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)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Meforgers/Aixr", device_map="auto")

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

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

Meforgers commited on Jun 12, 2024

Commit

53fc189

verified ·

1 Parent(s): 599bd45

Update README.md

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README.md +23 -1

README.md CHANGED Viewed

@@ -4,4 +4,26 @@ language:
 - tr
 - en
 - es
----

 - tr
 - en
 - es
+---
+Can you use that LLM with that code;
+from unsloth import FastLanguageModel
+model, tokenizer = FastLanguageModel.from_pretrained(
+model_name = "Meforgers/Aixrav",
+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 a basic code?", # instruction
+"", # input
+"", # output - leave this blank for generation!
+)
+], return_tensors = "pt").to("cuda")
+outputs = model.generate(**inputs, max_new_tokens = 64, use_cache = True)
+tokenizer.batch_decode(outputs)