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

Libraries

How to use rombodawg/LosslessMegaCoder-llama2-7b-mini with Transformers:

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

pipe = pipeline("text-generation", model="rombodawg/LosslessMegaCoder-llama2-7b-mini")

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

tokenizer = AutoTokenizer.from_pretrained("rombodawg/LosslessMegaCoder-llama2-7b-mini")
model = AutoModelForCausalLM.from_pretrained("rombodawg/LosslessMegaCoder-llama2-7b-mini", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use rombodawg/LosslessMegaCoder-llama2-7b-mini with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/rombodawg/LosslessMegaCoder-llama2-7b-mini

SGLang

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

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

rombodawg commited on Aug 13, 2023

Commit

6d60562

1 Parent(s): d5a624d

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@@ -29,6 +29,10 @@ multi-line:
 """
 ```
 The link for the full dataset is bellow:
 - https://huggingface.co/datasets/rombodawg/LosslessMegaCodeTrainingV2_1m_Evol_Uncensored

 """
 ```
+Benchmarks for the model can be found at the link bellow the model here is called (andreaskoepf/llama2-7b-megacode2_min100)
+- https://tju01.github.io/FastEval-OpenAssistant/
 The link for the full dataset is bellow:
 - https://huggingface.co/datasets/rombodawg/LosslessMegaCodeTrainingV2_1m_Evol_Uncensored