Update README.md

README.md

@@ -1,200 +1,80 @@
----
-library_name: transformers
-tags:
-- unsloth
----
+---
+license: apache-2.0
+language:
+- en
+- ru
+base_model:
+- Qwen/Qwen2.5-14B-Instruct
+---
 
-# Model Card for Model ID
+## Description
 
-<!-- Provide a quick summary of what the model is/does. -->
+*Eule* is my attempt at reproducing OpenAI's o1 series of reasoning models. At the moment the point is not to hit good scores on benchmarks (this model is rather stupid),
+but to introduce an quantitative change in how the LLM approaches tasks. Similar to o1 models, Eule approaches its proplems in a step-by-step manner. 
+It is also trained to reason in Russian (ultimately, I want to make a decent russian reasoning model).
 
+Cool things I found while playing with it:
+1. It tries to verify its solutions to make sure they are correct.
+2. When failed, sometimes it tries to reiterate on the problem and try a new approach or fix the mistake.
 
+Bad things:
+1. It is stupid. Although it's interesting to inspect its chains of thought.
+2. The final response (after the reasoning chain) is in English.
+3. Sometimes the model may not produce <|REASONING_END|> which messes up parsing.
 
-## Model Details
-
-### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
-
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-
-## Uses
-
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
+Atm it is trained only on math data but it can solve riddles and other problems that require step-by-step reasoning. 
+I'm planning on adding more non-math data and then proceed to RL.
 
 ## Training Details
 
-### Training Data
-
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
+It was trained using [kolibrify](https://github.com/oKatanaaa/kolibrify) on a single H800 for about 6 hours.
+Training data consists of math problems with solutions formatted as deliberate reasoning chains. The longest reasoning chain is ~19000 tokens.
 
-### Training Procedure
 
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+The model follows ChatML template, but introduces several new tokens:
+- `<|REASONING_START|>` - start of a reasoning chain.
+- `<|REASONING_END|>` - end of a reasoning chain.
+- `<|RSS|>` - start of a reasoning step.
+- `<|RSE|>` - end of a reasoning step.
 
-#### Preprocessing [optional]
-
-[More Information Needed]
+A typical conversation formatting structure is as follows:
+```
+<|im_start|>system
+System message<|im_end|>
+<|im_start|>user
+Problem description<|im_end|>
+<|im_start|>assistant
+<|REASONING_START|><|RSS|>step 1<|RSE|><|RSS|>step 2<|RSE|><|REASONING_END|>Final assistant response<|im_end|>
+```
 
+## How to Get Started with the Model
 
-#### Training Hyperparameters
+I use [unsloth](https://github.com/unslothai/unsloth) and recommend you do the same:
+```python
+import transformers
 
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+model, tokenizer = FastLanguageModel.from_pretrained(
+    model_name='kaleinaNyan/eule-qwen2.5instruct-14b-111224'
+)
+FastLanguageModel.for_inference(model)
 
-#### Speeds, Sizes, Times [optional]
+def generate(chat, n_tokens, use_cache=True, do_sample=False):
+    input_str = tokenizer.apply_chat_template(chat, tokenize=False) + '<|im_start|>assistant\n<|REASONING_START|><|RSS|>'
+    inputs = tokenizer(input_str, return_tensors='pt')
+    outputs = model.generate(input_ids = inputs['input_ids'], max_new_tokens = n_tokens, use_cache = use_cache, do_sample=do_sample, temperature=0.7)
+    return tokenizer.batch_decode(outputs)[0]
 
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+msg = "Come up with a qubic equation and solve it"
+system_message = "You are an AI assistant that thoroughly solves any task. Explore various routes and verify your solutions. Reason in Russian. Provide concise responses to the user."
 
-[More Information Needed]
+chat = [
+    {'role': 'system', 'content': system_message},
+    {'role': 'user', 'content': msg},
+]
+response = generate(chat, 8196, do_sample=True)
+print('\n'.join(response.split('<|REASONING_START|>')[-1].split('<|RSS|>')))
+```
 
 ## Evaluation
 
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
-
-## Model Card Contact
-
-[More Information Needed]
+I'll provide it later for MATH and GSM8K benchmarks.