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README.md

@@ -1,26 +1,35 @@
 ---
+library_name: transformers
+license: apache-2.0
+license_link: https://ai.google.dev/gemma/docs/gemma_4_license
+pipeline_tag: image-text-to-text
 tags:
-- ml-intern
+- mlx
+base_model: google/gemma-4-31B-it
 ---
 
-# tritesh/gemma-4-31B-it-mlx-2Bit
+# raazkumar/gemma-4-31B-it-mlx-2Bit
 
-<!-- ml-intern-provenance -->
-## Generated by ML Intern
+The Model [raazkumar/gemma-4-31B-it-mlx-2Bit](https://huggingface.co/raazkumar/gemma-4-31B-it-mlx-2Bit) was converted to MLX format from [google/gemma-4-31B-it](https://huggingface.co/google/gemma-4-31B-it) using mlx-lm version **0.31.2**.
 
-This model repository was generated by [ML Intern](https://github.com/huggingface/ml-intern), an agent for machine learning research and development on the Hugging Face Hub.
+## Use with mlx
 
-- Try ML Intern: https://smolagents-ml-intern.hf.space
-- Source code: https://github.com/huggingface/ml-intern
-
-## Usage
+```bash
+pip install mlx-lm
+```
 
 ```python
-from transformers import AutoModelForCausalLM, AutoTokenizer
+from mlx_lm import load, generate
 
-model_id = 'tritesh/gemma-4-31B-it-mlx-2Bit'
-tokenizer = AutoTokenizer.from_pretrained(model_id)
-model = AutoModelForCausalLM.from_pretrained(model_id)
-```
+model, tokenizer = load("raazkumar/gemma-4-31B-it-mlx-2Bit")
 
-For non-causal architectures, replace `AutoModelForCausalLM` with the appropriate `AutoModel` class.
+prompt="hello"
+
+if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None:
+    messages = [{"role": "user", "content": prompt}]
+    prompt = tokenizer.apply_chat_template(
+        messages, tokenize=False, add_generation_prompt=True
+    )
+
+response = generate(model, tokenizer, prompt=prompt, verbose=True)
+```