README.md

---
tags:
- generated_from_trainer
- conversational
- polish
license: mit
language:
- pl
datasets:
- eryk-mazus/polka-dpo-v1
pipeline_tag: text-generation
inference: false
---

![image/png](https://cdn-uploads.huggingface.co/production/uploads/61bf0e11c88f3fd22f654059/FiMCITBAaEyMyxCHhfWVD.png)

# Polka-1.1B-Chat

`eryk-mazus/polka-1.1b-chat` **is the first polish model trained to act as a helpful, conversational assistant that can be run locally.**

The model is based on [TinyLlama-1.1B](https://huggingface.co/TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T) with the custom, extended tokenizer for more efficient Polish text generation, that was additionally pretrained on 5.7 billion tokens. **It was then fine-tuned on around 60k synthetically generated and machine-translated multi-turn conversations with the [Direct Preference Optimization (DPO)](https://arxiv.org/abs/2305.18290) performed on top of it.**

Context size: 4,096 tokens

In addition, we're releasing:
* [polka-1.1b](https://huggingface.co/eryk-mazus/polka-1.1b) - our base model with an extended tokenizer and additional pre-training on Polish corpus sampled using [DSIR](https://github.com/p-lambda/dsir)
* [polka-pretrain-en-pl-v1](https://huggingface.co/datasets/eryk-mazus/polka-pretrain-en-pl-v1) - the pre-training dataset
* [polka-dpo-v1](https://huggingface.co/datasets/eryk-mazus/polka-dpo-v1) - dataset of DPO pairs
* [polka-1.1b-chat-gguf](https://huggingface.co/eryk-mazus/polka-1.1b-chat-gguf) - GGUF files for the chat model 

## Usage

Sample code:

```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, TextStreamer

model_name = "eryk-mazus/polka-1.1b-chat"

tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True)
tokenizer.pad_token = tokenizer.eos_token

model = AutoModelForCausalLM.from_pretrained(
    model_name, 
    torch_dtype=torch.bfloat16 if torch.cuda.is_bf16_supported() else torch.float16,
    device_map="auto"
)
streamer = TextStreamer(tokenizer, skip_prompt=True)

# You are a helpful assistant.
system_prompt = "Jesteś pomocnym asystentem."
chat = [{"role": "system", "content": system_prompt}]

# Compose a short song on programming.
user_input = "Napisz krótką piosenkę o programowaniu."
chat.append({"role": "user", "content": user_input})

# Generate - add_generation_prompt to make sure it continues as assistant
inputs = tokenizer.apply_chat_template(chat, add_generation_prompt=True, return_tensors="pt")
# For multi-GPU, find the device of the first parameter of the model
first_param_device = next(model.parameters()).device
inputs = inputs.to(first_param_device)

with torch.no_grad():
    outputs = model.generate(
        inputs,
        pad_token_id=tokenizer.eos_token_id,
        max_new_tokens=512,
        temperature=0.2,
        repetition_penalty=1.15,
        top_p=0.95,
        do_sample=True,
        streamer=streamer,
    )

# Add just the new tokens to our chat
new_tokens = outputs[0, inputs.size(1):]
response = tokenizer.decode(new_tokens, skip_special_tokens=True)
chat.append({"role": "assistant", "content": response})
```

The model works seamlessly with [vLLM](https://github.com/vllm-project/vllm) as well.

## Prompt format

This model uses ChatML as the prompt format:
```
<|im_start|>system
Jesteś pomocnym asystentem.
<|im_start|>user
Jakie jest dzienne zapotrzebowanie kaloryczne dorosłej osoby?<|im_end|>
<|im_start|>assistant
Dla dorosłych osób zaleca się spożywanie około 2000-3000 kcal dziennie, aby utrzymać optymalne zdrowie i dobre samopoczucie.<|im_end|>
```

This prompt is available as a [chat template](https://huggingface.co/docs/transformers/chat_templating), which means you can format messages using the `tokenizer.apply_chat_template()` method, as demonstrated in the example above.