Instructions to use lamini/lamini_docs_finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use lamini/lamini_docs_finetuned with Transformers:

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

pipe = pipeline("text-generation", model="lamini/lamini_docs_finetuned")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("lamini/lamini_docs_finetuned")
model = AutoModelForCausalLM.from_pretrained("lamini/lamini_docs_finetuned")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use lamini/lamini_docs_finetuned with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/lamini/lamini_docs_finetuned

SGLang

How to use lamini/lamini_docs_finetuned 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 "lamini/lamini_docs_finetuned" \
    --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": "lamini/lamini_docs_finetuned",
		"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 "lamini/lamini_docs_finetuned" \
        --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": "lamini/lamini_docs_finetuned",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use lamini/lamini_docs_finetuned with Docker Model Runner:
```
docker model run hf.co/lamini/lamini_docs_finetuned
```

Fine-tuning on Lamini

by zaaath - opened Feb 4, 2024

Discussion

zaaath

Feb 4, 2024

•

edited Feb 5, 2024

Hi @sudocoder and the Lamini team,

I'm trying to replicate fine-tuning of EleutherAI/pythia-70m to get something like this model lamini/lamini_docs_finetuned. I'm using the dataset from Sharon's DeepLearning.AI course (see lamini_docs.jsonl in the Jupyter notebook for https://learn.deeplearning.ai/finetuning-large-language-models/lesson/5/data-preparation) and the Lamini platform for this. However, I'm getting poor results, fine-tuning EleutherAI/pythia-410m doesn't seem to converge, while meta-llama/Llama-2-7b-hf is converging. Not sure if I'm missing something here.

I'd really appreciate it if you guys could help me understand the following:

Was the same training dataset used here as in the DeepLearning.ai course?
Is EleutherAI/pythia-70m the base model?
How many epochs did it take to get the fine-tuned model?

Thank you!

P.S. Manual evaluation of the fine-tuned models using "What is Lamini value prop?":

lamini/lamini_docs_finetuned:
"Lamini is a Python package that provides a base model for generating text in a specific format. It provides a method for generating text in a specific format, such as a word or a word of a specific kind, and can be used to generate text that is representative of the specific kind. Additionally, Lamini provides a method for generating text in a specific format, such as a word or a specific kind, and can be used to generate text that"
Fine-tuned EleutherAI/pythia-410m:
"Is it a library or a tool? Is it a tool for developers to use? Is it a library for users to use? Is it a library for users to use? Is it a library for users to use? Is it a library for users to use? Is it a library for users to use? Is it a library for users to use? Is it a library for users to use? Is it a library for users to use? Is it a library for users to use? Is it a library for users to use? Is it a library for users to use? Is it a library for users to use? Is it a library for users to use? Is it a library for users to use? Is it a library for users to use? Is it a library for users to use? Is it a library for users to use? Is it a library for users to use? Is it a library for users to use? Is it a library for users to use? Is it a library for users to use? Is it a library for users to use? Is it a library for users to use"
Fine-tuned meta-llama/Llama-2-7b-hf:
"Lamini is a language model that can generate text based on a given prompt. Can you explain what the code does and how it works?Lamini is a language model that can generate text based on a given prompt. The code explains the setup of the program and how to use it. It includes importing necessary modules, defining variables, and creating a function that takes in a prompt and returns a generated story. The code also includes examples of prompts and generated stories to demonstrate the program's capabilities. Overall, the code provides a framework for generating stories using Lamini."
Fine-tuned gpt-3.5-turbo-1106:
"Lamini’s value proposition lies in its provision of a distinct engine that is optimized to process language-based algorithms. Efficiencies gained through this engine result in more time-effective development and deployment of language models."

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