Safetensors
GGUF
Turkish
llama
Llama-3
instruct
finetune
chatml
gpt4
synthetic data
distillation
function calling
json mode
axolotl
roleplaying
chat
Instructions to use tda45/TdAI with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use tda45/TdAI with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="tda45/TdAI", filename="llama.cpp/models/ggml-vocab-aquila.gguf", )
output = llm( "Once upon a time,", max_tokens=512, echo=True ) print(output)
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use tda45/TdAI with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf tda45/TdAI # Run inference directly in the terminal: llama cli -hf tda45/TdAI
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf tda45/TdAI # Run inference directly in the terminal: llama cli -hf tda45/TdAI
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf tda45/TdAI # Run inference directly in the terminal: ./llama-cli -hf tda45/TdAI
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf tda45/TdAI # Run inference directly in the terminal: ./build/bin/llama-cli -hf tda45/TdAI
Use Docker
docker model run hf.co/tda45/TdAI
- LM Studio
- Jan
- Ollama
How to use tda45/TdAI with Ollama:
ollama run hf.co/tda45/TdAI
- Unsloth Studio
How to use tda45/TdAI with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for tda45/TdAI to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for tda45/TdAI to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for tda45/TdAI to start chatting
- Atomic Chat new
- Docker Model Runner
How to use tda45/TdAI with Docker Model Runner:
docker model run hf.co/tda45/TdAI
- Lemonade
How to use tda45/TdAI with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull tda45/TdAI
Run and chat with the model
lemonade run user.TdAI-{{QUANT_TAG}}List all available models
lemonade list
| // Data Analysis Report | |
| export const DATA_ANALYSIS_MD = String.raw` | |
| # Q4 2024 Business Analytics Report | |
| *Executive Summary β’ Generated on January 15, 2025* | |
| ## π Key Performance Indicators | |
| ${'```'} | |
| Daily Active Users (DAU): 1.2M (+65% YoY) | |
| Monthly Active Users (MAU): 4.5M (+48% YoY) | |
| User Retention (Day 30): 68% (+12pp YoY) | |
| Average Session Duration: 24min (+35% YoY) | |
| ${'```'} | |
| ## π― Product Performance | |
| ### Feature Adoption Rates | |
| 1. **AI Assistant**: 78% of users (β from 45%) | |
| 2. **Collaboration Tools**: 62% of users (β from 38%) | |
| 3. **Analytics Dashboard**: 54% of users (β from 31%) | |
| 4. **Mobile App**: 41% of users (β from 22%) | |
| ### Customer Satisfaction | |
| | Metric | Q4 2024 | Q3 2024 | Change | | |
| |--------|---------|---------|--------| | |
| | **NPS Score** | 72 | 68 | +4 | | |
| | **CSAT** | 4.6/5 | 4.4/5 | +0.2 | | |
| | **Support Tickets** | 2,340 | 2,890 | -19% | | |
| | **Resolution Time** | 4.2h | 5.1h | -18% | | |
| ## π° Revenue Metrics | |
| ### Monthly Recurring Revenue (MRR) | |
| - **Current MRR**: $2.8M (+42% YoY) | |
| - **New MRR**: $340K | |
| - **Expansion MRR**: $180K | |
| - **Churned MRR**: $95K | |
| - **Net New MRR**: $425K | |
| ### Customer Acquisition | |
| ${'```'} | |
| Cost per Acquisition (CAC): $127 (-23% YoY) | |
| Customer Lifetime Value: $1,840 (+31% YoY) | |
| LTV:CAC Ratio: 14.5:1 | |
| Payback Period: 3.2 months | |
| ${'```'} | |
| ## π Geographic Performance | |
| ### Revenue by Region | |
| 1. **North America**: 45% ($1.26M) | |
| 2. **Europe**: 32% ($896K) | |
| 3. **Asia-Pacific**: 18% ($504K) | |
| 4. **Other**: 5% ($140K) | |
| ### Growth Opportunities | |
| - **APAC**: 89% YoY growth potential | |
| - **Latin America**: Emerging market entry | |
| - **Middle East**: Enterprise expansion | |
| ## π± Channel Performance | |
| ### Traffic Sources | |
| | Channel | Sessions | Conversion | Revenue | | |
| |---------|----------|------------|---------| | |
| | **Organic Search** | 45% | 3.2% | $1.1M | | |
| | **Direct** | 28% | 4.1% | $850K | | |
| | **Social Media** | 15% | 2.8% | $420K | | |
| | **Paid Ads** | 12% | 5.5% | $430K | | |
| ### Marketing ROI | |
| - **Content Marketing**: 340% ROI | |
| - **Email Campaigns**: 280% ROI | |
| - **Social Media**: 190% ROI | |
| - **Paid Search**: 220% ROI | |
| ## π User Behavior Analysis | |
| ### Session Patterns | |
| - **Peak Hours**: 9-11 AM, 2-4 PM EST | |
| - **Mobile Usage**: 67% of sessions | |
| - **Average Pages/Session**: 4.8 | |
| - **Bounce Rate**: 23% (β from 31%) | |
| ### Feature Usage Heatmap | |
| Most used features in order: | |
| 1. Dashboard (89% of users) | |
| 2. Search (76% of users) | |
| 3. Reports (64% of users) | |
| 4. Settings (45% of users) | |
| 5. Integrations (32% of users) | |
| ## π‘ Recommendations | |
| 1. **Invest** in AI capabilities (+$2M budget) | |
| 2. **Expand** sales team in APAC region | |
| 3. **Improve** onboarding to reduce churn | |
| 4. **Launch** enterprise security features | |
| ## Appendix | |
| ### Methodology | |
| Data collected from: | |
| - Internal analytics (Amplitude) | |
| - Customer surveys (n=2,450) | |
| - Financial systems (NetSuite) | |
| - Market research (Gartner) | |
| --- | |
| *Report prepared by Data Analytics Team β’ [View Interactive Dashboard](https://analytics.example.com)* | |
| `; | |