README.md

---
datasets:
- theblackcat102/evol-codealpaca-v1
model-index:
- name: abacaj/starcoderbase-1b-sft
 results:
 - task:
 type: text-generation
 dataset:
 type: openai_humaneval
 name: HumanEval
 metrics:
 - name: pass@1
 type: pass@1
 value: 39
 verified: false
 - task:
 type: text-generation
 dataset:
 type: mbpp
 name: MBPP
 metrics:
 - name: pass@1
 type: pass@1
 value: 31.74
 verified: false
language:
- en
---

Dataset credits go to: [theblackcat102](https://huggingface.co/theblackcat102)

How to run inference:
```python
import transformers
import torch


def fmt_prompt(prompt: str) -> str:
 return f"""[Instructions]:\n{prompt}\n\n[Response]:"""


if __name__ == "__main__":
 model_name = "abacaj/starcoderbase-1b-sft"
 tokenizer = transformers.AutoTokenizer.from_pretrained(model_name)

 model = (
 transformers.AutoModelForCausalLM.from_pretrained(
 model_name,
 )
 .to("cuda:0")
 .eval()
 )

 prompt = "Write a python function to sort the following array in ascending order, don't use any built in sorting methods: [9,2,8,1,5]"
 prompt_input = fmt_prompt(prompt)
 inputs = tokenizer(prompt_input, return_tensors="pt").to(model.device)
 input_ids_cutoff = inputs.input_ids.size(dim=1)

 with torch.no_grad():
 generated_ids = model.generate(
 **inputs,
 use_cache=True,
 max_new_tokens=512,
 temperature=0.2,
 top_p=0.95,
 do_sample=True,
 eos_token_id=tokenizer.eos_token_id,
 pad_token_id=tokenizer.pad_token_id,
 )

 completion = tokenizer.decode(
 generated_ids[0][input_ids_cutoff:],
 skip_special_tokens=True,
 )

 print(completion)
```

Evals:
![image/png](https://cdn-uploads.huggingface.co/production/uploads/62ceeb27e7f6014c0e9d9268/U7L1aOV7UxBEBcLGqOZ2s.png)

Training charts:
![image/png](https://cdn-uploads.huggingface.co/production/uploads/62ceeb27e7f6014c0e9d9268/PLkFqE7_34-hJmFW7_opG.png)

Link to charts:
https://api.wandb.ai/links/abacaj1/c4nkcs9r

Code to train model:
https://github.com/abacaj/train-with-fsdp