Instructions to use selfrag/selfrag_llama2_7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use selfrag/selfrag_llama2_7b with Transformers:

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

pipe = pipeline("text-generation", model="selfrag/selfrag_llama2_7b")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("selfrag/selfrag_llama2_7b")
model = AutoModelForCausalLM.from_pretrained("selfrag/selfrag_llama2_7b")

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use selfrag/selfrag_llama2_7b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "selfrag/selfrag_llama2_7b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "selfrag/selfrag_llama2_7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/selfrag/selfrag_llama2_7b

SGLang

How to use selfrag/selfrag_llama2_7b 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 "selfrag/selfrag_llama2_7b" \
    --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": "selfrag/selfrag_llama2_7b",
		"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 "selfrag/selfrag_llama2_7b" \
        --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": "selfrag/selfrag_llama2_7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use selfrag/selfrag_llama2_7b with Docker Model Runner:
```
docker model run hf.co/selfrag/selfrag_llama2_7b
```

Getting error while trying to infer in huggingface

by jerinmathews - opened Jan 3, 2024

Discussion

jerinmathews

Jan 3, 2024

•

edited Jan 3, 2024

Error: DownloadError
2024/01/02 17:05:42 ~ {"timestamp":"2024-01-03T01:05:42.420564Z","level":"ERROR","fields":{"message":"Download encountered an error: Traceback (most recent call last):\n\n File "/opt/conda/lib/python3.10/site-packages/peft/utils/config.py", line 117, in from_pretrained\n config_file = hf_hub_download(\n\n File "/opt/conda/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py", line 110, in _inner_fn\n validate_repo_id(arg_value)\n\n File "/opt/conda/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py", line 164, in validate_repo_id\n raise

HFValidationError(\n\nhuggingface_hub.utils.validators.HFValidationError: Repo id must use alphanumeric chars or '-', '', '.', '--' and '..' are forbidden, '-' and '.' cannot start or end the name, max length is 96: '/repository'.\n\n\nDuring handling of the above exception, another exception occurred:\n\n\nTraceback (most recent call last):\n\n File "/opt/conda/bin/text-generation-server", line 8, in \n sys.exit(app())\n\n File "/opt/conda/lib/python3.10/site-packages/text_generation_server/cli.py", line 204, in download_weights\n utils.download_and_unload_peft(\n\n File "/opt/conda/lib/python3.10/site-packages/text_generation_server/utils/peft.py", line 24, in download_and_unload_peft\n model = AutoPeftModelForSeq2SeqLM.from_pretrained(\n\n File "/opt/conda/lib/python3.10/site-packages/peft/auto.py", line 69, in from_pretrained\n peft_config = PeftConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)\n\n File "/opt/conda/lib/python3.10/site-packages/peft/utils/config.py", line 121, in from_pretrained\n raise ValueError(f"Can't find '{CONFIG_NAME}' at '{pretrained_model_name_or_path}'")\n\nValueError: Can't find 'adapter_config.json' at '/repository'\n\n"},"target":"text_generation_launcher","span":{"name":"download"},"spans":[{"name":"download"}]}

akariasai

selfrag org Jan 22, 2024

Hi! I've never tried to load the model from PEFT, so I am not sure why this happens... If possible, I recommend loading the model with vllm, which provide a much faster inference!

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