Instructions to use AIIT-Threshold/Tessera-1B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use AIIT-Threshold/Tessera-1B 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 AIIT-Threshold/Tessera-1B:Q6_K
# Run inference directly in the terminal:
llama cli -hf AIIT-Threshold/Tessera-1B:Q6_K

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf AIIT-Threshold/Tessera-1B:Q6_K
# Run inference directly in the terminal:
llama cli -hf AIIT-Threshold/Tessera-1B: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 AIIT-Threshold/Tessera-1B:Q6_K
# Run inference directly in the terminal:
./llama-cli -hf AIIT-Threshold/Tessera-1B: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 AIIT-Threshold/Tessera-1B:Q6_K
# Run inference directly in the terminal:
./build/bin/llama-cli -hf AIIT-Threshold/Tessera-1B:Q6_K

Use Docker

docker model run hf.co/AIIT-Threshold/Tessera-1B:Q6_K

LM Studio
Jan
Ollama
How to use AIIT-Threshold/Tessera-1B with Ollama:
```
ollama run hf.co/AIIT-Threshold/Tessera-1B:Q6_K
```

Unsloth Studio

How to use AIIT-Threshold/Tessera-1B 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 AIIT-Threshold/Tessera-1B 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 AIIT-Threshold/Tessera-1B to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for AIIT-Threshold/Tessera-1B to start chatting

Docker Model Runner
How to use AIIT-Threshold/Tessera-1B with Docker Model Runner:
```
docker model run hf.co/AIIT-Threshold/Tessera-1B:Q6_K
```

Lemonade

How to use AIIT-Threshold/Tessera-1B with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull AIIT-Threshold/Tessera-1B:Q6_K

Run and chat with the model

lemonade run user.Tessera-1B-Q6_K

List all available models

lemonade list

Atomic Chat

AIIT-Threshold commited on Jul 6

Commit

3b84f40

verified ·

1 Parent(s): cdde94c

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@@ -19,7 +19,7 @@ A ~1B-parameter language model **trained from scratch** by AIIT-THRESHOLD (an in
 **What it is:** a clean, honest **base model**. It produces fluent English (and some Japanese) but has limited reasoning and factual reliability — it has not been post-trained for a task. **This is the point.** Tessera 1B is a well-built starting block: it SFTs cleanly and makes an excellent foundation for a **specialty model** — a system fine-tuned to answer specific questions about a specific domain.
-**What it is not:** a chat assistant, a reasoning model, or a drop-in ChatGPT. Out of the box it will not reliably answer trivia or follow complex instructions. Post-train it for your task.
 ## Model details
 | | |

 **What it is:** a clean, honest **base model**. It produces fluent English (and some Japanese) but has limited reasoning and factual reliability — it has not been post-trained for a task. **This is the point.** Tessera 1B is a well-built starting block: it SFTs cleanly and makes an excellent foundation for a **specialty model** — a system fine-tuned to answer specific questions about a specific domain.
+**What it is not:** a chat assistant, a reasoning model, or a drop-in ChatGPT. Out of the box it will not reliably answer trivia or follow complex instructions. Post-train it for your task. We genuinely believe the best application would be customer service for a particular Domain.
 ## Model details
 | | |