Instructions to use Zyphra/Zamba-7B-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Zyphra/Zamba-7B-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Zyphra/Zamba-7B-v1")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Zyphra/Zamba-7B-v1") model = AutoModelForCausalLM.from_pretrained("Zyphra/Zamba-7B-v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Zyphra/Zamba-7B-v1 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Zyphra/Zamba-7B-v1" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Zyphra/Zamba-7B-v1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Zyphra/Zamba-7B-v1
- SGLang
How to use Zyphra/Zamba-7B-v1 with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "Zyphra/Zamba-7B-v1" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Zyphra/Zamba-7B-v1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "Zyphra/Zamba-7B-v1" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Zyphra/Zamba-7B-v1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Zyphra/Zamba-7B-v1 with Docker Model Runner:
docker model run hf.co/Zyphra/Zamba-7B-v1
Bug: FlashAttention forward only supports head dimension
#3
by Xidong - opened
out, q, k, v, out_padded, softmax_lse, S_dmask, rng_state = flash_attn_cuda.fwd(
RuntimeError: FlashAttention forward only supports head dimension at most 256
This isn't a bug. You can't use Flash Attention on our HF implementation because of our concat before the shared attn layer. We got around this by adding split-head support in our inference stack, which we're working on upstreaming to https://github.com/Zyphra/Zamba-torch
In the meantime, we're going to add an assertion to disable FA2 here until we get the HF port figured out for our FA2 changes. Can you try with non-flash attention?
Ok, get it, Thanks
Xidong changed discussion status to closed