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Model: eryk-mazus/polka-1.1b-chat
Source: Original Platform

README.md

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+---
+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.