---
library_name: transformers
language:
- en
license: apache-2.0
base_model: BEE-spoke-data/tFINE-900m-e16-d32-flan
tags:
- instruct
- code
datasets:
- pszemraj/infinity-instruct-7m-T2T_en
pipeline_tag: text2text-generation
---


# tFINE-900m-e16-d32-instruct


## Model description

This model is a fine-tuned version of [BEE-spoke-data/tFINE-900m-e16-d32-flan](https://hf.co/BEE-spoke-data/tFINE-900m-e16-d32-flan) on the pszemraj/infinity-instruct-7m-T2T_en dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3588
- Num Input Tokens Seen: 810173896


## Usage Example

> [!TIP]
> You can also run inference with [turboT5](https://github.com/Knowledgator/TurboT5) on ampere+ GPUs for better performance. See [example on Colab](https://colab.research.google.com/gist/pszemraj/59cf088d603a0f62bbb5cedc42a7837b/turbot5-inference.ipynb).


```py
from transformers import pipeline

pipe = pipeline(
  "text2text-generation",
  model="BEE-spoke-data/tFINE-900m-e16-d32-instruct",
  # device_map="auto", # uncomment if have GPU/accelerate
)
prompt = "Write me a python script that demonstrates an advanced sorting algorithm"
res = pipe(
    prompt,
    max_new_tokens=384,
    num_beams=4,
    early_stopping=True,
    no_repeat_ngram_size=6,
)
print(res[0]["generated_text"])
```

## evals

### open-llm-leaderboard 2

| Model                                                                                                                                 | Average ⬆️ | IFEval | BBH  | MATH Lvl 5 | GPQA | MUSR | MMLU-PRO |
|---------------------------------------------------------------------------------------------------------------------------------------|------------|--------|------|------------|------|------|----------|
| 🔶 BEE-spoke-data/tFINE-900m-e16-d32-instruct                                                      | 5.82       | 13.21  | 4.74 | 0          | 0.56 | 13.81| 2.63     |
| 🔶 BEE-spoke-data/tFINE-900m-e16-d32-flan                                                                                     | 4.43       | 15.06  | 4.41 | 0          | 0    | 3.72 | 3.41     |