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Model: ibm-ai-platform/micro-g3.3-8b-instruct-1b
Source: Original Platform

README.md

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+---
+license: apache-2.0
+base_model:
+- ibm-granite/granite-3.3-8b-instruct
+---
+
+# Micro-G3.3-8B-Instruct-1B
+
+**Model Summary:**
+Micro-G3.3-8B-Instruct-1B is a 1-billion parameter micro language model fine-tuned for reasoning and instruction-following capabilities. Built on top of Granite-3.3-8B-Instruct, with only 3 hidden layers, this model is trained to maximize performance and hardware compatibility at minimal compute cost.
+
+**Generation:** 
+This is a simple example of how to use Micro-G3.3-8B-Instruct-1B model.
+
+Install the following libraries:
+
+```shell
+pip install torch torchvision torchaudio
+pip install accelerate
+pip install transformers
+```
+Then, copy the snippet from the section that is relevant for your use case.
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer, set_seed
+import torch
+
+model_path="ibm-ai-platform/micro-g3.3-8b-instruct-1b"
+device="cuda"
+model = AutoModelForCausalLM.from_pretrained(
+        model_path,
+        device_map=device,
+        torch_dtype=torch.bfloat16,
+    )
+tokenizer = AutoTokenizer.from_pretrained(
+        model_path
+)
+
+conv = [{"role": "user", "content":"What is your favorite color?"}]
+
+input_ids = tokenizer.apply_chat_template(conv, return_tensors="pt", thinking=True, return_dict=True, add_generation_prompt=True).to(device)
+
+set_seed(42)
+output = model.generate(
+    **input_ids,
+    max_new_tokens=8,
+)
+
+prediction = tokenizer.decode(output[0, input_ids["input_ids"].shape[1]:], skip_special_tokens=True)
+print(prediction)
+```
+