API reference

import Feedback from "/snippets/page-feedback.mdx";

The GenieX Python SDK follows the same design patterns as Hugging Face transformers — use AutoModel*.from_pretrained() to load a model, then call .generate() for inference.

AutoModelForCausalLM

Factory for loading causal language models — both text-only and multimodal. Returns a GenieXLLM for text-only models, or a GenieXVLM when a multimodal model is detected (e.g. phi4_multimodal, qwen3.5-vl, gemma4).

from geniex import AutoModelForCausalLM

model = AutoModelForCausalLM.from_pretrained(
    "ai-hub-models/Qwen3-4B-Instruct",
    device_map="auto",
)

from_pretrained()

Returns: GenieXLLM or GenieXVLM (auto-detected based on the model)

AutoModelForVision2Seq

Factory for loading vision-language / multimodal models. Returns a GenieXVLM instance.

from geniex import AutoModelForVision2Seq

model = AutoModelForVision2Seq.from_pretrained("ai-hub-models/Qwen2.5-7B-Instruct", device_map="qairt")

from_pretrained()

Accepts all the same parameters as AutoModelForCausalLM.from_pretrained() plus:

Returns: GenieXVLM

GenieXLLM

Text-only language model instance returned by AutoModelForCausalLM.from_pretrained().

generate()

Run text generation from a formatted prompt string.

output = model.generate(prompt, max_new_tokens=256)
print(output.text)

Returns: GenerateOutput (or TextIteratorStreamer when stream=True)

reset()

Resets conversation state and clears the KV cache.

save_kv_cache(path) / load_kv_cache(path)

Save or load the key-value cache to/from a file path (str).

close()

Releases the model handle and frees resources. Also supports context-manager usage:

with AutoModelForCausalLM.from_pretrained("qwen3") as model:
    output = model.generate(prompt)

GenieXVLM

Vision-language model instance returned by AutoModelForVision2Seq.from_pretrained().

generate()

Same parameters as GenieXLLM.generate() plus:

output = model.generate(prompt, images=["/path/to/image.jpg"], max_new_tokens=256)
print(output.text)

Returns: GenerateOutput (or TextIteratorStreamer when stream=True)

reset() / close()

Same as GenieXLLM.

ModelTokenizer

Accessed via model.tokenizer. Provides a transformers-compatible chat template interface.

apply_chat_template()

Formats a list of chat messages using the model's built-in chat template.

messages = [{"role": "user", "content": "What is 2+2?"}]
prompt = model.tokenizer.apply_chat_template(
    messages, tokenize=False, add_generation_prompt=True,
)

Returns: str — formatted prompt ready for model.generate().

Output classes

GenerateOutput

Returned by model.generate().

ProfileData

TextIteratorStreamer

Returned by model.generate(..., stream=True). Yields decoded text chunks as they are generated.

streamer = model.generate(prompt, max_new_tokens=256, stream=True)
for chunk in streamer:
    print(chunk, end="", flush=True)

final = streamer.output  # GenerateOutput available after iteration

Model manager

The same model manager the CLI uses is available programmatically via geniex.model_manager.

from geniex import model_manager as mm

MODEL = "Qwen/Qwen3-0.6B-GGUF"
mm.pull(MODEL)
print(f"pull complete: {MODEL}")
paths = mm.get_paths(MODEL)
print(f"model path:    {paths}")
local_models = mm.list_models()
print(f"local models:  {local_models}")
mm.remove(MODEL)
print(f"model removed: {MODEL}")

SDK functions