README.md

---
language:
- te
- en
license: llama2
datasets:
- Telugu-LLM-Labs/yahma_alpaca_cleaned_telugu_filtered_and_romanized
- Telugu-LLM-Labs/teknium_GPTeacher_general_instruct_telugu_filtered_and_romanized
model-index:
- name: Telugu-Llama2-7B-v0-Instruct
  results:
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: AI2 Reasoning Challenge (25-Shot)
      type: ai2_arc
      config: ARC-Challenge
      split: test
      args:
        num_few_shot: 25
    metrics:
    - type: acc_norm
      value: 53.58
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Telugu-LLM-Labs/Telugu-Llama2-7B-v0-Instruct
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: HellaSwag (10-Shot)
      type: hellaswag
      split: validation
      args:
        num_few_shot: 10
    metrics:
    - type: acc_norm
      value: 78.33
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Telugu-LLM-Labs/Telugu-Llama2-7B-v0-Instruct
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MMLU (5-Shot)
      type: cais/mmlu
      config: all
      split: test
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 47.63
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Telugu-LLM-Labs/Telugu-Llama2-7B-v0-Instruct
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: TruthfulQA (0-shot)
      type: truthful_qa
      config: multiple_choice
      split: validation
      args:
        num_few_shot: 0
    metrics:
    - type: mc2
      value: 43.26
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Telugu-LLM-Labs/Telugu-Llama2-7B-v0-Instruct
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: Winogrande (5-shot)
      type: winogrande
      config: winogrande_xl
      split: validation
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 73.95
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Telugu-LLM-Labs/Telugu-Llama2-7B-v0-Instruct
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: GSM8k (5-shot)
      type: gsm8k
      config: main
      split: test
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 20.39
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Telugu-LLM-Labs/Telugu-Llama2-7B-v0-Instruct
      name: Open LLM Leaderboard
---
# Telugu-Llama2-7B-v0-Instruct


This model is based on [Telugu-Llama2-7B-v0-Base](https://huggingface.co/Telugu-LLM-Labs/Telugu-Llama2-7B-v0-Base) and hase been finetuned on instruction datasets:
  1. [yahma_alpaca_cleaned_telugu_filtered_and_romanized](https://huggingface.co/datasets/Telugu-LLM-Labs/yahma_alpaca_cleaned_telugu_filtered_and_romanized)
  2. [teknium_GPTeacher_general_instruct_telugu_filtered_and_romanized](https://huggingface.co/datasets/Telugu-LLM-Labs/teknium_GPTeacher_general_instruct_telugu_filtered_and_romanized)

# Input Text Format

```
### Instruction: {instruction}

### Input: {input}

## Response: {response}
```

# Usage

## With Romanized Telugu

```python3
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

model_name = "Telugu-LLM-Labs/Telugu-Llama2-7B-v0-Instruct"

tokenizer = AutoTokenizer.from_pretrained(model_name, padding_side="right")
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16).to(device)

instruction = "Krindi samaacharam prakaram google app eppudu release ayyindi?"
input ="Google News is a news aggregator service developed by Google. It presents a continuous flow of links to articles organized from thousands of publishers and magazines. Google News is available as an app on Android, iOS, and the Web. Google released a beta version in September 2002 and the official app in January 2006."

text = f"""Instruction: {instruction} \nInput: {input} \nResponse:"""

encodings = tokenizer(text, padding=True, return_tensors="pt")
encodings = encodings.to(device)

with torch.inference_mode():
    outputs = model.generate(encodings.input_ids, do_sample=False, max_new_tokens=500)

output = tokenizer.batch_decode(outputs.detach(), skip_special_tokens=True)
```

### Sample Output:

```
1. September 2002 Google released a beta version of Google News.
2. January 2006 Google released the official version of Google News.
```

## With Native Telugu

```python3
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

model_name = "Telugu-LLM-Labs/Telugu-Llama2-7B-v0-Instruct"

tokenizer = AutoTokenizer.from_pretrained(model_name, padding_side="right")
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16).to(device)

instruction = "కింది వచనాన్ని సంగ్రహించండి"
input="గూగుల్ వార్తలు అనేది గూగుల్ ద్వారా అభివృద్ధి చేయబడిన వార్తా అగ్రిగేటర్ సేవ. ఇది వేలకొద్దీ ప్రచురణకర్తలు మరియు మ్యాగజైన్‌ల నుండి నిర్వహించబడిన కథనాలకు నిరంతర లింక్‌లను అందిస్తుంది. గూగుల్ వార్తలు Android, iOS మరియు వెబ్‌లో యాప్‌గా అందుబాటులో ఉన్నాయి. గూగుల్ సెప్టెంబరు 2002లో బీటా వెర్షన్‌ను మరియు జనవరి 2006లో అధికారిక యాప్‌ను విడుదల చేసింది."

text = f"""Instruction: {instruction} \nInput: {input} \nResponse:"""

encodings = tokenizer(text, padding=True, return_tensors="pt")
encodings = encodings.to(device)

with torch.inference_mode():
    outputs = model.generate(encodings.input_ids, do_sample=False, max_new_tokens=500)

output = tokenizer.batch_decode(outputs.detach(), skip_special_tokens=True)
```

### Sample Output:

1. గూగుల్ వార్తలు అనేది గూగుల్ ద్వారా అభివృద్ధి చేయబడిన వార్తా అగ్రిగేటర్ సేవ, వేలకొద్దీ ప్రచురణకర్తలు మరియు మ్యాగజైన్‌ల నుండి నిర్వహించబడిన కథనాలకు నిరంతర లింక్‌లను అందిస్తుంది.
2. గూగుల్ సెప్టెంబరు 2002లో బీటా వెర్షన్ మరియు జనవరి 2006లో అధికారిక యాప్ ను విడుదల చేసింది.

# Developers:

The model is a collaborative effort by [Ravi Theja](https://twitter.com/ravithejads) and [Ramsri Goutham](https://twitter.com/ramsri_goutham). Feel free to DM either of us if you have any questions.

# Note:

The model is quite sensitive to parameters and inputs and is not yet ready for production. It remains in the experimental phase, and we recommend using it accordingly.
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_Telugu-LLM-Labs__Telugu-Llama2-7B-v0-Instruct)

|             Metric              |Value|
|---------------------------------|----:|
|Avg.                             |52.86|
|AI2 Reasoning Challenge (25-Shot)|53.58|
|HellaSwag (10-Shot)              |78.33|
|MMLU (5-Shot)                    |47.63|
|TruthfulQA (0-shot)              |43.26|
|Winogrande (5-shot)              |73.95|
|GSM8k (5-shot)                   |20.39|