Instructions to use totally-not-an-llm/EverythingLM-13b-V2-16k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use totally-not-an-llm/EverythingLM-13b-V2-16k with Transformers:

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

pipe = pipeline("text-generation", model="totally-not-an-llm/EverythingLM-13b-V2-16k")

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

tokenizer = AutoTokenizer.from_pretrained("totally-not-an-llm/EverythingLM-13b-V2-16k")
model = AutoModelForCausalLM.from_pretrained("totally-not-an-llm/EverythingLM-13b-V2-16k", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use totally-not-an-llm/EverythingLM-13b-V2-16k with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "totally-not-an-llm/EverythingLM-13b-V2-16k"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "totally-not-an-llm/EverythingLM-13b-V2-16k",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/totally-not-an-llm/EverythingLM-13b-V2-16k

SGLang

How to use totally-not-an-llm/EverythingLM-13b-V2-16k 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 "totally-not-an-llm/EverythingLM-13b-V2-16k" \
    --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": "totally-not-an-llm/EverythingLM-13b-V2-16k",
		"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 "totally-not-an-llm/EverythingLM-13b-V2-16k" \
        --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": "totally-not-an-llm/EverythingLM-13b-V2-16k",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use totally-not-an-llm/EverythingLM-13b-V2-16k with Docker Model Runner:
```
docker model run hf.co/totally-not-an-llm/EverythingLM-13b-V2-16k
```

totally-not-an-llm commited on Aug 22, 2023

Commit

4717edd

1 Parent(s): 03b190f

Update README.md

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@@ -10,14 +10,6 @@ Introducing EverythingLM, a llama-2 based, general-purpose 13b model with 16k co
 The model is completely uncensored.
-### GGML quants:
-soon
-Make sure to use correct rope scaling settings:
-`-c 16384 --rope-freq-base 10000 --rope-freq-scale 0.25`
-### GPTQ quants:
-soon
 ### Notable features:
 - Automatically triggered CoT reasoning.
 - Verbose and detailed replies.
@@ -38,4 +30,4 @@ Training took about 2.5 hours using QLoRa on 1xA100, so this model can be recrea
 ### Future plans:
 - Native finetune.
 - Other model sizes.
-- Test some model merges using this model. (Specifically OpenOrca and Platypus models)

 The model is completely uncensored.
 ### Notable features:
 - Automatically triggered CoT reasoning.
 - Verbose and detailed replies.
 ### Future plans:
 - Native finetune.
 - Other model sizes.
+- Test some model merges using this model.