messages=[
{"role": "system", "content": "You are BIOS. Respond in JSON."},
{"role": "user", "content": "Diagnose: Gold Shop, 4.2M MMK revenue, 28% retention."},
],
max_tokens = 1024,
temperature = 0.3,
)
print(response.choices[0].message.content)
```
---
### Switching Models (Base vs Fine-tuned)
```python
controller = BIOSController(backend=ModelBackend.GROQ)
# Use base LLaMA-3.3-70B (default, available now)
report_base = controller.run_diagnosis(inputs)
# Switch to BIOS-Insight-v1 once published on HuggingFace
controller.switch_to_bios_insight()
report_bios = controller.run_diagnosis(inputs)
# Switch back to base
controller.switch_to_base()
```
---
### NeonDB Integration
```python
import os
os.environ["DATABASE_URL"] = "postgresql://user:pass@ep-xxx.neon.tech/neondb?sslmode=require"
controller = BIOSController(save_to_db=True)
report = controller.run_diagnosis(inputs)
# Retrieve saved report
saved = controller.get_report(report.session_id)
# List all diagnoses
history = controller.list_reports(limit=10)
```
---
### Limitations
- Benchmarks are calibrated for Myanmar market (MMK currency, Yangon/Mandalay/Naypyidaw context)
- Growth projections are estimates, not guarantees โ market conditions vary
- The model does not access real-time data or the internet
- Legal and financial decisions should always be reviewed by qualified professionals
---
### Citation
```bibtex
@misc{bios-insight-v1,
title = {BIOS-Insight-v1: Business Idea Operating System for Myanmar SMEs},
author = {BIOS-kernel},
year = {2026},
howpublished = {\url{https://huggingface.co/BIOS-kernel/BIOS-Insight-v1}},
note = {Fine-tuned on LLaMA 3.3 70B Instruct for Myanmar business diagnostics}
}
```
---
---
## ๐ฒ๐ฒ แแผแแบแแฌแแฌแแฌ (Burmese)
### แแฑแฌแบแแแบแแฑแฌแบแแผแแปแแบ
**BIOS-Insight-v1** แแแบ **LLaMA 3.3 70B Instruct** แแญแฏ แกแแผแฑแแถแ fine-tune แแผแฏแแฏแแบแแฌแธแแฑแฌ AI แแฑแฌแบแแแบแแ
แบแแฏแแผแ
แบแแผแฎแธแ แแผแแบแแฌแแญแฏแแบแแถแ SME (แกแแฑแธแ
แฌแธแแพแแทแบ แกแแแบแ
แฌแธแแฏแแบแแแบแธแแปแฌแธ) โ แแฝแพแฑแแญแฏแแบแแปแฌแธแ แแแบแแพแแบแแญแฏแแบแแปแฌแธแ แ
แฌแธแแฑแฌแแบแแญแฏแแบแแปแฌแธแ แแพแแทแบ แแฑแฌแแบแแฌแแแทแบ Southeast Asia แ แแฏแแบแแแบแธแแพแแบแแปแฌแธแกแแฝแแบ **Business Idea Operating System (BIOS)** แ AI แกแแญแแกแแบแแปแแบแกแแผแ
แบ แแฎแแญแฏแแบแธแแฏแแบแแฌแธแแแบแ
BIOS แแแบ chatbot แแ
แบแแฏแแแฏแแบแแซแ แแแบแธแแแบ **แแแบแแแฏแแบแแแบแธแกแแผแถแฅแฌแแบแแปแฌแธแกแแฝแแบ Operating System** แแ
แบแแฏแแผแ
แบแแแบ โ Windows แ แแแบแแแฝแแบแแปแฐแแฌแแญแฏ run แแแฒแทแแญแฏแทแ BIOS แ แแแบแแแฏแแบแแแบแธแแปแฐแแฌแแญแฏ run แแแบแ แแฑแธแแฌแธแแฑแฌแแฑแธแแฝแแบแธแแญแฏแแบแธแ แแฑแฌแบแแฏแแบแแฑแฌ แกแฌแธแแแบแธแแปแแบแแญแฏแแบแธแ แกแแแทแบแแแบแแพแแบแแฌแธแแฑแฌ แกแแฝแแทแบแกแแแบแธแแญแฏแแบแธ โ แแฑแฌแแบแแฝแแบแแพแฏแกแฌแธแแฏแถแธแแญแฏ AI kernel แแ
