---
title: Agentic-Coding-Tessa
canonical_url: "https://www.modelscope.cn/datasets/smirki/Agentic-Coding-Tessa"
md_url: "https://www.modelscope.cn/datasets/smirki/Agentic-Coding-Tessa.md"
repository: smirki/Agentic-Coding-Tessa
last_updated: 2025-08-12
license: apache-2.0
storage_size: "53 MB"
downloads: 186
stars: 1
---

# Agentic-Coding-Tessa

> Agentic-Coding-Tessa - smirki 在 ModelScope 开源的数据集。Agentic Coding Dataset for Tessa

smirki/Agentic-Coding-Tessa 是 ModelScope 魔搭社区上的数据集，存储大小 53 MB，采用 apache-2.0 许可。

- **Repository**: smirki/Agentic-Coding-Tessa
- **License**: apache-2.0
- **Storage size**: 53 MB
- **Downloads**: 186
- **Stars**: 1
- **Last updated**: 2025-08-12

Source: https://www.modelscope.cn/datasets/smirki/Agentic-Coding-Tessa

---

# Agentic Coding Dataset for Tessa

A comprehensive dataset for training coding agents with tool-use, reasoning, and software engineering capabilities.

## Dataset Composition

This dataset combines multiple high-quality sources:

- **hermes_reasoning** (20.0%): Tool-use and reasoning dataset - [interstellarninja/hermes_reasoning_tool_use](https://huggingface.co/datasets/interstellarninja/hermes_reasoning_tool_use)
- **search_arena** (15.0%): Search and retrieval tasks - [lmarena-ai/search-arena-24k](https://huggingface.co/datasets/lmarena-ai/search-arena-24k)
- **arena_human_pref** (15.0%): Human preference data for alignment - [lmarena-ai/arena-human-preference-140k](https://huggingface.co/datasets/lmarena-ai/arena-human-preference-140k)
- **rstar_coder** (25.0%): Advanced coding problems with reasoning - [microsoft/rStar-Coder](https://huggingface.co/datasets/microsoft/rStar-Coder)
- **swe_bench** (25.0%): Software engineering trajectories - [SWE-bench/SWE-smith-trajectories](https://huggingface.co/datasets/SWE-bench/SWE-smith-trajectories)


## Dataset Statistics

- **Total samples**: 44,100
- **Format**: Axolotl-compatible conversation format
- **Fields**: `conversations` (list of turns with `from` and `value` keys)

## Usage with Axolotl

```yaml
datasets:
  - path: smirki/Agentic-Coding-Tessa
    type: chat_template
    field_messages: conversations
    message_property_mappings:
      role: from
      content: value
    split: train
```

## Training Configuration for UIGEN-X

Recommended configuration for UIGEN-X-4B with this dataset:

```yaml
# Model
base_model: Tesslate/UIGEN-X-4B-0729
chat_template: chatml  # For Qwen3-based models

# LoRA Configuration
adapter: lora
lora_r: 256
lora_alpha: 512
lora_dropout: 0.05
lora_target_modules:
  - q_proj
  - k_proj
  - v_proj
  - o_proj
  - gate_proj
  - up_proj
  - down_proj

# Training
sequence_len: 8192  # Extended for code
micro_batch_size: 4
gradient_accumulation_steps: 4
num_epochs: 2
learning_rate: 5e-4
```

## Example Structure

```json
{
  "conversations": [
    {
      "from": "system",
      "value": "You are an expert programming assistant..."
    },
    {
      "from": "human",
      "value": "Help me implement a binary search algorithm"
    },
    {
      "from": "gpt",
      "value": "I'll help you implement binary search..."
    }
  ],
  "source": "dataset_name"
}
```

## License

Apache 2.0 (inherited from constituent datasets)

## Citation

```bibtex
@dataset{agentic_coding_tessa_2024,
  title={Agentic Coding Dataset for Tessa},
  author={Smirki},
  year={2024},
  publisher={HuggingFace}
}
```