How to use from
llama.cppInstall from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf QuantFactory/TriLM_3.9B_Unpacked-GGUF:# Run inference directly in the terminal:
llama-cli -hf QuantFactory/TriLM_3.9B_Unpacked-GGUF: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 QuantFactory/TriLM_3.9B_Unpacked-GGUF:# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/TriLM_3.9B_Unpacked-GGUF: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 QuantFactory/TriLM_3.9B_Unpacked-GGUF:# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/TriLM_3.9B_Unpacked-GGUF:Use Docker
docker model run hf.co/QuantFactory/TriLM_3.9B_Unpacked-GGUF:Quick Links
QuantFactory/TriLM_3.9B_Unpacked-GGUF
This is quantized version of SpectraSuite/TriLM_3.9B_Unpacked created using llama.cpp
Original Model Card
TriLM 3.9B Unpacked
TriLM (ternary model), unpacked to FP16 format - compatible with FP16 GEMMs. After unpacking, TriLM has the same architecture as LLaMa.
import transformers as tf, torch
model_name = "SpectraSuite/TriLM_3.9B_Unpacked"
# Please adjust the temperature, repetition penalty, top_k, top_p and other sampling parameters according to your needs.
pipeline = tf.pipeline("text-generation", model=model_id, model_kwargs={"torch_dtype": torch.float16}, device_map="auto")
# These are base (pretrained) LLMs that are not instruction and chat tuned. You may need to adjust your prompt accordingly.
pipeline("Once upon a time")
- License: Apache 2.0
- We will use our GitHub repo for communication (including HF repo related queries). Feel free to open an issue here https://github.com/NolanoOrg/SpectraSuite
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Install from brew
# Start a local OpenAI-compatible server with a web UI: llama-server -hf QuantFactory/TriLM_3.9B_Unpacked-GGUF:# Run inference directly in the terminal: llama-cli -hf QuantFactory/TriLM_3.9B_Unpacked-GGUF: