---
library_name: transformers
base_model: HuggingFaceH4/zephyr-7b-beta
license: mit
language:
- ru
- en
tags:
- python
- code
pipeline_tag: conversational
model-index:
- name: zephyr-python-ru-merged
  results:
  - task:
      type: text-generation
    dataset:
      name: gsm8k
      type: gsm8k
    metrics:
    - name: Grade School Math 8K (5-Shot)
      type: Grade School Math 8K (5-Shot)
      value: 32.52
    source:
      name: Open LLM Leaderboard
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
  - task:
      type: text-generation
    dataset:
      name: ai2_arc
      type: ai2_arc
    metrics:
    - name: AI2 Reasoning Challenge (25-Shot)
      type: AI2 Reasoning Challenge (25-Shot)
      value: 56.06
    source:
      name: Open LLM Leaderboard
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
  - task:
      type: text-generation
    dataset:
      name: HellaSwag
      type: HellaSwag
    metrics:
    - name: HellaSwag (10-Shot)
      type: HellaSwag (10-Shot)
      value: 82.06
    source:
      name: Open LLM Leaderboard
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
  - task:
      type: text-generation
    dataset:
      name: MMLU
      type: MMLU
    metrics:
    - name: MMLU (5-Shot)
      type: MMLU (5-Shot)
      value: 60.02
    source:
      name: Open LLM Leaderboard
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
  - task:
      type: text-generation
    dataset:
      name: truthfulqa
      type: truthfulqa
    metrics:
    - name: truthfulqa:mc (0-Shot)
      type: truthfulqa:mc (0-Shot)
      value: 52.81
    source:
      name: Open LLM Leaderboard
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
  - task:
      type: text-generation
    dataset:
      name: winogrande
      type: winogrande
    metrics:
    - name: winogrande (5-Shot)
      type: winogrande (5-Shot)
      value: 76.95
    source:
      name: Open LLM Leaderboard
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
datasets:
- MexIvanov/Vezora-Tested-22k-Python-Alpaca-ru
- MexIvanov/CodeExercise-Python-27k-ru
- zelkame/ru-stackoverflow-py
---

# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->



## Model Details

### Model Description

<!-- Provide a longer summary of what this model is. -->



- **Developed by:** C.B. Pronin, A.V. Volosova, A.V. Ostroukh, Yu.N. Strogov, V.V. Kurbatov, A.S. Umarova.
- **Model type:** Base model HuggingFaceH4/zephyr-7b-beta merged with LoRA (Peft) adapter model MexIvanov/zephyr-python-ru trained on a mix of publicly available data and machine-translated synthetic python coding datasets.
- **Language(s) (NLP):** Russian, English, Python
- **License:** MIT
- **Finetuned from model:** HuggingFaceH4/zephyr-7b-beta

### Model Sources

<!-- Provide the basic links for the model. -->

- **Repository:** Comming soon...
- **Paper:** Comming soon...

## 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. -->
An experimental finetune of Zephyr-7b-beta, aimed at improving coding performance and support for coding-related instructions written in Russian language.

### Direct Use

<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->

Instruction-based coding in Python, based of instructions written in natural language (English or Russian)

Prompt template - Zephyr:
```  
<|system|>
</s>
<|user|>
{prompt}</s>
<|assistant|>
```  

## Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->
This adapter model is intended (but not limited) for research usage only. It was trained on a code based instruction set and it does not have any moderation mechanisms. Use at your own risk, we are not responsible for any usage or output of this model.

Quote from Zephyr (base-model) repository: "Zephyr-7B-β has not been aligned to human preferences for safety within the RLHF phase or deployed with in-the-loop filtering of responses like ChatGPT, so the model can produce problematic outputs (especially when prompted to do so). It is also unknown what the size and composition of the corpus was used to train the base model (mistralai/Mistral-7B-v0.1), however it is likely to have included a mix of Web data and technical sources like books and code. See the Falcon 180B model card for an example of this."

### 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.


## Training procedure


The following `bitsandbytes` quantization config was used during training:
- quant_method: QuantizationMethod.BITS_AND_BYTES
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16

### Framework versions


- PEFT 0.6.2
## Training procedure


The following `bitsandbytes` quantization config was used during training:
- quant_method: QuantizationMethod.BITS_AND_BYTES
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16

### Framework versions


- PEFT 0.6.2