Instructions to use from-our-page/Trinity-Mini-Base-Pre-Anneal-Q6_K-Imatrix-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use from-our-page/Trinity-Mini-Base-Pre-Anneal-Q6_K-Imatrix-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 from-our-page/Trinity-Mini-Base-Pre-Anneal-Q6_K-Imatrix-GGUF:Q6_K # Run inference directly in the terminal: llama cli -hf from-our-page/Trinity-Mini-Base-Pre-Anneal-Q6_K-Imatrix-GGUF:Q6_K
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf from-our-page/Trinity-Mini-Base-Pre-Anneal-Q6_K-Imatrix-GGUF:Q6_K # Run inference directly in the terminal: llama cli -hf from-our-page/Trinity-Mini-Base-Pre-Anneal-Q6_K-Imatrix-GGUF:Q6_K
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 from-our-page/Trinity-Mini-Base-Pre-Anneal-Q6_K-Imatrix-GGUF:Q6_K # Run inference directly in the terminal: ./llama-cli -hf from-our-page/Trinity-Mini-Base-Pre-Anneal-Q6_K-Imatrix-GGUF:Q6_K
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 from-our-page/Trinity-Mini-Base-Pre-Anneal-Q6_K-Imatrix-GGUF:Q6_K # Run inference directly in the terminal: ./build/bin/llama-cli -hf from-our-page/Trinity-Mini-Base-Pre-Anneal-Q6_K-Imatrix-GGUF:Q6_K
Use Docker
docker model run hf.co/from-our-page/Trinity-Mini-Base-Pre-Anneal-Q6_K-Imatrix-GGUF:Q6_K
- LM Studio
- Jan
- vLLM
How to use from-our-page/Trinity-Mini-Base-Pre-Anneal-Q6_K-Imatrix-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "from-our-page/Trinity-Mini-Base-Pre-Anneal-Q6_K-Imatrix-GGUF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "from-our-page/Trinity-Mini-Base-Pre-Anneal-Q6_K-Imatrix-GGUF", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/from-our-page/Trinity-Mini-Base-Pre-Anneal-Q6_K-Imatrix-GGUF:Q6_K
- Ollama
How to use from-our-page/Trinity-Mini-Base-Pre-Anneal-Q6_K-Imatrix-GGUF with Ollama:
ollama run hf.co/from-our-page/Trinity-Mini-Base-Pre-Anneal-Q6_K-Imatrix-GGUF:Q6_K
- Unsloth Studio
How to use from-our-page/Trinity-Mini-Base-Pre-Anneal-Q6_K-Imatrix-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 from-our-page/Trinity-Mini-Base-Pre-Anneal-Q6_K-Imatrix-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 from-our-page/Trinity-Mini-Base-Pre-Anneal-Q6_K-Imatrix-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for from-our-page/Trinity-Mini-Base-Pre-Anneal-Q6_K-Imatrix-GGUF to start chatting
- Docker Model Runner
How to use from-our-page/Trinity-Mini-Base-Pre-Anneal-Q6_K-Imatrix-GGUF with Docker Model Runner:
docker model run hf.co/from-our-page/Trinity-Mini-Base-Pre-Anneal-Q6_K-Imatrix-GGUF:Q6_K
- Lemonade
How to use from-our-page/Trinity-Mini-Base-Pre-Anneal-Q6_K-Imatrix-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull from-our-page/Trinity-Mini-Base-Pre-Anneal-Q6_K-Imatrix-GGUF:Q6_K
Run and chat with the model
lemonade run user.Trinity-Mini-Base-Pre-Anneal-Q6_K-Imatrix-GGUF-Q6_K
List all available models
lemonade list
- Atomic Chat
Trinity Mini Base Pre-Anneal (Q6_K Imatrix)
This is a Q6_K Imatrix quantization of arcee-ai/Trinity-Mini-Base-Pre-Anneal.
license: apache-2.0 language: - en - es - fr - de - it - pt - ru - ar - hi - ko - zh library_name: transformers
Trinity Mini Base Pre Anneal
Trinity-Mini-Base-Pre-Anneal is an Arcee AI 26B MoE model with 3B active parameters. It is the medium-sized model in our new Trinity family, a series of open-weight models for enterprise and tinkerers alike.
This base model is a pre-anneal checkpoint captured at Adam LR: 0.002, Muon LR: 0.001 before starting learning rate decay on a high-quality data mix. While this checkpoint was not exposed to the anneal phase mix containing high proportions of math and code content, it has been trained on significant amounts of such data. This checkpoint is not suitable for chatting or general use without further finetuning and should be trained for your specific domain before use.
Trinity-Mini-Base-Pre-Anneal is trained on 8.8T tokens gathered and curated through a key partnership with Datology, building upon the excellent dataset we used on AFM-4.5B with additional math and code.
Training was performed on a cluster of 512 H200 GPUs powered by Prime Intellect using HSDP parallelism.
More details, including key architecture decisions, can be found on our blog here
Model Details
- Model Architecture: AfmoeForCausalLM
- Parameters: 26B, 3B active
- Experts: 128 total, 8 active, 1 shared
- Context length: 4K
- Learning rate during pretraining:
adam_lr = 0.0002muon_lr = 0.001
- Training Tokens: 8.8T
- License: Apache 2.0
Try out our reasoning tune
Trinity Mini is available today on openrouter:
https://openrouter.ai/arcee-ai/trinity-mini
curl -X POST "https://openrouter.ai/v1/chat/completions" \
-H "Authorization: Bearer $OPENROUTER_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "arcee-ai/trinity-mini",
"messages": [
{
"role": "user",
"content": "What are some fun things to do in New York?"
}
]
}'
License
Trinity-Mini-Base-Pre-Anneal is released under the Apache-2.0 license.
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Base model
arcee-ai/Trinity-Mini-Base-Pre-Anneal