Instructions to use ValdoLab/base_model_llama-2-70b-chat-hf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ValdoLab/base_model_llama-2-70b-chat-hf with Transformers:

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

pipe = pipeline("text-generation", model="ValdoLab/base_model_llama-2-70b-chat-hf")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ValdoLab/base_model_llama-2-70b-chat-hf")
model = AutoModelForCausalLM.from_pretrained("ValdoLab/base_model_llama-2-70b-chat-hf")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use ValdoLab/base_model_llama-2-70b-chat-hf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ValdoLab/base_model_llama-2-70b-chat-hf"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ValdoLab/base_model_llama-2-70b-chat-hf",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ValdoLab/base_model_llama-2-70b-chat-hf

SGLang

How to use ValdoLab/base_model_llama-2-70b-chat-hf 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 "ValdoLab/base_model_llama-2-70b-chat-hf" \
    --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": "ValdoLab/base_model_llama-2-70b-chat-hf",
		"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 "ValdoLab/base_model_llama-2-70b-chat-hf" \
        --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": "ValdoLab/base_model_llama-2-70b-chat-hf",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ValdoLab/base_model_llama-2-70b-chat-hf with Docker Model Runner:
```
docker model run hf.co/ValdoLab/base_model_llama-2-70b-chat-hf
```

base_model_llama-2-70b-chat-hf

276 GB

Ctrl+K

2 contributors

History: 1 commit

ValdoLab

emozilla

Duplicate from NousResearch/Llama-2-70b-chat-hf

93eaf04 over 2 years ago

Duplicated from NousResearch/Llama-2-70b-chat-hf

ValdoLab
/

base_model_llama-2-70b-chat-hf

Detected Pickle imports (4)

Detected Pickle imports (4)

Detected Pickle imports (4)

Detected Pickle imports (4)

Detected Pickle imports (4)

Detected Pickle imports (4)

Detected Pickle imports (4)

Detected Pickle imports (4)

Detected Pickle imports (4)

Detected Pickle imports (4)

Detected Pickle imports (4)

Detected Pickle imports (4)

Detected Pickle imports (4)

Detected Pickle imports (4)

Detected Pickle imports (3)