Create README.md

README.md

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+---
+datasets:
+- HuggingFaceH4/no_robots
+- yahma/alpaca-cleaned
+language:
+- en
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+- instruct
+- OpenELM
+---
+
+### [Model Information]
+
+- *This is a fine-tuned version of `Apple/OpenELM` model series; created in hopes of testing the limitations of `OpenELM` architecture. And maybe it had something to do with Apple's instruct models not providing the inst format.*
+
+- *This language model is trained on a total estimated sample size of `61k` lines of data without giving a system prompt.*
+
+### [Model Usage]
+
+- *Tokenizer is included in this repo, so you may use the model as any other model.*
+
+- *Model currently can handle up to `2048/T` maximum embeddings. This is the default limit imposed by `Apple` during the original training process.*
+
+- *In the training process of the language model, I didn't use any moderation filtration's; so this model might generate unwanted surprises.*
+
+- ***Please be aware that this model is trained on a tiny fraction of, what other models are trained on; as of `2024/5/12`, you may consider this model as a research-model `(This might change, if I feel like continuing improvement)`.***
+
+### [How to utilize the model to it's full capacity.]
+ 
+- *First you will need the basic dependencies that are required for operations, you may install it by running this command:*
+```python
+pip install -U transformers torch torchvision torchaudio accelerate
+```
+- *Secondly you may run this code, and remember to replace the `What can you do.` example question with your own.*
+```python
+from accelerate import Accelerator
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+accelerator = Accelerator()
+
+tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path="VINUK/OpenELM_Instruct_272M_V1.0", trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(pretrained_model_name_or_path="VINUK/OpenELM_Instruct_272M_V1.0", trust_remote_code=True, torch_dtype=torch.bfloat16)
+
+model = accelerator.prepare_model(model=model, evaluation_mode=True)
+
+
+
+with torch.no_grad():
+    inputs = tokenizer(text=f"[|=U=|]\nWhat can you do.\n[|=M=|]\n", return_tensors='pt').to(accelerator.device)
+    response = model.generate(inputs=inputs['input_ids'],
+                                      attention_mask=inputs['attention_mask'],
+                                      max_new_tokens=1024,
+                                      min_new_tokens=10,
+                                      do_sample=True,
+                                      top_p=0.95,
+                                      top_k=50,
+                                      temperature=0.6,
+                                      repetition_penalty=1.0,
+                                      use_cache=True,
+                                      pad_token_id=tokenizer.eos_token_id,
+                                      )
+    decoded = tokenizer.decode(response[:, inputs['input_ids'].shape[-1]:][0], skip_special_tokens=True)
+
+    print(decoded.replace('\\n', '\n'))
+```