Instructions to use Tarxxxxxx/TX-M-72B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Tarxxxxxx/TX-M-72B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Tarxxxxxx/TX-M-72B")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Tarxxxxxx/TX-M-72B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Tarxxxxxx/TX-M-72B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Tarxxxxxx/TX-M-72B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Tarxxxxxx/TX-M-72B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Tarxxxxxx/TX-M-72B
- SGLang
How to use Tarxxxxxx/TX-M-72B with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "Tarxxxxxx/TX-M-72B" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Tarxxxxxx/TX-M-72B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "Tarxxxxxx/TX-M-72B" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Tarxxxxxx/TX-M-72B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Tarxxxxxx/TX-M-72B with Docker Model Runner:
docker model run hf.co/Tarxxxxxx/TX-M-72B
TX-M-72B
Mesh-augmented model. Runs across the TARX network.
TX-M-72B is TARX's flagship distributed model, designed to run across the mesh network of TARX users. It combines local inference with network-augmented capabilities for maximum performance.
Model Details
| Property | Value |
|---|---|
| Parameters | 72B |
| Architecture | Mesh-distributed |
| Minimum Local RAM | 8 GB (with mesh) |
| Context Length | 128K tokens |
| License | Apache 2.0 |
How It Works
TX-M-72B uses TARX's mesh network to distribute inference across multiple nodes:
- Local Processing: Your device handles initial processing and context
- Mesh Augmentation: Complex reasoning is distributed across the network
- Privacy Preserved: Conversation content is encrypted; only embeddings are shared
- Automatic Fallback: Works offline with reduced capability
Capabilities
- β Everything TX-16G does, plus:
- β 128K context window
- β Expert-level reasoning
- β Complex code generation
- β Multi-document synthesis
- β Research-grade analysis
Requirements
| Requirement | Value |
|---|---|
| TARX Desktop | v1.0.0+ |
| Mesh Network | Enabled |
| Internet | Required for mesh features |
| Local RAM | 8 GB minimum |
Usage
With TARX Desktop
TX-M-72B is automatically available when mesh network is enabled:
Settings β Network β Enable Mesh
The model activates automatically for complex queries when mesh nodes are available.
API Access
# TX-M-72B is accessed through standard TARX API
# Mesh routing is handled automatically
from tarx import Client
client = Client()
response = client.chat(
model="tx-m-72b",
messages=[{"role": "user", "content": "Analyze this research paper..."}]
)
Mesh Network
TX-M-72B leverages the TARX mesh network:
- Distributed Computing: Inference spread across participating nodes
- Privacy First: Zero-knowledge proofs verify computation without exposing data
- Incentivized: Mesh contributors earn TARX tokens
- Resilient: No single point of failure
Performance
| Benchmark | TX-M-72B | GPT-4 | Claude 3.5 |
|---|---|---|---|
| MMLU | TBD | 86.4 | 88.7 |
| HumanEval | TBD | 67.0 | 92.0 |
| MATH | TBD | 42.5 | 71.1 |
Benchmarks in progress - Q1 2026
Comparison
| Feature | TX-16G | TX-M-72B |
|---|---|---|
| Parameters | 14B | 72B |
| Context | 32K | 128K |
| Runs Offline | β Full | β Reduced |
| Mesh Required | β | β For full capability |
| Best For | General use | Complex reasoning |
Links
Built by TARX | tarx.com