Instructions to use andreaskoepf/llama2-7b-megacode2_min100 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use andreaskoepf/llama2-7b-megacode2_min100 with Transformers:

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

pipe = pipeline("text-generation", model="andreaskoepf/llama2-7b-megacode2_min100")

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

tokenizer = AutoTokenizer.from_pretrained("andreaskoepf/llama2-7b-megacode2_min100")
model = AutoModelForCausalLM.from_pretrained("andreaskoepf/llama2-7b-megacode2_min100", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use andreaskoepf/llama2-7b-megacode2_min100 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_min100"
# 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_min100",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/andreaskoepf/llama2-7b-megacode2_min100

SGLang

How to use andreaskoepf/llama2-7b-megacode2_min100 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_min100" \
    --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_min100",
		"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_min100" \
        --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_min100",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use andreaskoepf/llama2-7b-megacode2_min100 with Docker Model Runner:
```
docker model run hf.co/andreaskoepf/llama2-7b-megacode2_min100
```

andreaskoepf commited on Aug 13, 2023

Commit

27ea8a3

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@@ -3,4 +3,5 @@ license: other
 datasets:
 - rombodawg/LosslessMegaCodeTrainingV2_1m_Evol_Uncensored
 ---
-- [wandb](https://wandb.ai/open-assistant/epfl-mt-sft/runs/run17_megacode_min100)

 datasets:
 - rombodawg/LosslessMegaCodeTrainingV2_1m_Evol_Uncensored
 ---
+- wandb: [open-assistant/public-sft/runs/run17_megacode_min100](https://wandb.ai/open-assistant/public-sft/runs/run17_megacode_min100)
+- [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%2F2023-08-12_andreaskoepf_llama2-7b-megacode2_min100_sampling_noprefix2.json)