This dataset was generated using teich by TeichAI

Prepare these datasets for supervised fine-tuning in just a few lines of code — see the Conversion section below.

DeepSeek v4 Pro Agent Traces

This directory contains raw agent trace files generated by teich.

All assistant responses were generated by deepseek/deepseek-v4-pro.

JSONL files: 4006

Training-ready tools

A complete configured tools schema snapshot is embedded in the collapsed section at the bottom of this README. Use it when rendering loaded examples through your training chat template. load_traces applies this snapshot to each loaded example as the tools field.

Format

Each file is newline-delimited JSON representing a single captured agent session. The trace schema is designed for upload-first preservation so you can keep the original session history and convert it later for training.

Common top-level event groups:

• session_meta
• turn_context
• event_msg
• response_item
• session
• message
• session_info
• model_change
• thinking_level_change

Example

{"type":"session","version":3,"id":"019e03aa-8ec4-768e-9833-edc257e9203a","timestamp":"2026-05-07T18:19:29.863Z","cwd":"/workspace"}
{"type":"message","id":"system-bca663f1","parentId":null,"timestamp":"2026-05-07T18:19:31.559Z","message":{"role":"developer","content":[{"type":"text","text":"You are an expert coding assistant operating inside pi, a coding agent harness. You help users by reading files, executing commands, editing code, and writing new files.\n\nAvailable tools:\n- read: Read file contents\n- bash: Execute bash commands (ls, grep, find, etc.)\n- edit: Make precise file edits with exact text replacement, including multiple disjoint edits in one call\n- write: Create or overwrite files\n\nIn addition to the tools above, you may have access to other custom tools depending on the project.\n\nGuidelines:\n- Use bash for file operations like ls, rg, find\n- Use read to examine files instead of cat or sed.\n- Use edit for precise changes (edits[].oldText must match exactly)\n- When changing multiple separate locations in one file, use one edit call with multiple entries in edits[] instead of multiple edit calls\n- Each edits[].oldText is matched against the original file, not after earlier edits are applied. Do not emit overlapping or nested edits. Merge nearby changes into one edit.\n- Keep edits[].oldText as small as possible while still being unique in the file. Do not pad with large unchanged regions.\n- Use write only for new files or complete rewrites.\n- Be concise in your responses\n- Show file paths clearly when working with files\n\nPi documentation (read only when the user asks about pi itself, its SDK, extensions, themes, skills, or TUI):\n- Main documentation: /usr/local/lib/node_modules/@mariozechner/pi-coding-agent/README.md\n- Additional docs: /usr/local/lib/node_modules/@mariozechner/pi-coding-agent/docs\n- Examples: /usr/local/lib/node_modules/@mariozechner/pi-coding-agent/examples (extensions, custom tools, SDK)\n- When asked about: extensions (docs/extensions.md, examples/extensions/), themes (docs/themes.md), skills (docs/skills.md), prompt templates (docs/prompt-templates.md), TUI components (docs/tui.md), keybindings (docs/keybindings.md), SDK integrations (docs/sdk.md), custom providers (docs/custom-provider.md), adding models (docs/models.md), pi packages (docs/packages.md)\n- When working on pi topics, read the docs and examples, and follow .md cross-references before implementing\n- Always read pi .md files completely and follow links to related docs (e.g., tui.md for TUI API details)\nCurrent date: 2026-05-07\nCurrent working directory: /workspace"}]}}
{"type":"model_change","id":"5d586b96","parentId":null,"timestamp":"2026-05-07T18:19:31.376Z","modelId":"deepseek/deepseek-v4-pro"}

