Using LoRA to finetune [bigsciene/bloom-1b7](https://huggingface.co/bigscience/bloom-1b7) model with [oasst1](https://huggingface.co/datasets/OpenAssistant/oasst1) data.

Sample code to run
```python
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Zayt/bloom-1b7-lora-merged-oasst")
model = AutoModelForCausalLM.from_pretrained("Zayt/bloom-1b7-lora-merged-oasst", device_map='auto', torch_dtype=torch.float16)

prompt_format = "### Input:\n{human}\n\n### Response:\n"
text = prompt_format.format(**{"human": "what is the weather today?"})
inputs = tokenizer(text, return_tensors='pt').to(model.device)
input_length = inputs.input_ids.shape[1]

with torch.no_grad():
    outputs = model.generate(
        **inputs, max_new_tokens=400, do_sample=True, temperature=0.5, top_k=50, return_dict_in_generate=True, no_repeat_ngram_size=5,
        pad_token_id=tokenizer.pad_token_id,
        bos_token_id=tokenizer.bos_token_id,
        eos_token_id=tokenizer.eos_token_id
    )
token = outputs.sequences[0, input_length:]
output_str = tokenizer.decode(token)

print(output_str)
```

{"license": "apache-2.0"}
transformers pytorch bloom text-generation license:apache-2.0 autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #bloom #text-generation #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

Using LoRA to finetune bigsciene/bloom-1b7 model with oasst1 data.

Sample code to run


TAGS
#transformers #pytorch #bloom #text-generation #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
