Instructions to use danielpark/asp-9b-inst-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use danielpark/asp-9b-inst-base with Transformers:

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

pipe = pipeline("text-generation", model="danielpark/asp-9b-inst-base", trust_remote_code=True)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("danielpark/asp-9b-inst-base", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("danielpark/asp-9b-inst-base", trust_remote_code=True)

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use danielpark/asp-9b-inst-base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "danielpark/asp-9b-inst-base"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "danielpark/asp-9b-inst-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/danielpark/asp-9b-inst-base

SGLang

How to use danielpark/asp-9b-inst-base 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 "danielpark/asp-9b-inst-base" \
    --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": "danielpark/asp-9b-inst-base",
		"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 "danielpark/asp-9b-inst-base" \
        --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": "danielpark/asp-9b-inst-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use danielpark/asp-9b-inst-base with Docker Model Runner:
```
docker model run hf.co/danielpark/asp-9b-inst-base
```

danielpark commited on Aug 6, 2024

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@@ -11,9 +11,9 @@ tags:
 # A experts weights of [Jamba-v0.1](https://huggingface.co/ai21labs/Jamba-v0.1)
-Required Weights for Follow-up Research
-The original model is **[AI21lab's Jamba-v0.1](https://huggingface.co/ai21labs/Jamba-v0.1)**, which requires an **A100 80GB GPU**. Unfortunately, this almonst was not available via Google Colab or cloud computing services. Thus, attempts were made to perform **MoE (Mixture of Experts) splitting**, using the following resources as a basis:
 - **Original Model:** [Jamba-v0.1](https://huggingface.co/ai21labs/Jamba-v0.1)
 - **MoE Layer Separation**: Consult [this script](https://github.com/TechxGenus/Jamba-utils/blob/main/dense_downcycling.py) written by [@TechxGenusand](https://github.com/TechxGenusand) and use [TechxGenus/Jamba-v0.1-9B](https://huggingface.co/TechxGenus/Jamba-v0.1-9B).

 # A experts weights of [Jamba-v0.1](https://huggingface.co/ai21labs/Jamba-v0.1)
+Required Weights for follow-up research.
+The original model is **[AI21lab's Jamba-v0.1](https://huggingface.co/ai21labs/Jamba-v0.1)**, which requires an **>80GB VRAM**. Unfortunately, this almonst was not available via Google Colab or cloud computing services. Thus, attempts were made to perform **MoE (Mixture of Experts) splitting**, using the following resources as a basis:
 - **Original Model:** [Jamba-v0.1](https://huggingface.co/ai21labs/Jamba-v0.1)
 - **MoE Layer Separation**: Consult [this script](https://github.com/TechxGenus/Jamba-utils/blob/main/dense_downcycling.py) written by [@TechxGenusand](https://github.com/TechxGenusand) and use [TechxGenus/Jamba-v0.1-9B](https://huggingface.co/TechxGenus/Jamba-v0.1-9B).