Instructions to use harborwater/open-llama-3b-everything-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use harborwater/open-llama-3b-everything-v2 with Transformers:

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

pipe = pipeline("text-generation", model="harborwater/open-llama-3b-everything-v2")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("harborwater/open-llama-3b-everything-v2")
model = AutoModelForCausalLM.from_pretrained("harborwater/open-llama-3b-everything-v2")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use harborwater/open-llama-3b-everything-v2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "harborwater/open-llama-3b-everything-v2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "harborwater/open-llama-3b-everything-v2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/harborwater/open-llama-3b-everything-v2

SGLang

How to use harborwater/open-llama-3b-everything-v2 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 "harborwater/open-llama-3b-everything-v2" \
    --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": "harborwater/open-llama-3b-everything-v2",
		"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 "harborwater/open-llama-3b-everything-v2" \
        --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": "harborwater/open-llama-3b-everything-v2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use harborwater/open-llama-3b-everything-v2 with Docker Model Runner:
```
docker model run hf.co/harborwater/open-llama-3b-everything-v2
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Trained on 3 epochs of the totally-not-an-llm/EverythingLM-data-V2-sharegpt dataset.

### HUMAN:
{prompt}

### RESPONSE:
<leave a newline for the model to answer>

note: Changed a few of the finetuning parameters this time around. I have no idea if its any good but Feel free to give it a try!

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	36.29
ARC (25-shot)	42.83
HellaSwag (10-shot)	73.28
MMLU (5-shot)	26.87
TruthfulQA (0-shot)	37.26
Winogrande (5-shot)	66.61
GSM8K (5-shot)	1.59
DROP (3-shot)	5.61

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Safetensors

Model size

3B params

Tensor type

F32

F16

Model tree for harborwater/open-llama-3b-everything-v2

Quantizations

1 model

harborwater
/

open-llama-3b-everything-v2

Open LLM Leaderboard Evaluation Results

Model tree for harborwater/open-llama-3b-everything-v2

Dataset used to train harborwater/open-llama-3b-everything-v2