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

368eda3

verified ·

1 Parent(s): 4c2cfcd

Update README.md

Browse files

Files changed (1) hide show

README.md +13 -11

README.md CHANGED Viewed

@@ -16,17 +16,19 @@ tags:
 - portuguese
 - instruct
-task:
-  type: text-generation
-  dataset:
-    type: Muennighoff/xwinograd
-    name: XWinograd (pt)
-    config: pt
-    split: test
-  metrics:
-    type: Accuracy
-    value: 50.19
 ---
 <hr>

 - portuguese
 - instruct
+model-index:
+- name: boana-7b-instruct
+  results:
+  - task:
+      type: text-generation
+    dataset:
+      type: Muennighoff/xwinograd
+      name: XWinograd (pt)
+      config: pt
+      split: test
+    metrics:
+    - type: Accuracy
+      value: 50.19
 ---
 <hr>