Instructions to use AesSedai/Step-3.7-Flash-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use AesSedai/Step-3.7-Flash-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="AesSedai/Step-3.7-Flash-GGUF", filename="IQ2_S/Step-3.7-Flash-IQ2_S-00001-of-00003.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use AesSedai/Step-3.7-Flash-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf AesSedai/Step-3.7-Flash-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf AesSedai/Step-3.7-Flash-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf AesSedai/Step-3.7-Flash-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf AesSedai/Step-3.7-Flash-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 AesSedai/Step-3.7-Flash-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf AesSedai/Step-3.7-Flash-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 AesSedai/Step-3.7-Flash-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf AesSedai/Step-3.7-Flash-GGUF:Q4_K_M
Use Docker
docker model run hf.co/AesSedai/Step-3.7-Flash-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use AesSedai/Step-3.7-Flash-GGUF with Ollama:
ollama run hf.co/AesSedai/Step-3.7-Flash-GGUF:Q4_K_M
- Unsloth Studio
How to use AesSedai/Step-3.7-Flash-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 AesSedai/Step-3.7-Flash-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 AesSedai/Step-3.7-Flash-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for AesSedai/Step-3.7-Flash-GGUF to start chatting
- Pi
How to use AesSedai/Step-3.7-Flash-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf AesSedai/Step-3.7-Flash-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": "AesSedai/Step-3.7-Flash-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use AesSedai/Step-3.7-Flash-GGUF with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf AesSedai/Step-3.7-Flash-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 AesSedai/Step-3.7-Flash-GGUF:Q4_K_M
Run Hermes
hermes
- Docker Model Runner
How to use AesSedai/Step-3.7-Flash-GGUF with Docker Model Runner:
docker model run hf.co/AesSedai/Step-3.7-Flash-GGUF:Q4_K_M
- Lemonade
How to use AesSedai/Step-3.7-Flash-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull AesSedai/Step-3.7-Flash-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Step-3.7-Flash-GGUF-Q4_K_M
List all available models
lemonade list
Problems....
llama barely announced the version with MTP to Step models, and you already made this. Keep the good job. Congratulations.
Yep... But got this while trying to load the Q5_K_M...
0.00.803.436 E llama_model_load: error loading model: done_getting_tensors: wrong number of tensors; expected 805, got 790
0.00.803.444 E llama_model_load_from_file_impl: failed to load model
0.00.803.497 E srv load_model: [spec] failed to measure MTP context memory: failed to load model
Just to make sure, you did pull and recompile? Because it loads and runs when I was on master which is how I ran the PPL / KLD testing.
I took my last version directly from the releases page, so it comes already compiled (windows 11 - cuda 13.3). I am using llama-b9484. https://github.com/ggml-org/llama.cpp/releases. The MTP for Stepfun is ready since release b9480.
Looks like I had a newer version?
$ ./build/bin/llama-server --version
version: 9481 (bfb4308b0)
built with GNU 15.2.1 for Linux x86_64
I just loaded it up to double check:
../llama.cpp/build/bin/llama-server \
--threads 54 --batch-size 4096 --ubatch-size 4096 --fit-target 4096,4096,4096,4096,4096,4096,4096,4096 --direct-io \
--ctx-size 262144 --flash-attn on --port 10000 --host 0.0.0.0 --log-prefix --log-timestamps \
--model /mnt/srv/snowdrift/gguf/Step-3.7-Flash-GGUF/aes_sedai/Step-3.7-Flash-Q5_K_M.gguf --alias "Step-3.7-Flash Q8_0" \
--parallel 1 --spec-type draft-mtp --spec-draft-n-max 2
....
1.11.742.612 I srv load_model: creating MTP draft context against the target model '/mnt/srv/snowdrift/gguf/Step-3.7-Flash-GGUF/aes_sedai/Step-3.7-Flash-Q5_K_M.gguf'
1.11.870.515 I srv load_model: initializing slots, n_slots = 1
1.11.898.909 I common_speculative_impl_draft_mtp: adding speculative implementation 'draft-mtp'
1.11.898.919 I common_speculative_impl_draft_mtp: - n_max=2, n_min=0, p_min=0.00, n_embd=4096, backend_sampling=1
1.11.898.921 I common_speculative_impl_draft_mtp: - gpu_layers=-1, cache_k=f16, cache_v=f16, ctx_tgt=yes, ctx_dft=yes, devices=[default]
and the server came up without issues. Did you download the new quants? They were just updated a little while ago.
My version is newer than yours. But I will download that exact version you are using. It could be a problem introduced by the latest release. I will downloaded your model again, I deleted it. And yes, I downloaded as soon you made the update in your page announcing the MTP version.
There was a short window where I had messed the upload up and had to fix it, so I had to re-upload the splits. Maybe you ended up with one good split and one bad split or something and that caused the issue. The split files are all correct now.
Ah, must be this. I downloaded again and now is working just fine. No problem at all. Thanks for your nice work.
Glad it's sorted! 🤗