Instructions to use OpenAssistant/llama2-13b-megacode2-oasst with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OpenAssistant/llama2-13b-megacode2-oasst with Transformers:

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

pipe = pipeline("text-generation", model="OpenAssistant/llama2-13b-megacode2-oasst")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("OpenAssistant/llama2-13b-megacode2-oasst")
model = AutoModelForCausalLM.from_pretrained("OpenAssistant/llama2-13b-megacode2-oasst")

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use OpenAssistant/llama2-13b-megacode2-oasst with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "OpenAssistant/llama2-13b-megacode2-oasst"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "OpenAssistant/llama2-13b-megacode2-oasst",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/OpenAssistant/llama2-13b-megacode2-oasst

SGLang

How to use OpenAssistant/llama2-13b-megacode2-oasst 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 "OpenAssistant/llama2-13b-megacode2-oasst" \
    --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": "OpenAssistant/llama2-13b-megacode2-oasst",
		"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 "OpenAssistant/llama2-13b-megacode2-oasst" \
        --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": "OpenAssistant/llama2-13b-megacode2-oasst",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use OpenAssistant/llama2-13b-megacode2-oasst with Docker Model Runner:
```
docker model run hf.co/OpenAssistant/llama2-13b-megacode2-oasst
```

Adding Evaluation Results

by leaderboard-pr-bot - opened Nov 17, 2023

base: refs/heads/main

←

from: refs/pr/2

Discussion Files changed

+14

-0

Files changed (1) hide show

README.md +14 -0

README.md CHANGED Viewed

@@ -25,3 +25,17 @@ Multi-line:
 - The open-source [epfLLM/Megatron-LLM](https://github.com/epfLLM/Megatron-LLM) trainer was used for fine-tuning.
 - [rombodawg](https://huggingface.co/rombodawg) curated and published [LosslessMegaCodeTrainingV2_1m_Evol_Uncensored](https://huggingface.co/datasets/rombodawg/LosslessMegaCodeTrainingV2_1m_Evol_Uncensored)
 - [andreaskoepf](https://github.com/andreaskoepf/) prepared & orchestrated the training.

 - The open-source [epfLLM/Megatron-LLM](https://github.com/epfLLM/Megatron-LLM) trainer was used for fine-tuning.
 - [rombodawg](https://huggingface.co/rombodawg) curated and published [LosslessMegaCodeTrainingV2_1m_Evol_Uncensored](https://huggingface.co/datasets/rombodawg/LosslessMegaCodeTrainingV2_1m_Evol_Uncensored)
 - [andreaskoepf](https://github.com/andreaskoepf/) prepared & orchestrated the training.
+# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
+Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_OpenAssistant__llama2-13b-megacode2-oasst)
+| Metric                | Value                     |
+|-----------------------|---------------------------|
+| Avg.                  | 49.61   |
+| ARC (25-shot)         | 60.67          |
+| HellaSwag (10-shot)   | 81.93    |
+| MMLU (5-shot)         | 57.38         |
+| TruthfulQA (0-shot)   | 47.85   |
+| Winogrande (5-shot)   | 76.16   |
+| GSM8K (5-shot)        | 15.54        |
+| DROP (3-shot)         | 7.74         |