แบแแฏแแแบแธแแผแแทแบ แแแบแธแแฝแพแแบแแแบแ
---
### แแแบแแฝแแบแแฑแฌแกแแฏแถแธแแผแฏแแแบแแแบ
BIOS-Insight-v1 แแญแฏ แกแฑแฌแแบแแซแแฏแแบแแแบแธแแปแฌแธแกแแฝแแบ แกแแฐแธแแแทแบแแฑแฌแบแแแบ:
**โ
แกแแญแแกแแฏแถแธแแผแฏแแแบแแแบแแปแฌแธ**
- ๐ฅ **แแผแแบแแฌแแฝแพแฑแแญแฏแแบแแปแฌแธแแพแแทแบ แแแบแแแบแแแแฌแแญแฏแแบแแปแฌแธ** โ แแผแแบแแฌแทแแแบแแฎแแฏแแบแแผแฑแฌแแบแ
แฎแธแแฝแฌแธแแฑแธแ แกแแแบแแผแฑแฌแแผแ
แบแแฑแฌ แแญแฏแแบแแปแฌแธ
- ๐ **แแแบแแพแแบแแพแแทแบ แกแแแบแกแแแบ SME แแปแฌแธ** โ แแแบแแฏแแบแ แแแนแแแฑแธแ แแฑแแผแแบแแฑแฌแบแแพแญ แแญแฏแแบแแปแฌแธ
- ๐ **F&B แแฏแแบแแแบแธแแปแฌแธ** โ แ
แฌแธแแฑแฌแแบแแญแฏแแบแ แแแบแแแบแแแบแแญแฏแแบแ Catering แแฏแแบแแแบแธแแปแฌแธ
- ๐ **แแพแแแฑแธแแพแแทแบ แแฑแฌแแบแแฎแแ
แบแ Brand แแปแฌแธ** โ แแผแแบแแฌ DTC Brand แแปแฌแธ
- ๐ฑ **Electronics แแญแฏแแบแแปแฌแธ** โ แแฏแแบแแ
แนแ
แแบแธแแแบแแญแฏแธแแผแแทแบแแฑแฌแ margin แแแบแธแแฑแฌแแฏแแบแแแบแธแแปแฌแธ
- ๐ข **แแผแแบแแฌ SME แแแบแแฑแฌแแบแแฐแแปแฌแธ** โ consultant แฆแธแ
แฑแฌแแบแแผแฑแธแแแฑแธแแฒ แแปแฐแแฌแแญแฏ แแพแแบแธแแแบแธแ
แฑแแญแฏแแฐแแปแฌแธ
---
### BIOS แ แแปแแบแธแแฌแแฑแธแแแพแแบแแฑแฌแบแแผแฐแแฌ
BIOS Health Score แแญแฏ แแฎแแปแพแแฑแฌแกแแปแญแแบแแปแญแแบแแฌแธแแฑแฌ แแแนแ แ
แแฏแแผแแทแบ แแฝแแบแแปแแบแแแบ:
```
Health Score = (แแแบแแฝแฑแแญแฏแแบแแถแทแแพแฏ ร แแ%) +
(แแฑแฌแแบแแแบแแแบแแแบแแฑแฌแแบแแฝแแบแแพแฏ ร แแ%) +
(แแฑแธแแฝแแบแแฝแแบแแฑแแฌ ร แแ%) +
(แแแบแธแแแฌแแญแฏแแบแแฌแแฏแถแธแ
แฝแฒแแพแฏ ร แแ%) +
(แแญแฏแธแแแบแแพแฏแแแบแธแแญแฏแแบ ร แแ%)
แกแแผแแทแบแแฏแถแธแแแพแแบ: แแแ
```
| แแแพแแบ | แกแแพแแบแแถแแญแแบ | แกแแญแแนแแซแแบ |
|------|------------|---------|
| แแโแแแ | ๐ข แแฐแธแแผแฌแธแแฑแฌแแบแธแแฝแแบแแฑแฌ | แแแนแ แกแแฑแฌแแบแธแแฏแถแธแ แแญแฏแธแแปแฒแทแแซแ |
| แแ
โแแ | ๐ต แแฑแฌแแบแธแแฝแแบแแฑแฌ | แแญแฏแแบแแฌแแฑแฌแกแแผแฑแแถแ แแฝแฌแแแปแแบ แโแ แแฏแแญแฏ แกแฌแแฏแถแ
แญแฏแแบแแซแ |
| แแ
โแแ | ๐ก แแผแ
แบแแญแฏแแบแแฑแฌ | แแผแแบแแฌแแฑแฌแกแฌแธแแแบแธแแปแแบแแปแฌแธแ แแ
แบแแพแแบแแฌแธแแผแแบแแแบแแแบแแญแฏแแแบแ |
| แแโแแ | ๐ แแปแแบแธแแปแพแกแฑแฌแแบ | แ
แแ
แบแแญแฏแแบแแฌแแผแฟแแฌแแปแฌแธแ แแผแแบแแฝแฒแทแ
แแบแธแแแบแแญแฏแแแบแ |
| แโแแ | ๐ด แกแแฑแธแแฑแซแบ | แแปแแบแแปแแบแธแแแบแแฑแฌแแบแแฐแแฎแแแบแแญแฏแแแบแ |
---
### แแแบแแญแฏแทแกแแฏแถแธแแผแฏแแแบแแแบแธ (`transformers` แแพแแทแบ)
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "BIOS-kernel/BIOS-Insight-v1"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype = torch.bfloat16,
device_map = "auto",
)
# แแผแแบแแฌแแฌแแฌแแผแแทแบ แแฑแธแแผแแบแธแแญแฏแแบแแแบ
messages = [
{
"role": "system",
"content": (
"แแแบแแแบ BIOS แแผแ
แบแแแบ โ Business Idea Operating Systemแ "
