Instructions to use mediusware-ai/intellix with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries
PEFT
How to use mediusware-ai/intellix with PEFT:
```
Task type is invalid.
```

How to use mediusware-ai/intellix with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("mediusware-ai/intellix")
model = AutoModelForCausalLM.from_pretrained("mediusware-ai/intellix", 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 mediusware-ai/intellix with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/mediusware-ai/intellix

SGLang

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

Docker Model Runner
How to use mediusware-ai/intellix with Docker Model Runner:
```
docker model run hf.co/mediusware-ai/intellix
```

mediusware-ai commited on Apr 3

Commit

07f005d

verified ·

1 Parent(s): cd8bb19

Upload intellix-Q8_0.gguf with huggingface_hub

Browse files

Files changed (2) hide show

.gitattributes +1 -0
intellix-Q8_0.gguf +3 -0

.gitattributes CHANGED Viewed

@@ -36,3 +36,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 tokenizer.json filter=lfs diff=lfs merge=lfs -text
 mw-intellix-Q8_0.gguf filter=lfs diff=lfs merge=lfs -text
 logo.png filter=lfs diff=lfs merge=lfs -text

 tokenizer.json filter=lfs diff=lfs merge=lfs -text
 mw-intellix-Q8_0.gguf filter=lfs diff=lfs merge=lfs -text
 logo.png filter=lfs diff=lfs merge=lfs -text
+intellix-Q8_0.gguf filter=lfs diff=lfs merge=lfs -text

intellix-Q8_0.gguf ADDED Viewed

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:10087e3d3fe44b8341e2301472405559f606db3e336c59b546b0f05a4b400033
+size 1646572320