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

4c2cfcd

verified ·

1 Parent(s): c93c33a

Update README.md

Browse files

Files changed (1) hide show

README.md +14 -3

README.md CHANGED Viewed

@@ -15,6 +15,18 @@ tags:
 - peft
 - portuguese
 - instruct
 ---
 <hr>
@@ -75,8 +87,7 @@ tokenizer = AutoTokenizer.from_pretrained('lrds-code/boana-7b-instruct')
 instruction = 'Como desenvolver habilidades de programação em python?'
 text = GeneratePrompt(input='', instruction=instruction)
 inputs = tokenizer(text, return_tensors='pt')
-outputs = model.generate(input_ids=inputs['input_ids'], attention_mask=inputs['attention_mask'], generation_config=model_config, repetition_penalty=1.1)
 # repetition_penalty:
 # Observamos que em alguns casos de entrada o modelo repetiu excessivamente algumas palavras.
 # Esse parâmetro é usado para tentar reduzir esse comportamento.
@@ -160,7 +171,7 @@ tokenizer = AutoTokenizer.from_pretrained('lrds-code/boana-7b-instruct')
 instruction = 'Como desenvolver habilidades de programação em python?'
 text = GeneratePrompt(input='', instruction=instruction)
 inputs = tokenizer(text, return_tensors='pt')
-outputs = model.generate(input_ids=inputs['input_ids'], attention_mask=inputs['attention_mask'], generation_config=model_config, repetition_penalty=1.1)
 # repetition_penalty:
 # We observed that in some input cases the model began to repeat words excessively in its output.

 - peft
 - portuguese
 - instruct
+task:
+  type: text-generation
+  dataset:
+    type: Muennighoff/xwinograd
+    name: XWinograd (pt)
+    config: pt
+    split: test
+  metrics:
+    type: Accuracy
+    value: 50.19
 ---
 <hr>
 instruction = 'Como desenvolver habilidades de programação em python?'
 text = GeneratePrompt(input='', instruction=instruction)
 inputs = tokenizer(text, return_tensors='pt')
+outputs = model.generate(input_ids=inputs['input_ids'], attention_mask=inputs['attention_mask'], generation_config=model_config, repetition_penalty=1.1, do_sample=False
 # repetition_penalty:
 # Observamos que em alguns casos de entrada o modelo repetiu excessivamente algumas palavras.
 # Esse parâmetro é usado para tentar reduzir esse comportamento.
 instruction = 'Como desenvolver habilidades de programação em python?'
 text = GeneratePrompt(input='', instruction=instruction)
 inputs = tokenizer(text, return_tensors='pt')
+outputs = model.generate(input_ids=inputs['input_ids'], attention_mask=inputs['attention_mask'], generation_config=model_config, repetition_penalty=1.1, do_sample=False)
 # repetition_penalty:
 # We observed that in some input cases the model began to repeat words excessively in its output.