Instructions to use Sandiago21/llama-7b-hf-prompt-answering with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Sandiago21/llama-7b-hf-prompt-answering with Transformers:

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

pipe = pipeline("text-generation", model="Sandiago21/llama-7b-hf-prompt-answering")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Sandiago21/llama-7b-hf-prompt-answering")
model = AutoModelForCausalLM.from_pretrained("Sandiago21/llama-7b-hf-prompt-answering", device_map="auto")

PEFT
How to use Sandiago21/llama-7b-hf-prompt-answering with PEFT:
```
Task type is invalid.
```
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Sandiago21/llama-7b-hf-prompt-answering with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Sandiago21/llama-7b-hf-prompt-answering"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Sandiago21/llama-7b-hf-prompt-answering",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Sandiago21/llama-7b-hf-prompt-answering

SGLang

How to use Sandiago21/llama-7b-hf-prompt-answering 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 "Sandiago21/llama-7b-hf-prompt-answering" \
    --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": "Sandiago21/llama-7b-hf-prompt-answering",
		"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 "Sandiago21/llama-7b-hf-prompt-answering" \
        --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": "Sandiago21/llama-7b-hf-prompt-answering",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Sandiago21/llama-7b-hf-prompt-answering with Docker Model Runner:
```
docker model run hf.co/Sandiago21/llama-7b-hf-prompt-answering
```

Sandiago21 commited on Jun 12, 2023

Commit

f45f66c

1 Parent(s): 108c3ae

Update README.md

Browse files

Files changed (1) hide show

README.md +6 -6

README.md CHANGED Viewed

@@ -96,7 +96,7 @@ def generate_prompt(instruction: str, input_ctxt: str = None) -> str:
 Use the code below to get started with the model.
-1. You can git clone the repo, which contains also the artifacts for the base model for simplicity and completeness, and run the following code snippet to load the mode:
 ```python
 import torch
@@ -104,11 +104,12 @@ from peft import PeftConfig, PeftModel
 from transformers import GenerationConfig, LlamaTokenizer, LlamaForCausalLM
 MODEL_NAME = "Sandiago21/llama-7b-hf-prompt-answering"
 config = PeftConfig.from_pretrained(MODEL_NAME)
 model = LlamaForCausalLM.from_pretrained(
-    config.base_model_name_or_path,
     load_in_8bit=True,
     torch_dtype=torch.float16,
     device_map="auto",
@@ -132,6 +133,7 @@ if torch.__version__ >= "2":
 ```
 ### Example of Usage
 ```python
 instruction = "What is the capital city of Greece and with which countries does Greece border?"
 input_ctxt = None  # For some tasks, you can provide an input context to help the model generate a better response.
@@ -154,7 +156,7 @@ print(response)
 >>> The capital city of Greece is Athens and it borders Turkey, Bulgaria, Macedonia, Albania, and the Aegean Sea.
 ```
-2. You can also directly call the model from HuggingFace using the following code snippet:
 ```python
 import torch
@@ -162,12 +164,11 @@ from peft import PeftConfig, PeftModel
 from transformers import GenerationConfig, LlamaTokenizer, LlamaForCausalLM
 MODEL_NAME = "Sandiago21/llama-7b-hf-prompt-answering"
-BASE_MODEL = "decapoda-research/llama-7b-hf"
 config = PeftConfig.from_pretrained(MODEL_NAME)
 model = LlamaForCausalLM.from_pretrained(
-    BASE_MODEL,
     load_in_8bit=True,
     torch_dtype=torch.float16,
     device_map="auto",
@@ -191,7 +192,6 @@ if torch.__version__ >= "2":
 ```
 ### Example of Usage
 ```python
 instruction = "What is the capital city of Greece and with which countries does Greece border?"
 input_ctxt = None  # For some tasks, you can provide an input context to help the model generate a better response.

 Use the code below to get started with the model.
+1. You can directly call the model from HuggingFace using the following code snippet:
 ```python
 import torch
 from transformers import GenerationConfig, LlamaTokenizer, LlamaForCausalLM
 MODEL_NAME = "Sandiago21/llama-7b-hf-prompt-answering"
+BASE_MODEL = "decapoda-research/llama-7b-hf"
 config = PeftConfig.from_pretrained(MODEL_NAME)
 model = LlamaForCausalLM.from_pretrained(
+    BASE_MODEL,
     load_in_8bit=True,
     torch_dtype=torch.float16,
     device_map="auto",
 ```
 ### Example of Usage
 ```python
 instruction = "What is the capital city of Greece and with which countries does Greece border?"
 input_ctxt = None  # For some tasks, you can provide an input context to help the model generate a better response.
 >>> The capital city of Greece is Athens and it borders Turkey, Bulgaria, Macedonia, Albania, and the Aegean Sea.
 ```
+2. You can git clone the repo, which contains also the artifacts for the base model for simplicity and completeness, and run the following code snippet to load the mode:
 ```python
 import torch
 from transformers import GenerationConfig, LlamaTokenizer, LlamaForCausalLM
 MODEL_NAME = "Sandiago21/llama-7b-hf-prompt-answering"
 config = PeftConfig.from_pretrained(MODEL_NAME)
 model = LlamaForCausalLM.from_pretrained(
+    config.base_model_name_or_path,
     load_in_8bit=True,
     torch_dtype=torch.float16,
     device_map="auto",
 ```
 ### Example of Usage
 ```python
 instruction = "What is the capital city of Greece and with which countries does Greece border?"
 input_ctxt = None  # For some tasks, you can provide an input context to help the model generate a better response.