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

beea936

1 Parent(s): eec9751

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@@ -1,7 +1,7 @@
 ---
 license: other
 ---
-This is one of the first models trained on the LosslessMegaCodeTrainingV2_1m_Evol_Uncensored dataset. The version of the dataset used for this model was poorly filtered on some loose parameters that arent anything to write home about but plans for much more refined filtering are int he works
 The link for the full dataset is bellow:

 ---
 license: other
 ---
+This is one of the first models trained on the LosslessMegaCodeTrainingV2_1m_Evol_Uncensored dataset. The version of the dataset used for this model was poorly filtered on some loose parameters that arent anything to write home about but plans for much more refined filtering are in the works
 The link for the full dataset is bellow: