Instructions to use BaseMemory/SSA-Llama-3.1-8B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use BaseMemory/SSA-Llama-3.1-8B-Instruct with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.1-8B-Instruct")
model = PeftModel.from_pretrained(base_model, "BaseMemory/SSA-Llama-3.1-8B-Instruct")

Transformers

How to use BaseMemory/SSA-Llama-3.1-8B-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="BaseMemory/SSA-Llama-3.1-8B-Instruct")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("BaseMemory/SSA-Llama-3.1-8B-Instruct", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use BaseMemory/SSA-Llama-3.1-8B-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "BaseMemory/SSA-Llama-3.1-8B-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "BaseMemory/SSA-Llama-3.1-8B-Instruct",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/BaseMemory/SSA-Llama-3.1-8B-Instruct

SGLang

How to use BaseMemory/SSA-Llama-3.1-8B-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 "BaseMemory/SSA-Llama-3.1-8B-Instruct" \
    --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": "BaseMemory/SSA-Llama-3.1-8B-Instruct",
		"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 "BaseMemory/SSA-Llama-3.1-8B-Instruct" \
        --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": "BaseMemory/SSA-Llama-3.1-8B-Instruct",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use BaseMemory/SSA-Llama-3.1-8B-Instruct with Docker Model Runner:
```
docker model run hf.co/BaseMemory/SSA-Llama-3.1-8B-Instruct
```

onlyaady commited on Sep 9

Commit

dd440b4

verified ·

1 Parent(s): c8b4487

Add checkpoint SHA-256 manifest

Browse files

Files changed (1) hide show

CHECKPOINT_SHA256SUMS.txt +3 -0

CHECKPOINT_SHA256SUMS.txt ADDED Viewed

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+d347c2db843ff052cdbbdf02a8c6b58a2b0e8e2bcb3c80063ba7f0920291fb3f  README.md
+e96a03e2f4b473e080cedfef3c1e80704153e601135a649a3c269c2de885869a  adapter_config.json
+2f30c01a9ef2605bf94a92e245dc0264da21adc70fbb4bcf4636970bad52992a  adapter_model.safetensors