Instructions to use royleibov/Jamba-v0.1-ZipNN-Compressed with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use royleibov/Jamba-v0.1-ZipNN-Compressed with Transformers:

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

pipe = pipeline("text-generation", model="royleibov/Jamba-v0.1-ZipNN-Compressed", trust_remote_code=True)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("royleibov/Jamba-v0.1-ZipNN-Compressed", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("royleibov/Jamba-v0.1-ZipNN-Compressed", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use royleibov/Jamba-v0.1-ZipNN-Compressed with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "royleibov/Jamba-v0.1-ZipNN-Compressed"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "royleibov/Jamba-v0.1-ZipNN-Compressed",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/royleibov/Jamba-v0.1-ZipNN-Compressed

SGLang

How to use royleibov/Jamba-v0.1-ZipNN-Compressed 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 "royleibov/Jamba-v0.1-ZipNN-Compressed" \
    --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": "royleibov/Jamba-v0.1-ZipNN-Compressed",
		"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 "royleibov/Jamba-v0.1-ZipNN-Compressed" \
        --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": "royleibov/Jamba-v0.1-ZipNN-Compressed",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use royleibov/Jamba-v0.1-ZipNN-Compressed with Docker Model Runner:
```
docker model run hf.co/royleibov/Jamba-v0.1-ZipNN-Compressed
```

royleibov commited on Oct 3, 2024

Commit

d5966db

verified ·

1 Parent(s): ef9dc41

remove trust remote code

Browse files

Files changed (1) hide show

README.md +3 -3

README.md CHANGED Viewed

@@ -26,7 +26,7 @@ from zipnn import zipnn_hf
 zipnn_hf()
-pipe = pipeline("text-generation", model="royleibov/Jamba-v0.1-ZipNN-Compressed", trust_remote_code=True)
 ```
 ```python
 # Load model directly
@@ -35,8 +35,8 @@ from zipnn import zipnn_hf
 zipnn_hf()
-tokenizer = AutoTokenizer.from_pretrained("royleibov/Jamba-v0.1-ZipNN-Compressed", trust_remote_code=True)
-model = AutoModelForCausalLM.from_pretrained("royleibov/Jamba-v0.1-ZipNN-Compressed", trust_remote_code=True)
 ```
 ### ZipNN
 ZipNN also allows you to seemlessly save local disk space in your cache after the model is downloaded.

 zipnn_hf()
+pipe = pipeline("text-generation", model="royleibov/Jamba-v0.1-ZipNN-Compressed")
 ```
 ```python
 # Load model directly
 zipnn_hf()
+tokenizer = AutoTokenizer.from_pretrained("royleibov/Jamba-v0.1-ZipNN-Compressed")
+model = AutoModelForCausalLM.from_pretrained("royleibov/Jamba-v0.1-ZipNN-Compressed")
 ```
 ### ZipNN
 ZipNN also allows you to seemlessly save local disk space in your cache after the model is downloaded.