Instructions to use 79Labs/astraforge-70b-TCR-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use 79Labs/astraforge-70b-TCR-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="79Labs/astraforge-70b-TCR-GGUF",
	filename="astraforge-70b-TCR-Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use 79Labs/astraforge-70b-TCR-GGUF 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 79Labs/astraforge-70b-TCR-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf 79Labs/astraforge-70b-TCR-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf 79Labs/astraforge-70b-TCR-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf 79Labs/astraforge-70b-TCR-GGUF:Q4_K_M

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 79Labs/astraforge-70b-TCR-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf 79Labs/astraforge-70b-TCR-GGUF:Q4_K_M

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 79Labs/astraforge-70b-TCR-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf 79Labs/astraforge-70b-TCR-GGUF:Q4_K_M

Use Docker

docker model run hf.co/79Labs/astraforge-70b-TCR-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use 79Labs/astraforge-70b-TCR-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "79Labs/astraforge-70b-TCR-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "79Labs/astraforge-70b-TCR-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/79Labs/astraforge-70b-TCR-GGUF:Q4_K_M

Ollama
How to use 79Labs/astraforge-70b-TCR-GGUF with Ollama:
```
ollama run hf.co/79Labs/astraforge-70b-TCR-GGUF:Q4_K_M
```

Unsloth Studio

How to use 79Labs/astraforge-70b-TCR-GGUF 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 79Labs/astraforge-70b-TCR-GGUF 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 79Labs/astraforge-70b-TCR-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for 79Labs/astraforge-70b-TCR-GGUF to start chatting

How to use 79Labs/astraforge-70b-TCR-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf 79Labs/astraforge-70b-TCR-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "79Labs/astraforge-70b-TCR-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use 79Labs/astraforge-70b-TCR-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf 79Labs/astraforge-70b-TCR-GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default 79Labs/astraforge-70b-TCR-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use 79Labs/astraforge-70b-TCR-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf 79Labs/astraforge-70b-TCR-GGUF:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "79Labs/astraforge-70b-TCR-GGUF:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use 79Labs/astraforge-70b-TCR-GGUF with Docker Model Runner:
```
docker model run hf.co/79Labs/astraforge-70b-TCR-GGUF:Q4_K_M
```

Lemonade

How to use 79Labs/astraforge-70b-TCR-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull 79Labs/astraforge-70b-TCR-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.astraforge-70b-TCR-GGUF-Q4_K_M

List all available models

lemonade list

astraforge-70b-TCR — GGUF

Developed by 79Labs · Version 1.0.0

Quantized GGUF builds of 79Labs/astraforge-70b-TCR — the LoRA-on-Llama-3.3-70B agentic tool-calling model, merged into the full-precision base and quantized for local inference with llama.cpp and Ollama. Same weights, same behaviour, no Python / GPU stack required.

For the model description, training details, evaluation, and honest limitations, see the main model card. This repo is the GGUF distribution only.

Files

File	Quant	Size (approx)	Notes
`astraforge-70b-TCR-Q4_K_M.gguf`	Q4_K_M	~42 GB	Recommended default — best size/quality trade-off for a 70B; runs on ~48 GB RAM/VRAM

(More quants — Q5_K_M, Q8_0 — can be added on request.)

Run with Ollama

# from this repo directory (with the .gguf + Modelfile present)
ollama create astraforge-70b-tcr -f Modelfile
ollama run astraforge-70b-tcr

Run with llama.cpp

llama-cli -m astraforge-70b-TCR-Q4_K_M.gguf -c 4096 -p "Book Ada a flight from SFO to JFK on 2026-08-01."
# tool-calling: pass tools via the chat template; keep the working context within ~4K (the tuned window).

Notes

Merged, not an adapter. The LoRA is baked into the base weights, so no separate base download is needed — this GGUF is a complete model.
Context: reinforced at 4K (the trained/served window); the base supports up to 128K.
Build provenance: merged from the full-precision unsloth/Llama-3.3-70B-Instruct base + the 79Labs/astraforge-70b-TCR LoRA via llama.cpp (convert_hf_to_gguf → convert_lora_to_gguf → llama-export-lora → llama-quantize).