Instructions to use Brillibits/Instruct_Llama70B_Dolly15k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Brillibits/Instruct_Llama70B_Dolly15k with Transformers:

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

pipe = pipeline("text-generation", model="Brillibits/Instruct_Llama70B_Dolly15k")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Brillibits/Instruct_Llama70B_Dolly15k")
model = AutoModelForCausalLM.from_pretrained("Brillibits/Instruct_Llama70B_Dolly15k")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Brillibits/Instruct_Llama70B_Dolly15k with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Brillibits/Instruct_Llama70B_Dolly15k"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Brillibits/Instruct_Llama70B_Dolly15k",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Brillibits/Instruct_Llama70B_Dolly15k

SGLang

How to use Brillibits/Instruct_Llama70B_Dolly15k 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 "Brillibits/Instruct_Llama70B_Dolly15k" \
    --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": "Brillibits/Instruct_Llama70B_Dolly15k",
		"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 "Brillibits/Instruct_Llama70B_Dolly15k" \
        --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": "Brillibits/Instruct_Llama70B_Dolly15k",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Brillibits/Instruct_Llama70B_Dolly15k with Docker Model Runner:
```
docker model run hf.co/Brillibits/Instruct_Llama70B_Dolly15k
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Instruct_Llama70B_Dolly15k

Fine-tuned from Llama-2-70B，used Dolly15k for the dataset. 80% for training, 15% validation, 5% test. Trained for 1.5 epochs using QLora. Trained with 1024 context window.

Model Details

Trained by: trained by Brillibits. See YouTube as well.
Model type: Instruct_Llama70B_Dolly15k is an auto-regressive language model based on the Llama 2 transformer architecture.
Language(s): English
License for Instruct_Llama70B_Dolly15ks: llama2 license

Prompting

Prompt Template With Context

Write a 10-line poem about a given topic

Input:

The topic is about racecars

Output:

Prompt Template Without Context

Who was the was the second president of the United States?

Output:

Professional Assistance

This model and other models like it are great, but where LLMs hold the most promise is when they are applied on custom data to automate a wide variety of tasks

If you have a dataset and want to see if you might be able to apply that data to automate some tasks, and you are looking for professional assistance, contact me here

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	60.97
ARC (25-shot)	68.34
HellaSwag (10-shot)	87.21
MMLU (5-shot)	69.52
TruthfulQA (0-shot)	46.46
Winogrande (5-shot)	84.29
GSM8K (5-shot)	42.68
DROP (3-shot)	28.26

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