Instructions to use RthItalia/Rth-lm-code-25b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RthItalia/Rth-lm-code-25b with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="RthItalia/Rth-lm-code-25b",
	filename="rth_lm_25b_code.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use RthItalia/Rth-lm-code-25b with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf RthItalia/Rth-lm-code-25b
# Run inference directly in the terminal:
llama-cli -hf RthItalia/Rth-lm-code-25b

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf RthItalia/Rth-lm-code-25b
# Run inference directly in the terminal:
llama-cli -hf RthItalia/Rth-lm-code-25b

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 RthItalia/Rth-lm-code-25b
# Run inference directly in the terminal:
./llama-cli -hf RthItalia/Rth-lm-code-25b

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 RthItalia/Rth-lm-code-25b
# Run inference directly in the terminal:
./build/bin/llama-cli -hf RthItalia/Rth-lm-code-25b

Use Docker

docker model run hf.co/RthItalia/Rth-lm-code-25b

LM Studio
Jan

vLLM

How to use RthItalia/Rth-lm-code-25b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "RthItalia/Rth-lm-code-25b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "RthItalia/Rth-lm-code-25b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/RthItalia/Rth-lm-code-25b

Ollama
How to use RthItalia/Rth-lm-code-25b with Ollama:
```
ollama run hf.co/RthItalia/Rth-lm-code-25b
```

Unsloth Studio new

How to use RthItalia/Rth-lm-code-25b 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 RthItalia/Rth-lm-code-25b 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 RthItalia/Rth-lm-code-25b to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for RthItalia/Rth-lm-code-25b to start chatting

Docker Model Runner
How to use RthItalia/Rth-lm-code-25b with Docker Model Runner:
```
docker model run hf.co/RthItalia/Rth-lm-code-25b
```

Lemonade

How to use RthItalia/Rth-lm-code-25b with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull RthItalia/Rth-lm-code-25b

Run and chat with the model

lemonade run user.Rth-lm-code-25b-{{QUANT_TAG}}

List all available models

lemonade list

RthItalia commited on 28 days ago

Commit

75ee6dd

verified ·

1 Parent(s): ddaa4ab

Update README.md

Browse files

Files changed (1) hide show

README.md +9 -6

README.md CHANGED Viewed

@@ -47,16 +47,19 @@ Unlike Traditional Transformers, ZetaGrid uses a **Gated Causal TCN backbone** w
 ## 🚀 Usage (Inference)
 ### Prerequisites
-You need the `cpu_da` framework or the Python inference script.
 ```bash
 # Clone the repo
-git clone https://github.com/rth-italia/cpu-da
-cd cpu-da
 ```
 ### Running the Model (Python)
-Ensure you have `zeta25b_step15000.pt` (Weights) and `zetagrid_25b_production.npy` (Genome).
 ```python
 import torch
@@ -71,7 +74,7 @@ print(text)
 ```
 ### QULP 2-bit Inference (Ultra-Low Memory)
-To run on consumer CPUs with <2GB RAM:
 ```bash
 python QULP_INFERENCE.py --model zeta25b_2bit.qulp
@@ -100,5 +103,5 @@ ZetaGrid is **NOT** a Transformer. It is a TCN-based organism.
 ## 📜 License
 CC BY-NC 4.0 (Creative Commons Non-Commercial) for Research.
-**Commercial Use:** Requires a license from **RTH Italia** (Cpu-DA Project).
 For inquiries: info@rthitalia.com

 ## 🚀 Usage (Inference)
 ### Prerequisites
+Use the ZetaGrid reference repository and download the model artifacts from this Hugging Face repository.
 ```bash
 # Clone the repo
+git clone https://github.com/rthgit/ZetaGrid
+cd ZetaGrid
 ```
 ### Running the Model (Python)
+Place the required artifacts in the working directory, or update the paths in the script:
+- `zeta25b_step15000.pt` - Soul/checkpoint weights
+- `zetagrid_25b_production.npy` - Genome weight bank
 ```python
 import torch
 ```
 ### QULP 2-bit Inference (Ultra-Low Memory)
+If using the QULP artifact, download `zeta25b_2bit.qulp` from the model repository and run the matching local inference script when available:
 ```bash
 python QULP_INFERENCE.py --model zeta25b_2bit.qulp
 ## 📜 License
 CC BY-NC 4.0 (Creative Commons Non-Commercial) for Research.
+**Commercial Use:** Requires a license from **RTH Italia**.
 For inquiries: info@rthitalia.com