Instructions to use Zyphra/Zamba2-7B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Zyphra/Zamba2-7B-Instruct with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Zyphra/Zamba2-7B-Instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Zyphra/Zamba2-7B-Instruct")
model = AutoModelForCausalLM.from_pretrained("Zyphra/Zamba2-7B-Instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Zyphra/Zamba2-7B-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Zyphra/Zamba2-7B-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Zyphra/Zamba2-7B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Zyphra/Zamba2-7B-Instruct

SGLang

How to use Zyphra/Zamba2-7B-Instruct 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/Zamba2-7B-Instruct" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Zyphra/Zamba2-7B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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/Zamba2-7B-Instruct" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Zyphra/Zamba2-7B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Zyphra/Zamba2-7B-Instruct with Docker Model Runner:
```
docker model run hf.co/Zyphra/Zamba2-7B-Instruct
```

Error When Training LoRA Model with Unsupported Target Modules

by anandhperumal - opened Oct 17, 2024

Discussion

anandhperumal

Oct 17, 2024

I am trying to train a LoRA model using the following configuration:

lora_config = LoraConfig(
    r=8,
    target_modules=["x_proj", "embeddings", "in_proj", "out_proj"],
    task_type="CAUSAL_LM",
    bias="none"
)

However, I am encountering the following error:

Currently, only the following modules are supported: torch.nn.Linear, torch.nn.Embedding, torch.nn.Conv2d, transformers.pytorch_utils.Conv1D.

I followed the installation steps as recommended:

Cloned the repository: git clone https://github.com/Zyphra/transformers_zamba2.git
cd transformers_zamba2
pip install -e .
pip install accelerate

Despite following these steps, I am still seeing the error regarding unsupported modules. Could you please advise on how to resolve this issue or if additional modules need to be supported for LoRA training?

annago-zy

Zyphra org Oct 17, 2024

•

edited Oct 17, 2024

It's always more helpful to see the entire error message!
The issue is in_proj, which is a ModuleList and not a Linear. For some reason, the code underlying lora config that matches target_modules to model parts does not descend into the ModuleList to find the Linear.

ValueError: Target module ModuleList(
  (0): Linear(in_features=2560, out_features=10448, bias=False)
) is not supported. Currently, only the following modules are supported: `torch.nn.Linear`, `torch.nn.Embedding`, `torch.nn.Conv2d`, `transformers.pytorch_utils.Conv1D`.

A straightforward way to circumvent this error is to provide a list of parameter names without the .weight extension, like this:

target_modules = ["x_proj", "embeddings", "out_proj"]

for n, p in model.named_parameters():
    if 'mamba.in_proj' in n:
        target_modules.append(n.strip('.weight'))

annago-zy changed discussion status to closed Oct 17, 2024

pglo

Oct 18, 2024

Hi @anandhperumal , thanks for bringing this up. We made a small update to transformers_zamba2 and also to the model weights. Please run git pull inside the folder of transformers_zamba2 and download the updated model. After doing that, you should be able to use the LoRA config that you originally specified, as well as the approach that @annago-zy suggested.

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