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---
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:
Training charts:
Link to charts:
https://api.wandb.ai/links/abacaj1/c4nkcs9r
Code to train model:
https://github.com/abacaj/train-with-fsdp |