Instructions to use ethzanalytics/stablelm-tuned-alpha-7b-sharded with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ethzanalytics/stablelm-tuned-alpha-7b-sharded with Transformers:

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

pipe = pipeline("text-generation", model="ethzanalytics/stablelm-tuned-alpha-7b-sharded")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ethzanalytics/stablelm-tuned-alpha-7b-sharded")
model = AutoModelForCausalLM.from_pretrained("ethzanalytics/stablelm-tuned-alpha-7b-sharded")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use ethzanalytics/stablelm-tuned-alpha-7b-sharded with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ethzanalytics/stablelm-tuned-alpha-7b-sharded"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ethzanalytics/stablelm-tuned-alpha-7b-sharded",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ethzanalytics/stablelm-tuned-alpha-7b-sharded

SGLang

How to use ethzanalytics/stablelm-tuned-alpha-7b-sharded 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 "ethzanalytics/stablelm-tuned-alpha-7b-sharded" \
    --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": "ethzanalytics/stablelm-tuned-alpha-7b-sharded",
		"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 "ethzanalytics/stablelm-tuned-alpha-7b-sharded" \
        --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": "ethzanalytics/stablelm-tuned-alpha-7b-sharded",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ethzanalytics/stablelm-tuned-alpha-7b-sharded with Docker Model Runner:
```
docker model run hf.co/ethzanalytics/stablelm-tuned-alpha-7b-sharded
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

StableLM-Tuned-Alpha 7b: sharded checkpoint

This is a sharded checkpoint (with ~4GB shards) of the model. Refer to the original model for all details.

this enables low-RAM loading, i.e. Colab :)

Basic Usage

install transformers, accelerate, and bitsandbytes.

pip install -U -q transformers bitsandbytes accelerate

Load the model in 8bit, then run inference:

from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "ethzanalytics/stablelm-tuned-alpha-7b-sharded"
tokenizer = AutoTokenizer.from_pretrained(model_name)

model = AutoModelForCausalLM.from_pretrained(
    model_name, load_in_8bit=True, device_map="auto"
)

Downloads last month: 6

Safetensors

Model size

8B params

Tensor type

F32

BOOL

Model tree for ethzanalytics/stablelm-tuned-alpha-7b-sharded

Quantizations

2 models