Instructions to use mradermacher/Seed-X-Instruct-7B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mradermacher/Seed-X-Instruct-7B-GGUF with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("mradermacher/Seed-X-Instruct-7B-GGUF", dtype="auto") - llama-cpp-python
How to use mradermacher/Seed-X-Instruct-7B-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="mradermacher/Seed-X-Instruct-7B-GGUF", filename="Seed-X-Instruct-7B.IQ4_XS.gguf", )
output = llm( "Once upon a time,", max_tokens=512, echo=True ) print(output)
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use mradermacher/Seed-X-Instruct-7B-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf mradermacher/Seed-X-Instruct-7B-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf mradermacher/Seed-X-Instruct-7B-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 mradermacher/Seed-X-Instruct-7B-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf mradermacher/Seed-X-Instruct-7B-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 mradermacher/Seed-X-Instruct-7B-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf mradermacher/Seed-X-Instruct-7B-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 mradermacher/Seed-X-Instruct-7B-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf mradermacher/Seed-X-Instruct-7B-GGUF:Q4_K_M
Use Docker
docker model run hf.co/mradermacher/Seed-X-Instruct-7B-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use mradermacher/Seed-X-Instruct-7B-GGUF with Ollama:
ollama run hf.co/mradermacher/Seed-X-Instruct-7B-GGUF:Q4_K_M
- Unsloth Studio
How to use mradermacher/Seed-X-Instruct-7B-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 mradermacher/Seed-X-Instruct-7B-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 mradermacher/Seed-X-Instruct-7B-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for mradermacher/Seed-X-Instruct-7B-GGUF to start chatting
- Atomic Chat new
- Docker Model Runner
How to use mradermacher/Seed-X-Instruct-7B-GGUF with Docker Model Runner:
docker model run hf.co/mradermacher/Seed-X-Instruct-7B-GGUF:Q4_K_M
- Lemonade
How to use mradermacher/Seed-X-Instruct-7B-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull mradermacher/Seed-X-Instruct-7B-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Seed-X-Instruct-7B-GGUF-Q4_K_M
List all available models
lemonade list
did imatrix fail?
the i1 is still not released.
and, they also released pro and rm version.
- https://huggingface.co/ByteDance-Seed/Seed-X-PPO-7B
- https://huggingface.co/ByteDance-Seed/Seed-X-RM-7B
i don't know which is better.
Edit: I tried this model. there is either no translated content, or just repetition of first phase.
imatrix indeed failed. i'll try to queue the two other models
Seed-X-RM-7B: either llama.cpp didn't properly convert the model or it seems to be seriously hosed (e.g. tokenizer does not match the model weights)
llama_model_load: error loading model: check_tensor_dims: tensor 'token_embd.weight' has wrong shape; expected 4096, 65269, got 4096, 65272, 1, 1
and the PRO failed with nans during imatrix generation. tough world.
The RM version is for reward modeling in reinforcement learning phase and is NOT designed for typical generative purpose, thus the llama.cpp failing to load it seems to be a fair consequence (IMHO llama.cpp didn't take these training-only auxiliary models into consideration, as it's a simple AI-on-Edge deployment framework, instead of a full-fledged monstrosity like vllm).
Any chance to redo the GGUFs for this model and fix the issue? Someone mentioned here that it helps to add a special token map: https://huggingface.co/ByteDance-Seed/Seed-X-Instruct-7B/discussions/1#687f2fcc49f1113567075e9a
Would really like to try out this model in llama.cpp...
If somebody clones it under a slightly different name (e.g. Seed-X-Instruct-7B-fix) and adds the file I'll be happy to quant it - I strongly prefer having an upstream repo for nontrivial changes.