"แแผแแบแแฌ SME แแปแฌแธแกแแฝแแบ elite AI แกแแผแถแแฑแธแ "
"JSON แแฑแฌแบแแแบแแผแแทแบ แแผแฑแแซแ"
),
},
{
"role": "user",
"content": (
"แคแแฏแแบแแแบแธแแญแฏ แ
แ
แบแแฑแธแแซ:\n"
"แแฏแแบแแแบแธ: แแฝแพแฑแแแบ แแฝแพแฑแแพแแทแบ แแแบแแแบแแแแฌ | แแแนแ: แแฝแพแฑแแญแฏแแบ | แแแบแแฑแแฌ: แแแบแแฏแแบ\n"
"แแ
แแบแแแบแแฝแฑ: แ,แแแ,แแแ แแปแแบ | Retention Rate: แแ% | แกแแฝแฒแทแแแบ: แ แฆแธ\n"
"แกแแผแฎแธแแฏแถแธแแผแฟแแฌ: แแฑแฌแแบแแแบแแปแฌแธแแญแฏ แแผแแบแแแฌแกแฑแฌแแบ แแแบแแฝแแบแแญแฏแแบแแฑแฌแ
แแ
แบ แแแพแญ\n"
"แแ แแแแบแธแแญแฏแแบ: แแ,แแแ,แแแ แแปแแบ"
),
},
]
input_ids = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
output = model.generate(
input_ids, max_new_tokens=1024, temperature=0.3, do_sample=True,
pad_token_id=tokenizer.eos_token_id,
)
response = tokenizer.decode(output[0][input_ids.shape[-1]:], skip_special_tokens=True)
print(response)
```
---
### BIOS Controller แแผแแทแบ แกแแฏแถแธแแผแฏแแผแแบแธ
```python
from bios_controller import BIOSController, BusinessInputs, ModelBackend
controller = BIOSController(backend=ModelBackend.GROQ, save_to_db=True)
inputs = BusinessInputs(
business_name = "แแฝแพแฑแแแบ แแฝแพแฑแแพแแทแบ แแแบแแแบแแแแฌ",
industry = "Gold Shop",
location = "แแแบแแฏแแบ",
years_in_business = 7,
monthly_revenue = 4_200_000,
team_size = 3,
retention_rate = 28.0,
unique_selling_proposition = "แกแแญแกแแพแแบแแผแฏแแฌแธแแฑแฌ แแ.แ% แแฝแพแฑแ
แ
แบ โ แแ แแพแ
แบ buyback แกแฌแแแถ",
biggest_pain_point = "แแฑแฌแแบแแแบแแปแฌแธแแญแฏ แแแแแแบแแผแฎแธแแฑแฌแแบ แแแบแแฝแแบแแญแฏแแบแแฑแฌแ
แแ
แบ แแแพแญ",
goal_12_month = 12_000_000,
preferred_language = "แแผแแบแแฌแแฌแแฌ",
# ... (แแฑแธแแฝแแบแธ แแ แแฏแแฏแถแธ)
)
report = controller.run_diagnosis(inputs)
print(f"แแปแแบแธแแฌแแฑแธแแแพแแบ: {report.health_score}/แแแ ({report.health_label})")
print(f"AI แกแ
แฎแแแบแแถแแปแแบ:\n{report.ai_narrative}")
```
---
### แแฏแถแแผแฏแถแแฑแธแแพแแทแบ แแแทแบแแแบแแปแแบแแปแฌแธ
- Benchmark แแปแฌแธแแแบ แแผแแบแแฌแทแแฑแธแแฝแแบแกแแผแฑแกแแฑ (MMK แแฝแฑแแผแฑแธ) แกแแฝแแบ แแปแญแแบแแพแญแแฌแธแแแบ
- แแผแฎแธแแฝแฌแธแแพแฏแแแทแบแแพแแบแธแแปแแบแแปแฌแธแแแบ estimate แแปแฌแธแแฌแแผแ
แบแแผแฎแธ แกแฌแแแถแแปแแบแแแฑแธแแญแฏแแบแแซ
- แฅแแแฑแแพแแทแบ แแแนแแฌแแฑแธแแญแฏแแบแแฌ แแฏแถแธแแผแแบแแปแแบแแปแฌแธแแญแฏ แกแแแบแกแแปแแบแธแแผแแทแบแแแฑแฌแแปแฝแแบแธแแปแแบแแฐแแปแฌแธแแพแแทแบ แแผแแบแแแบแ
แ
แบแแฑแธแแแทแบแแแบ
---
<div align="center">
**BIOS โ Business Idea Operating System**
*"แแแบแแแฏแแบแแแบแธแแญแฏ แแปแฝแแบแฏแแบแแญแฏแท แแญแฏแธแแญแฏแธแ
แ
แบแแฑแธแแฌแแแฏแแบแแซแ แแปแฝแแบแฏแแบแแญแฏแท แแแบแธแแญแฏ แแแบแธแแญแแบแ
แฑแแแบแ"*
*"We don't just analyse businesses. We illuminate them."*
[](https://huggingface.co/BIOS-kernel/BIOS-Insight-v1)
</div>
|