Instructions to use LeroyDyer/SpydazWeb_AI_CyberTron_Ultra_7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LeroyDyer/SpydazWeb_AI_CyberTron_Ultra_7b with Transformers:

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

pipe = pipeline("text-generation", model="LeroyDyer/SpydazWeb_AI_CyberTron_Ultra_7b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("LeroyDyer/SpydazWeb_AI_CyberTron_Ultra_7b")
model = AutoModelForCausalLM.from_pretrained("LeroyDyer/SpydazWeb_AI_CyberTron_Ultra_7b")
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

vLLM

How to use LeroyDyer/SpydazWeb_AI_CyberTron_Ultra_7b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LeroyDyer/SpydazWeb_AI_CyberTron_Ultra_7b"
# 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/SpydazWeb_AI_CyberTron_Ultra_7b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/LeroyDyer/SpydazWeb_AI_CyberTron_Ultra_7b

SGLang

How to use LeroyDyer/SpydazWeb_AI_CyberTron_Ultra_7b 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/SpydazWeb_AI_CyberTron_Ultra_7b" \
    --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/SpydazWeb_AI_CyberTron_Ultra_7b",
		"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/SpydazWeb_AI_CyberTron_Ultra_7b" \
        --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/SpydazWeb_AI_CyberTron_Ultra_7b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio new

How to use LeroyDyer/SpydazWeb_AI_CyberTron_Ultra_7b 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 LeroyDyer/SpydazWeb_AI_CyberTron_Ultra_7b 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 LeroyDyer/SpydazWeb_AI_CyberTron_Ultra_7b to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for LeroyDyer/SpydazWeb_AI_CyberTron_Ultra_7b to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="LeroyDyer/SpydazWeb_AI_CyberTron_Ultra_7b",
    max_seq_length=2048,
)

Docker Model Runner
How to use LeroyDyer/SpydazWeb_AI_CyberTron_Ultra_7b with Docker Model Runner:
```
docker model run hf.co/LeroyDyer/SpydazWeb_AI_CyberTron_Ultra_7b
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Uploaded model

Developed by: LeroyDyer
License: apache-2.0
Finetuned from model : LeroyDyer/Mixtral_AI_CyberTron_Ultra

[ https://github.com/spydaz

The Mistral-7B-Instruct-v0.2 Large Language Model (LLM) is an instruct fine-tuned version of the Mistral-7B-v0.2.
Mistral-7B-v0.2 has the following changes compared to Mistral-7B-v0.1
- 32k context window (vs 8k context in v0.1)
- Rope-theta = 1e6
- No Sliding-Window Attention

What does he NOT KNOW ! that is the question!

MOTTO FOR MODEL!

Models are the same as loras , take them with light weight they are like tablets of knowledge!

Exactly ! ( models / loras ? is there a difference ? only mega merges make a true difference ! the small merges are just applying an adapter lol - Its in there somewhere?)

Ok Its a Great MODEL ! (My Favorite Goto Brain now ! - will be fine tuned even more ! (if i get cloud credits))

Highly Math Trained As well as many TextBooks and Lessons Highly fit datasets as well as Coding Datasets highly tuned!

This model has absorbed all its previous generations as well as ALL high performers and Specialist models (mistral) It has absorb many foriegn languge models and still stays as an english model !

Very impressive responses Short and long as also it was trained on some binary datasets to return a direct answer! and others to perform step by step response as wel as other to perform interactive response with clients for vairous tasks, such as product design and system design discussion:

Finacial information and other finacial tasks have been highly tunes also : Infact when returning to previous aligned datasets they stayed in line and was sdtill able to achieve High tuning! Hence a process of merging with a specific topic or role and then training for the role and topic on themed data, hence previous itterations heavily tuned for medical or law or role play as the conception was that intergating the model into a single enity may even corrput them , so the decision to seperate concerns was taken : This enabled for ssstrategic merging and tuning !

Concepts : chain of thought and functin calling Self rag ! Thoughts , emotive responses have been enhance where possibel with the data given . even sexy books have been highly tuned into the model : but also i think american genera books (sci fi, fantasy, romance novels are required) for great role play which some expect: ) I have recently seen a strategy in which prompts can be embedded into the adapter to Trigger Specific Roles : I hae tried to remove such prompting as you are a helpful ai to a character theme instead such as you are a cyber hacker by day and business man by night ! ie to give the model various internal personas ! after some training i noticed it was also talking to itself !! (rehersing) but the tokens for thought were missing so it lookeed strange until i noticed the bug; After removing the thought tokens they were displayed in the output as the tokenizer was masking them !

But Still a Great Model , Given a Task based data set it Coverges Super quickly hence my enjoyment of the model as training of it is super quick ! Now when ii load up datasets : they are generally only a few bad steps before it begins to drop below zero maintaining a steady 0.6 etc whilst loading the unnseen new dataset , hence not needing so many epochs to adjust the matrix to the new information !

Im not sure if Lora actually works when you save them but i do save some and use them to load models for training ! as they are jump starts for model which did not recive that fine tuning , they can be merged and alligned ! (probably thiey are Good! )

This mistral model was trained 2x faster with Unsloth and Huggingface's TRL library.

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	13.57
IFEval (0-Shot)	15.56
BBH (3-Shot)	27.75
MATH Lvl 5 (4-Shot)	1.36
GPQA (0-shot)	5.70
MuSR (0-shot)	10.30
MMLU-PRO (5-shot)	20.73

Downloads last month: 15

Safetensors

Model size

7B params

Tensor type

F16

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Datasets used to train LeroyDyer/SpydazWeb_AI_CyberTron_Ultra_7b

Spaces using LeroyDyer/SpydazWeb_AI_CyberTron_Ultra_7b 9

Collections including LeroyDyer/SpydazWeb_AI_CyberTron_Ultra_7b

Evaluation results

strict accuracy on IFEval (0-Shot)
Open LLM Leaderboard

15.560
normalized accuracy on BBH (3-Shot)
Open LLM Leaderboard

27.750
exact match on MATH Lvl 5 (4-Shot)
Open LLM Leaderboard

1.360
acc_norm on GPQA (0-shot)
Open LLM Leaderboard

5.700
acc_norm on MuSR (0-shot)
Open LLM Leaderboard

10.300
accuracy on MMLU-PRO (5-shot)
test set Open LLM Leaderboard

20.730