Instructions to use raicrits/OpenLLama13b_Loquace_ITA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use raicrits/OpenLLama13b_Loquace_ITA with Transformers:

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

pipe = pipeline("text-generation", model="raicrits/OpenLLama13b_Loquace_ITA")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("raicrits/OpenLLama13b_Loquace_ITA", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use raicrits/OpenLLama13b_Loquace_ITA with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "raicrits/OpenLLama13b_Loquace_ITA"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "raicrits/OpenLLama13b_Loquace_ITA",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/raicrits/OpenLLama13b_Loquace_ITA

SGLang

How to use raicrits/OpenLLama13b_Loquace_ITA 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 "raicrits/OpenLLama13b_Loquace_ITA" \
    --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": "raicrits/OpenLLama13b_Loquace_ITA",
		"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 "raicrits/OpenLLama13b_Loquace_ITA" \
        --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": "raicrits/OpenLLama13b_Loquace_ITA",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use raicrits/OpenLLama13b_Loquace_ITA with Docker Model Runner:
```
docker model run hf.co/raicrits/OpenLLama13b_Loquace_ITA
```

stefanoscotta commited on Jul 25, 2023

Commit

8be35b4

1 Parent(s): d0c39e4

Update README.md

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@@ -16,7 +16,7 @@ An open-source LLaMa language model of 13b parameters fine-tuned to follow instr
 This model is an open-source LLM of 13b parameters based on [OpenLLaMA](https://github.com/openlm-research/open_llama), an open-source replica of Meta AI's LLaMA.
 The model was fine-tuned in order to follow instructions, as proposed in [Alpaca](https://github.com/tatsu-lab/stanford_alpaca),
-but using [LoRA](https://arxiv.org/pdf/2106.09685.pdf) technique and a bigger dataset of instruction/answers in italian, [cosimoiaia/Loquace-102k](cosimoiaia/Loquace-102k).
 This repository contains the model merged with the LoRA adapters obtained in the fine-tuning procedure.
@@ -25,7 +25,7 @@ This repository contains the model merged with the LoRA adapters obtained in the
 - **Model type:** LLM fine-tuned to follow instructions
 - **Language(s) (NLP):** Italian
 - **License:** [More Information Needed]
-- **Finetuned from model:** [openlm-research/open_llama_13b](openlm-research/open_llama_13b)
 ## Uses
@@ -73,7 +73,7 @@ Use the code below to get started with the model.
 ### Training Data
 <!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-The model was fine-tinuned on [cosimoiaia/Loquace-102k](cosimoiaia/Loquace-102k), a dataset of 102k question/answer pairs in italian.
 ### Training Procedure

 This model is an open-source LLM of 13b parameters based on [OpenLLaMA](https://github.com/openlm-research/open_llama), an open-source replica of Meta AI's LLaMA.
 The model was fine-tuned in order to follow instructions, as proposed in [Alpaca](https://github.com/tatsu-lab/stanford_alpaca),
+but using [LoRA](https://arxiv.org/pdf/2106.09685.pdf) technique and a bigger dataset of instruction/answers in italian, [cosimoiaia/Loquace-102k](https://huggingface.co/datasets/cosimoiaia/Loquace-102k/viewer/cosimoiaia--Loquace-102k).
 This repository contains the model merged with the LoRA adapters obtained in the fine-tuning procedure.
 - **Model type:** LLM fine-tuned to follow instructions
 - **Language(s) (NLP):** Italian
 - **License:** [More Information Needed]
+- **Finetuned from model:** [openlm-research/open_llama_13b](https://huggingface.co/openlm-research/open_llama_13b)
 ## Uses
 ### Training Data
 <!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+The model was fine-tinuned on [cosimoiaia/Loquace-102k](https://huggingface.co/datasets/cosimoiaia/Loquace-102k/viewer/cosimoiaia--Loquace-102k), a dataset of 102k question/answer pairs in italian.
 ### Training Procedure