Instructions to use TheBloke/LosslessMegaCoder-Llama2-13B-Mini-AWQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TheBloke/LosslessMegaCoder-Llama2-13B-Mini-AWQ with Transformers:

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

pipe = pipeline("text-generation", model="TheBloke/LosslessMegaCoder-Llama2-13B-Mini-AWQ")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("TheBloke/LosslessMegaCoder-Llama2-13B-Mini-AWQ")
model = AutoModelForCausalLM.from_pretrained("TheBloke/LosslessMegaCoder-Llama2-13B-Mini-AWQ")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use TheBloke/LosslessMegaCoder-Llama2-13B-Mini-AWQ with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "TheBloke/LosslessMegaCoder-Llama2-13B-Mini-AWQ"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TheBloke/LosslessMegaCoder-Llama2-13B-Mini-AWQ",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/TheBloke/LosslessMegaCoder-Llama2-13B-Mini-AWQ

SGLang

How to use TheBloke/LosslessMegaCoder-Llama2-13B-Mini-AWQ 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 "TheBloke/LosslessMegaCoder-Llama2-13B-Mini-AWQ" \
    --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": "TheBloke/LosslessMegaCoder-Llama2-13B-Mini-AWQ",
		"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 "TheBloke/LosslessMegaCoder-Llama2-13B-Mini-AWQ" \
        --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": "TheBloke/LosslessMegaCoder-Llama2-13B-Mini-AWQ",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use TheBloke/LosslessMegaCoder-Llama2-13B-Mini-AWQ with Docker Model Runner:
```
docker model run hf.co/TheBloke/LosslessMegaCoder-Llama2-13B-Mini-AWQ
```

TheBloke commited on Sep 27, 2023

Commit

23cf88f

1 Parent(s): 9d9ee50

Update base_model formatting

Browse files

Files changed (1) hide show

README.md +3 -3

README.md CHANGED Viewed

@@ -1,11 +1,11 @@
 ---
-base_model: https://huggingface.co/rombodawg/LosslessMegaCoder-llama2-13b-mini
 datasets:
 - rombodawg/LosslessMegaCodeTrainingV2_1m_Evol_Uncensored
 inference: false
-license: llama2
 model_creator: Rombo Dawg
-model_name: LosslessMegaCoder Llama2 13B Mini
 model_type: llama
 prompt_template: '<|im_start|>system

 ---
+license: llama2
 datasets:
 - rombodawg/LosslessMegaCodeTrainingV2_1m_Evol_Uncensored
+model_name: LosslessMegaCoder Llama2 13B Mini
+base_model: rombodawg/LosslessMegaCoder-llama2-13b-mini
 inference: false
 model_creator: Rombo Dawg
 model_type: llama
 prompt_template: '<|im_start|>system