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---
license: other
license_name: qwen-research
license_link: https://huggingface.co/Qwen/Qwen2.5-3B/blob/main/LICENSE
language:
- fr
- en
pipeline_tag: text-generation
tags:
- chat
- qwen
- qwen2.5
- finetune
- french
- english
library_name: transformers
inference: false
model_creator: MaziyarPanahi
quantized_by: MaziyarPanahi
base_model: Qwen/Qwen2.5-3B
model_name: calme-3.3-instruct-3b
datasets:
- MaziyarPanahi/french_instruct_sharegpt
- arcee-ai/EvolKit-20k
---
<img src="./calme_3.png" alt="Calme-3 Models" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
> [!TIP]
> This is avery small model, so it might not perform well for some prompts and may be sensitive to hyper parameters. I would appreciate any feedback to see if I can fix any issues in the next iteration. ❤️
>
# MaziyarPanahi/calme-3.3-instruct-3b
This model is an advanced iteration of the powerful `Qwen/Qwen2.5-3B`, specifically fine-tuned to enhance its capabilities in generic domains.
# ⚡ Quantized GGUF
All GGUF models are available here: [MaziyarPanahi/calme-3.3-instruct-3b-GGUF](https://huggingface.co/MaziyarPanahi/calme-3.3-instruct-3b-GGUF)
# 🏆 [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Leaderboard 2 coming soon!
# Prompt Template
This model uses `ChatML` prompt template:
```
<|im_start|>system
{System}
<|im_end|>
<|im_start|>user
{User}
<|im_end|>
<|im_start|>assistant
{Assistant}
````
# How to use
```python
# Use a pipeline as a high-level helper
from transformers import pipeline
messages = [
{"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="MaziyarPanahi/calme-3.3-instruct-3b")
pipe(messages)
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("MaziyarPanahi/calme-3.3-instruct-3b")
model = AutoModelForCausalLM.from_pretrained("MaziyarPanahi/calme-3.3-instruct-3b")
```
# Ethical Considerations
As with any large language model, users should be aware of potential biases and limitations. We recommend implementing appropriate safeguards and human oversight when deploying this model in production environments. |