Instructions to use lrds-code/boana-7b-instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use lrds-code/boana-7b-instruct with Transformers:

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

pipe = pipeline("text-generation", model="lrds-code/boana-7b-instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("lrds-code/boana-7b-instruct")
model = AutoModelForCausalLM.from_pretrained("lrds-code/boana-7b-instruct", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

PEFT
How to use lrds-code/boana-7b-instruct with PEFT:
```
Task type is invalid.
```
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use lrds-code/boana-7b-instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "lrds-code/boana-7b-instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "lrds-code/boana-7b-instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/lrds-code/boana-7b-instruct

SGLang

How to use lrds-code/boana-7b-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 "lrds-code/boana-7b-instruct" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "lrds-code/boana-7b-instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "lrds-code/boana-7b-instruct" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "lrds-code/boana-7b-instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use lrds-code/boana-7b-instruct with Docker Model Runner:
```
docker model run hf.co/lrds-code/boana-7b-instruct
```

lrds-code commited on Jan 23, 2024

Commit

5ba825a

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Update README.md

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@@ -28,7 +28,7 @@ model-index:
       split: test
     metrics:
     - type: Accuracy
-      value: 50.19
 ---
 <hr>
@@ -64,7 +64,7 @@ Usamos o mesmo prompt usado no fine-tune do modelo [Cabrita](https://huggingface
 ## Avaliação
-O modelo foi avaliado no conjunto de dados [XWinograd - pt](https://huggingface.co/datasets/Muennighoff/xwinograd) que consiste em uma simples task de fill-mask. Alcançamos resultados que variaram entre 40.03 e 50.19 de acurácia, dependendo da configuração de prompt utilizada.
 Não foi aplicado nenhum tipo de correção na saída do modelo, apenas realizamos uma etapa de pós processamento para extração do valor de interesse.
 ## Como Usar
@@ -153,7 +153,7 @@ We used the same prompt used in the fine-tune of the [Cabrita](https://huggingfa
 ## Evaluation
-The model was evaluated on the dataset [XWinograd - pt](https://huggingface.co/datasets/Muennighoff/xwinograd) which consists of a simple fill-mask task. We achieved results that varied between 40.03 and 50.19 accuracy, depending on the prompt configuration used.
 No type of correction was applied to the model output, we only carried out a post-processing step to extract the value of interest.
 ## How to Use

       split: test
     metrics:
     - type: Accuracy
+      value: 50.57
 ---
 <hr>
 ## Avaliação
+O modelo foi avaliado no conjunto de dados [XWinograd - pt](https://huggingface.co/datasets/Muennighoff/xwinograd) que consiste em uma simples task de fill-mask. Alcançamos resultados que variaram entre 40.03 e 50.57 de acurácia, dependendo da configuração de prompt utilizada.
 Não foi aplicado nenhum tipo de correção na saída do modelo, apenas realizamos uma etapa de pós processamento para extração do valor de interesse.
 ## Como Usar
 ## Evaluation
+The model was evaluated on the dataset [XWinograd - pt](https://huggingface.co/datasets/Muennighoff/xwinograd) which consists of a simple fill-mask task. We achieved results that varied between 40.03 and 50.57 accuracy, depending on the prompt configuration used.
 No type of correction was applied to the model output, we only carried out a post-processing step to extract the value of interest.
 ## How to Use