Instructions to use LeroyDyer/Mixtral_AI_Cyber_4.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LeroyDyer/Mixtral_AI_Cyber_4.0 with Transformers:

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

pipe = pipeline("text-generation", model="LeroyDyer/Mixtral_AI_Cyber_4.0")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("LeroyDyer/Mixtral_AI_Cyber_4.0")
model = AutoModelForCausalLM.from_pretrained("LeroyDyer/Mixtral_AI_Cyber_4.0", device_map="auto")
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]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use LeroyDyer/Mixtral_AI_Cyber_4.0 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LeroyDyer/Mixtral_AI_Cyber_4.0"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LeroyDyer/Mixtral_AI_Cyber_4.0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/LeroyDyer/Mixtral_AI_Cyber_4.0

SGLang

How to use LeroyDyer/Mixtral_AI_Cyber_4.0 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 "LeroyDyer/Mixtral_AI_Cyber_4.0" \
    --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": "LeroyDyer/Mixtral_AI_Cyber_4.0",
		"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 "LeroyDyer/Mixtral_AI_Cyber_4.0" \
        --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": "LeroyDyer/Mixtral_AI_Cyber_4.0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use LeroyDyer/Mixtral_AI_Cyber_4.0 with Docker Model Runner:
```
docker model run hf.co/LeroyDyer/Mixtral_AI_Cyber_4.0
```

Alternative quantizations.

by ZeroWw - opened Jul 15, 2024

Discussion

ZeroWw

Jul 15, 2024

•

edited Jul 23, 2024

These are my own quantizations (updated almost daily).

The difference with normal quantizations is that I quantize the output and embed tensors to f16.
and the other tensors to 15_k,q6_k or q8_0.
This creates models that are little or not degraded at all and have a smaller size.
They run at about 3-6 t/sec on CPU only using llama.cpp
And obviously faster on computers with potent GPUs

ZeroWw/Mixtral_AI_Cyber_4.0-GGUF

LeroyDyer

Owner Jul 16, 2024

I think that the degradation is minor , when I train I try to train quantized .. so I expect the model to be in perfect precision in double quantized ..
I had read that the models were basically best at fp16 and personally considered this the base model ..

When I got the unsloth set up . I noticed they was using the 4-bit mistral ? So if they was using this why not use the 4 bit models as a base also !

It's a pity they could not make ggnls from 4-bit . The unsloth does not even want to give you a 4 bit model you have to specify forced !

But since I have been using 4 bit o have not gone back . I maintain a fp16 onsite , but the conversion between types only the switch down sometime has errors .. but I think there is a order to things .
So a finetuned model has it's deltas (extra) they are not entirely merged into the model as it still recognises these tensors . When compressing if a model is highly tuned then some of these tensors can give errors.
If the model has been merged with another model that product is good to reduce as a 4bit as the mathematics solved these potential future reduction errors ..
It's becomes a process of elimination when testing and merging etc . To discover where those tensor errors came from .. or quantisation errors which seems it overcame or not ? ..
So if the model does queantize to 4 bit with no errors then there is zero loss .
And zero degradation .
Hence not using an interface which hides these important verbose logging .
Maybe ? As that is my thoughta and testing on this so far .. and we are all experimenting and sharing what we learn as we go..
So it's not gospel !

LeroyDyer

Owner Jul 16, 2024

Thanks , I use the q4km or s ..
It good to know that we can go much lower ! As this is the aim ... But also we need to be able to reexpand the models whan we need them. .
I consider gfml as a zip methodology

LeroyDyer

Owner Jul 17, 2024

also i have some upcommming projects which require a remote - code to run ? how do you quantize these custom models ?

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