| library_name: transformers | |
| license: apache-2.0 | |
| tags: | |
| - jamba | |
| - mamba | |
| - moe | |
| ### Mini-Jamba | |
| [**Experimental Version**] We initialized the model according to [Jamba](https://huggingface.co/ai21labs/Jamba-v0.1), but with much smaller parameters. It was then trained using about 1B of python code, and has the simplest python code generation capabilities. | |
| ### Usage | |
| Here give some examples of how to use our model: | |
| ```python | |
| from transformers import AutoTokenizer, AutoModelForCausalLM | |
| import torch | |
| prompt = '''def min(arr): | |
| """ | |
| Returns the minimum value from the list `arr`. | |
| Parameters: | |
| - arr (list): A list of numerical values. | |
| Returns: | |
| - The minimum value in `arr`. | |
| """ | |
| ''' | |
| tokenizer = AutoTokenizer.from_pretrained( | |
| "TechxGenus/Mini-Jamba", | |
| trust_remote_code=True, | |
| ) | |
| tokenizer.pad_token = tokenizer.eos_token | |
| model = AutoModelForCausalLM.from_pretrained( | |
| "TechxGenus/Mini-Jamba", | |
| torch_dtype=torch.float16, | |
| device_map="auto", | |
| trust_remote_code=True, | |
| ) | |
| inputs = tokenizer.encode(prompt, return_tensors="pt") | |
| outputs = model.generate( | |
| input_ids=inputs.to(model.device), | |
| max_new_tokens=64, | |
| do_sample=False, | |
| ) | |
| print(tokenizer.decode(outputs[0], skip_special_tokens=True)) | |
| ``` | |
| ### Note | |
| Model may sometimes make errors, produce misleading contents, or struggle to manage tasks that are not related to coding. It has undergone very limited testing. Additional safety testing should be performed before any real-world deployments. | |