Instructions to use ghost-x/ghost-8b-beta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ghost-x/ghost-8b-beta with Transformers:

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

pipe = pipeline("text-generation", model="ghost-x/ghost-8b-beta")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ghost-x/ghost-8b-beta")
model = AutoModelForCausalLM.from_pretrained("ghost-x/ghost-8b-beta")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Local Apps Settings

vLLM

How to use ghost-x/ghost-8b-beta with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ghost-x/ghost-8b-beta"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ghost-x/ghost-8b-beta",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ghost-x/ghost-8b-beta

SGLang

How to use ghost-x/ghost-8b-beta 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 "ghost-x/ghost-8b-beta" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ghost-x/ghost-8b-beta",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "ghost-x/ghost-8b-beta" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ghost-x/ghost-8b-beta",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use ghost-x/ghost-8b-beta with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for ghost-x/ghost-8b-beta to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for ghost-x/ghost-8b-beta to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for ghost-x/ghost-8b-beta to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="ghost-x/ghost-8b-beta",
    max_seq_length=2048,
)

Docker Model Runner
How to use ghost-x/ghost-8b-beta with Docker Model Runner:
```
docker model run hf.co/ghost-x/ghost-8b-beta
```

My "silly" experiment.

by ZeroWw - opened Jul 27, 2024

Discussion

ZeroWw

Jul 27, 2024

ZeroWw 'SILLY' version.
The original model has been quantized (fq8 version)
and a percentage of it's tensors have
been modified adding some noise.

Full colab: https://colab.research.google.com/drive/1a7seagBzu5l3k3FL4SFk0YJocl7nsDJw?usp=sharing

Fast colab: https://colab.research.google.com/drive/1SDD7ox21di_82Y9v68AUoy0PhkxwBVvN?usp=sharing

Original reddit post: https://www.reddit.com/r/LocalLLaMA/comments/1ec0s8p/i_made_a_silly_test/

I created a program to randomize the weights of a model. The program has 2 parameters: the percentage of weights to modify and the percentage of the original value to randmly apply to each weight.

At the end I check the resulting GGUF file for binary differences.
In this example I set to modify 100% of the weights of Mistral 7b Instruct v0.3 by a maximum of 15% deviation.

Since the deviation is calculated on the F32 weights, when quantized to Q8_0 this changes.
So, in the end I got a file that compared to the original has:

Bytes Difference percentage: 73.04%

Average value divergence: 2.98%

The cool thing is that chatting with the model I see no apparent difference and the model still works nicely as the original.

Since I am running everything on CPU, I could not run perplexity scores or anything computing intensive.

As a small test, I asked the model a few questions (like the history of the roman empire) and then fact check its answer using a big model. No errors were detected.

Update: all procedure tested and created on COLAB.

Result (changes every time): https://huggingface.co/ZeroWw/ghost-8b-beta-SILLY

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