Instructions to use andreaskoepf/llama2-7b-megacode2_frac05 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use andreaskoepf/llama2-7b-megacode2_frac05 with Transformers:

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

pipe = pipeline("text-generation", model="andreaskoepf/llama2-7b-megacode2_frac05")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("andreaskoepf/llama2-7b-megacode2_frac05")
model = AutoModelForCausalLM.from_pretrained("andreaskoepf/llama2-7b-megacode2_frac05", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use andreaskoepf/llama2-7b-megacode2_frac05 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "andreaskoepf/llama2-7b-megacode2_frac05"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "andreaskoepf/llama2-7b-megacode2_frac05",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/andreaskoepf/llama2-7b-megacode2_frac05

SGLang

How to use andreaskoepf/llama2-7b-megacode2_frac05 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 "andreaskoepf/llama2-7b-megacode2_frac05" \
    --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": "andreaskoepf/llama2-7b-megacode2_frac05",
		"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 "andreaskoepf/llama2-7b-megacode2_frac05" \
        --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": "andreaskoepf/llama2-7b-megacode2_frac05",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use andreaskoepf/llama2-7b-megacode2_frac05 with Docker Model Runner:
```
docker model run hf.co/andreaskoepf/llama2-7b-megacode2_frac05
```

andreaskoepf commited on Aug 14, 2023

Commit

e104b4c

1 Parent(s): 4e5fad3

Update README.md

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@@ -1,4 +1,5 @@
 ---
 license: other
 ---
-- wandb: [run18_megacode2_frac05](https://wandb.ai/open-assistant/epfl-mt-sft/runs/run18_megacode2_frac05)

 ---
 license: other
 ---
+- wandb: [run18_megacode2_frac05](https://wandb.ai/open-assistant/epfl-mt-sft/runs/run18_megacode2_frac05)
+- [sampling report](https://open-assistant.github.io/oasst-model-eval/?f=https%3A%2F%2Fraw.githubusercontent.com%2FOpen-Assistant%2Foasst-model-eval%2Fmain%2Fsampling_reports%2Foasst-pretrained%2F2023-08-14_andreaskoepf_llama2-7b-megacode2_frac05_sampling_noprefix2.json)