Instructions to use CONCISE/LLaMa_V2-13B-Instruct-Uncensored-GGML with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use CONCISE/LLaMa_V2-13B-Instruct-Uncensored-GGML with Transformers:

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

pipe = pipeline("text-generation", model="CONCISE/LLaMa_V2-13B-Instruct-Uncensored-GGML")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("CONCISE/LLaMa_V2-13B-Instruct-Uncensored-GGML")
model = AutoModelForCausalLM.from_pretrained("CONCISE/LLaMa_V2-13B-Instruct-Uncensored-GGML")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use CONCISE/LLaMa_V2-13B-Instruct-Uncensored-GGML with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "CONCISE/LLaMa_V2-13B-Instruct-Uncensored-GGML"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "CONCISE/LLaMa_V2-13B-Instruct-Uncensored-GGML",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/CONCISE/LLaMa_V2-13B-Instruct-Uncensored-GGML

SGLang

How to use CONCISE/LLaMa_V2-13B-Instruct-Uncensored-GGML 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 "CONCISE/LLaMa_V2-13B-Instruct-Uncensored-GGML" \
    --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": "CONCISE/LLaMa_V2-13B-Instruct-Uncensored-GGML",
		"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 "CONCISE/LLaMa_V2-13B-Instruct-Uncensored-GGML" \
        --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": "CONCISE/LLaMa_V2-13B-Instruct-Uncensored-GGML",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use CONCISE/LLaMa_V2-13B-Instruct-Uncensored-GGML with Docker Model Runner:
```
docker model run hf.co/CONCISE/LLaMa_V2-13B-Instruct-Uncensored-GGML
```

LLaMa_V2-13B-Instruct-Uncensored-GGML

Quantized LLaMa.V2 13B model weights - Instruction based

Big thank you to Eric Hartford for his work aiding the creation of datasets such as:
- wizardlm_evol_instruct_V2_196k_unfiltered_merged_split
- wizard_vicuna_70k_unfiltered

Such datasets play a big role in accurately addressing model biases, without sacrificing performance