---
license: mit
datasets:
- >-
CreitinGameplays/Magpie-Reasoning-V2-250K-CoT-Deepseek-R1-Llama-70B-filtered-mistral
language:
- en
base_model:
- mistralai/Mistral-Nemo-Instruct-2407
pipeline_tag: text-generation
library_name: transformers
---
## Mistral Nemo 12B R1
Took **12 hours** to finetune on **1x Nvidia H100** with the following settings:
- Batch size: 26
- Gradient accumulation steps: 1
- Epochs: 1
- Learning rate: 2e-5
- Warmup ratio: 0.1
Run the model:
```python
import torch
from transformers import pipeline
model_id = "CreitinGameplays/Mistral-Nemo-12B-R1-v0.2"
pipe = pipeline(
"text-generation",
model=model_id,
torch_dtype=torch.bfloat16,
device_map="auto"
)
messages = [
{"role": "system", "content": "You are a helpful AI assistant named Mistral Nemo."},
{"role": "user", "content": "How many r's are in strawberry?"}
]
outputs = pipe(
messages,
temperature=0.6,
top_p=0.95,
top_k=40,
repetition_penalty=1.1,
max_new_tokens=2048
)
print(outputs[0]["generated_text"][-1])
```
### Example response generated with this model:
- User prompt:
```
how many r's are in strawberry?
```
- Model output:
```
To determine the number of 'r's in the word "strawberry," I will start by breaking down the word into its component parts: "straw" and "berry."
Next, I will count the number of 'r's in each part. The word "straw" contains one 'r,' and the word "berry" contains two 'r's.
Finally, I will add the number of 'r's from both parts together, which gives me a total of three 'r's in the word "strawberry."
To determine the number of 'r's in the word "strawberry," let's break it down step by step:
1. **Break down the word:**
The word "strawberry" can be divided into two parts: "straw" and "berry."
2. **Count the 'r's in each part:**
- In "straw": There is **1** 'r'.
- In "berry": There are **2** 'r's.
3. **Add the 'r's together:**
\( 1 \text{ (from straw)} + 2 \text{ (from berry)} = 3 \)
Therefore, the word "strawberry" contains **3** 'r's.
\[
\boxed{3}
\]
```
Recommended system prompt for activating reasoning step:
```
You are an AI focused on providing systematic, well-reasoned responses. Response Structure: - Format: {reasoning}{answer} - Reasoning: Minimum 6 logical steps only when it required in block - Process: Think first, then answer.
```
**Note**: The model was mainly finetuned on English dataset, meaning the model may not perform well in other languages.