Instructions to use chargoddard/llama2-22b-blocktriangular with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use chargoddard/llama2-22b-blocktriangular with Transformers:

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

pipe = pipeline("text-generation", model="chargoddard/llama2-22b-blocktriangular")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("chargoddard/llama2-22b-blocktriangular")
model = AutoModelForCausalLM.from_pretrained("chargoddard/llama2-22b-blocktriangular")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use chargoddard/llama2-22b-blocktriangular with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "chargoddard/llama2-22b-blocktriangular"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "chargoddard/llama2-22b-blocktriangular",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/chargoddard/llama2-22b-blocktriangular

SGLang

How to use chargoddard/llama2-22b-blocktriangular 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 "chargoddard/llama2-22b-blocktriangular" \
    --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": "chargoddard/llama2-22b-blocktriangular",
		"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 "chargoddard/llama2-22b-blocktriangular" \
        --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": "chargoddard/llama2-22b-blocktriangular",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use chargoddard/llama2-22b-blocktriangular with Docker Model Runner:
```
docker model run hf.co/chargoddard/llama2-22b-blocktriangular
```

Similar to llama2-22b, but with BLOCK_DIAGONAL=false in the merge and twice the fine-tuning tokens.

Again, not intended for direct use - meant as a base for further tuning and merging.

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	46.86
ARC (25-shot)	58.28
HellaSwag (10-shot)	82.69
MMLU (5-shot)	54.53
TruthfulQA (0-shot)	39.23
Winogrande (5-shot)	75.93
GSM8K (5-shot)	11.22
DROP (3-shot)	6.17

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