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

Libraries

How to use Meforgers/Aixr with Transformers:

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

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

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Meforgers/Aixr", dtype="auto")

PEFT
How to use Meforgers/Aixr with PEFT:
```
Task type is invalid.
```
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Meforgers/Aixr with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Meforgers/Aixr

SGLang

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

Unsloth Studio new

How to use Meforgers/Aixr 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 Meforgers/Aixr 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 Meforgers/Aixr to start chatting

Using HuggingFace Spaces for Unsloth

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

Load model with FastModel

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

Docker Model Runner
How to use Meforgers/Aixr with Docker Model Runner:
```
docker model run hf.co/Meforgers/Aixr
```

Model Trained By Meforgers

This model, named 'Aixr,' is designed for science and artificial intelligence development. You can use it as the foundation for many of your scientific projects and interesting ideas. In short, Aixr is an artificial intelligence model that is based on futurism and innovation.

Firstly

-If you intend to use unsloth with Pytorch 1.3.0: Utilize the "ampere" path for newer RTX 30xx GPUs or higher.

    pip install "unsloth[cu118-torch230] @ git+https://github.com/unslothai/unsloth.git"

    pip install "unsloth[cu121-torch230] @ git+https://github.com/unslothai/unsloth.git"

    pip install "unsloth[cu118-ampere-torch230] @ git+https://github.com/unslothai/unsloth.git"

    pip install "unsloth[cu121-ampere-torch230] @ git+https://github.com/unslothai/unsloth.git"

-Also you can use another system

Usage

from unsloth import FastLanguageModel
import torch

# Variable side
max_seq_length = 512 
dtype = torch.float16 
load_in_4bit = True

# Alpaca prompt
alpaca_prompt = """### Instruction:
{0}

### Input:
{1}

### Response:
{2}
"""

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name="Meforgers/Aixr",
    max_seq_length=max_seq_length,
    dtype=dtype,
    load_in_4bit=load_in_4bit,
)

FastLanguageModel.for_inference(model)

inputs = tokenizer(
    [
        alpaca_prompt.format(
            "Can u text me basic python code?",  # instruction side (You need to change that side)
            "",  # input
            "",  # output - leave this blank for generation!
        )
    ],
    return_tensors="pt"
).to("cuda")

outputs = model.generate(**inputs, max_new_tokens=128, use_cache=True)
print(tokenizer.batch_decode(outputs))

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