File size: 1,407 Bytes
dfd565c 955dece e675cff dfd565c 955dece ff90418 955dece 5a4f94f 955dece |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 |
---
license: apache-2.0
pipeline_tag: text-generation
tags:
- finetuned
inference:
parameters:
temperature: 0.01
---
A Mistral7B Instruct (https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1)
Finetune using QLoRA on the docs available in https://docs.modular.com/mojo/
The Mistral-7B-Instruct-v0.1 Large Language Model (LLM) is a instruct fine-tuned version of the [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) generative text model using a variety of publicly available conversation datasets.
For full details of this model please read our [paper](https://arxiv.org/abs/2310.06825) and [release blog post](https://mistral.ai/news/announcing-mistral-7b/).
## Instruction format
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained("mcysqrd/MODULARMOJO_Mistral-V1")
tokenizer = AutoTokenizer.from_pretrained("mcysqrd/MODULARMOJO_Mistral-V1")
message = "What can you tell me about MODULAR_MOJO mojo_roadmap Scoping and mutability of statement variables?"
encodeds = tokenizer.apply_chat_template(message, return_tensors="pt")
model_inputs = encodeds.to(device)
model.to(device)
generated_ids = model.generate(model_inputs, max_new_tokens=1650, do_sample=True, temperature = 0.01)
decoded = tokenizer.batch_decode(generated_ids)
print(decoded[0])
``` |