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---
language:
- en
license: other
tags:
- AI
- ConversationalAI
pipeline_tag: conversational
inference: false
model-index:
- name: LLmRa-1.3B_V2
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: 30.46
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=L-R/LLmRa-1.3B_V2
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: 53.03
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=L-R/LLmRa-1.3B_V2
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: 26.06
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=L-R/LLmRa-1.3B_V2
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: 36.46
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=L-R/LLmRa-1.3B_V2
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: 59.27
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=L-R/LLmRa-1.3B_V2
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: 0.0
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=L-R/LLmRa-1.3B_V2
name: Open LLM Leaderboard
---
<h1 style="text-align: center">LLmRa-1.3B-V2</h1>
<h2 style="text-align: center">A conversational Open Pre-trained Transformer Language Model fine-tune.</h2>
**LLmRa 1.3B-V2**, as a proof-of-concept fine-tune of [facebook/opt-1.3b](https://huggingface.co/facebook/opt-1.3b) optimized for dialogue.
**Disclaimer:** NSFW data was included in the fine-tuning of this model. Although SFW inputs will usually result in SFW outputs, you are advised to **chat at your own risk. This model is not suitable for use by minors.**
**Warning:** This model is **NOT** suitable for use by minors. **It will output X-rated content under certain circumstances.**
**Model Fine-Tuned on LLmRa-100K conversational dataset - small version**
---
## Usage Format
To effectively utilize the model, follow this structured format for engaging text-based conversations:
**1. Initialization**
Here is how you can define the personality of the language model:
```
<|system|>[Persona]
```
- **Persona**: You can define a specific persona or context for the AI, but it's optional. It can be a character, a role, or just a style of interaction.
**2. AI Introduction**
```
<|user|>[User input]<|model|>
```
- Users can start the conversation by entering their message within `<|user|>` and closing with `<|model|>`.
---
### Example Usage:
Here's an example of how to start a conversation with the AI:
```
<|system|>I'm here to provide information and assistance on a wide range of topics.
<|model|>Hello! Welcome to our AI-powered assistant. How can I assist you today?
<|user|>Tell me about the history of artificial intelligence.
<|model|>
```
Continue the conversation as needed. This structured format helps maintain a smooth and engaging interaction with the AI.
You are not required to include `User`, you can change it to your prefered name or leave it blank You may also add the AI name, example:
```
<|user|>YourNameHere: Hello.<|model|>CharacterName:
```
You can also use this instruct prompt example:
```
<|system|>What is one plus one?<|model|>
```
## Loading The Model
To use the model and interact with it, use the Python code below:
```Python
from transformers import (AutoModelForCausalLM,
AutoTokenizer,
pipeline,
)
model = AutoModelForCausalLM.from_pretrained('L-R/LLmRa-1.3B-V2')
tokenizer = AutoTokenizer.from_pretrained('L-R/LLmRa-1.3B-V2')
pipe = pipeline(task="text-generation", model=model, tokenizer=tokenizer, max_length=100)
input_question = 'QUESTION HERE'
question_formatted = f'<|system|>{input_question}<|model|>'
result = pipe(question_formatted)
print(f"[model]: {result[0]['generated_text'][len(question_formatted):]}")
```
## Known issues
Model doesn't some of the times follow instructions.
# [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_L-R__LLmRa-1.3B_V2)
| Metric |Value|
|---------------------------------|----:|
|Avg. |34.21|
|AI2 Reasoning Challenge (25-Shot)|30.46|
|HellaSwag (10-Shot) |53.03|
|MMLU (5-Shot) |26.06|
|TruthfulQA (0-shot) |36.46|
|Winogrande (5-shot) |59.27|
|GSM8k (5-shot) | 0.00|
|