metadata

library_name: transformers
license: apache-2.0
base_model: bespokelabs/Bespoke-Stratos-7B
tags:
  - llama-factory
  - full
  - generated_from_trainer
  - llama-cpp
  - gguf-my-repo
language:
  - en
datasets:
  - bespokelabs/Bespoke-Stratos-17k
model-index:
  - name: original
    results: []

Triangle104/Bespoke-Stratos-7B-Q8_0-GGUF

This model was converted to GGUF format from bespokelabs/Bespoke-Stratos-7B using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model.

Model details:

This model is a fine-tuned version of Qwen/Qwen2.5-7B-Instruct on the Bespoke-Stratos-17k dataset. The dataset is derived by distilling DeepSeek-R1 using the data pipeline of Berkeley NovaSky’s Sky-T1 with some modifications. More info in the dataset card at Bespoke-Stratos-17k. It outperforms Qwen-2.5-7B-Instruct on math reasoning benchmarks

Use with llama.cpp

Install llama.cpp through brew (works on Mac and Linux)

brew install llama.cpp

Invoke the llama.cpp server or the CLI.

CLI:

llama-cli --hf-repo Triangle104/Bespoke-Stratos-7B-Q8_0-GGUF --hf-file bespoke-stratos-7b-q8_0.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo Triangle104/Bespoke-Stratos-7B-Q8_0-GGUF --hf-file bespoke-stratos-7b-q8_0.gguf -c 2048

Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well.

Step 1: Clone llama.cpp from GitHub.

git clone https://github.com/ggerganov/llama.cpp

Step 2: Move into the llama.cpp folder and build it with LLAMA_CURL=1 flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).

cd llama.cpp && LLAMA_CURL=1 make

Step 3: Run inference through the main binary.

./llama-cli --hf-repo Triangle104/Bespoke-Stratos-7B-Q8_0-GGUF --hf-file bespoke-stratos-7b-q8_0.gguf -p "The meaning to life and the universe is"

./llama-server --hf-repo Triangle104/Bespoke-Stratos-7B-Q8_0-GGUF --hf-file bespoke-stratos-7b-q8_0.gguf -c 2048