Instructions to use MBZUAI/LaMini-Neo-125M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MBZUAI/LaMini-Neo-125M with Transformers:

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

pipe = pipeline("text-generation", model="MBZUAI/LaMini-Neo-125M")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("MBZUAI/LaMini-Neo-125M")
model = AutoModelForCausalLM.from_pretrained("MBZUAI/LaMini-Neo-125M", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use MBZUAI/LaMini-Neo-125M with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MBZUAI/LaMini-Neo-125M"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MBZUAI/LaMini-Neo-125M",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/MBZUAI/LaMini-Neo-125M

SGLang

How to use MBZUAI/LaMini-Neo-125M 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 "MBZUAI/LaMini-Neo-125M" \
    --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": "MBZUAI/LaMini-Neo-125M",
		"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 "MBZUAI/LaMini-Neo-125M" \
        --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": "MBZUAI/LaMini-Neo-125M",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use MBZUAI/LaMini-Neo-125M with Docker Model Runner:
```
docker model run hf.co/MBZUAI/LaMini-Neo-125M
```

macabdul9 commited on Apr 25, 2023

Commit

f2042a5

1 Parent(s): 7ab66c7

Update README.md

Browse files

Files changed (1) hide show

README.md +4 -4

README.md CHANGED Viewed

@@ -99,7 +99,7 @@ We recommend using the model to respond to human instructions written in natural
 Since this decoder-only model is fine-tuned with wrapper text, we suggest using the same wrapper text to achieve the best performance.
 See the example on the right or the code below.
-We now show you how to load and use our model using HuggingFace `pipline()`.
 ```python
 # pip install -q transformers
@@ -107,15 +107,15 @@ from transformers import pipeline
 checkpoint = "{model_name}"
-model = pipeline('text-generation', model=checkpoint, use_auth_token=True)
 instruction = 'Please let me know your thoughts on the given place and why you think it deserves to be visited: \n"Barcelona, Spain"'
 input_prompt = f"Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n{instruction}\n\n### Response:"
-generated_text = generator(input_prompt, max_length=512, do_sample=True)[0]['generated_text']
-print("Response": generated_text)
 ```
 ## Training Procedure

 Since this decoder-only model is fine-tuned with wrapper text, we suggest using the same wrapper text to achieve the best performance.
 See the example on the right or the code below.
+We now show you how to load and use our model using HuggingFace `pipeline()`.
 ```python
 # pip install -q transformers
 checkpoint = "{model_name}"
+model = pipeline('text-generation', model = checkpoint)
 instruction = 'Please let me know your thoughts on the given place and why you think it deserves to be visited: \n"Barcelona, Spain"'
 input_prompt = f"Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n{instruction}\n\n### Response:"
+generated_text = model(input_prompt, max_length=512, do_sample=True)[0]['generated_text']
+print("Response", generated_text)
 ```
 ## Training Procedure