Conversion

Recommended: train with Unsloth and TRL SFTTrainer

Use the trainer-first path: prepare_data renders trainer-friendly text rows with Teich supervision metadata, SFTTrainer tokenizes them, then mask_data applies Teich's multi-turn/tool-aware response-only labels:

import os

from unsloth import FastLanguageModel
import torch
from trl import SFTConfig, SFTTrainer

from teich import mask_data, prepare_data

MAX_SEQ_LEN = 32768
MODEL_NAME = 'unsloth/Qwen3.5-0.8B'
CHAT_TEMPLATE_KWARGS = {'enable_thinking': True}
PUSH_TO_HUB_REPO_ID = 'username/teich-sft-model'
HF_TOKEN = os.environ.get('HF_TOKEN') or ''

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name=MODEL_NAME,
    max_seq_length=MAX_SEQ_LEN,
    load_in_4bit=False,
    load_in_8bit=False,
    full_finetuning=False,
)

model = FastLanguageModel.get_peft_model(
    model,
    r=32,
    target_modules=['q_proj', 'k_proj', 'v_proj', 'o_proj', 'gate_proj', 'up_proj', 'down_proj', 'out_proj'],
    lora_alpha=64,
    lora_dropout=0,
    bias='none',
    use_gradient_checkpointing='unsloth',
    random_state=3407,
    use_rslora=False,
    loftq_config=None,
)

train_dataset = prepare_data(
    'armand0e/DeepSeek-v4-Pro-Agent',
    tokenizer,
    split='train',
    max_examples=500,
    chat_template_kwargs=CHAT_TEMPLATE_KWARGS,
    max_length=MAX_SEQ_LEN,
    drop_oversized_examples=True,
    tokenize=True,
    strict=True,
)

trainer = SFTTrainer(
    model=model,
    tokenizer=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=None,
    args=SFTConfig(
        dataset_text_field='text',
        dataset_num_proc=1,
        max_length=MAX_SEQ_LEN,
        packing=False,
        per_device_train_batch_size=1,
        gradient_accumulation_steps=4,
        warmup_steps=5,
        num_train_epochs=1,
        learning_rate=2e-4,
        logging_steps=1,
        optim='muon',
        optim_target_modules='all-linear',
        weight_decay=0.001,
        lr_scheduler_type='linear',
        output_dir='outputs',
        seed=3407,
        report_to='none',
    ),
)

trainer = mask_data(
    trainer,
    tokenizer=tokenizer,
    train_on_reasoning=True,
    train_on_final_answers=True,
    train_on_tools=True,
)

trainer_stats = trainer.train(resume_from_checkpoint=False)

model.push_to_hub_merged(PUSH_TO_HUB_REPO_ID, tokenizer, save_method='merged_16bit', token=HF_TOKEN)

mask_data keeps the normal trainer configuration flow while applying Teich's assistant/tool-call labels after trainer tokenization. Keep packing=False for this flow. If you want standard next-token training without Teich response-only labels, call prepare_data(..., teich_masking=False) and skip mask_data().

You can combine this dataset with other Teich chat-only or tool-call datasets by passing a list of dataset IDs, local paths, or loaded datasets.Dataset objects:

train_dataset = prepare_data(
    ['armand0e/DeepSeek-v4-Pro-Agent', 'username/other-teich-dataset'],
    tokenizer,
    max_length=MAX_SEQ_LEN,
    drop_oversized_examples=True,
    tokenize=True,
    chat_template_kwargs=CHAT_TEMPLATE_KWARGS,
)

Fallback: render loaded examples with your tokenizer

Use load_traces directly only when you want to own the remaining training pipeline yourself: chat-template rendering, filtering, tokenization, label masking, packing policy, and auditing. load_traces returns rows with normalized messages ready for tokenizer.apply_chat_template(...):

from teich import load_traces

dataset = load_traces('armand0e/DeepSeek-v4-Pro-Agent')
example = dataset[0]
rendered = tokenizer.apply_chat_template(
    example['messages'],
    tools=example.get('tools') or [],
    tokenize=False,
    add_generation_prompt=False,
    enable_thinking=True,
)

Tool schema snapshot

[
  {
    "type": "function",
    "function": {
      "name": "bash",
      "description": "Run shell commands in the workspace.",
      "parameters": {
        "type": "object",
        "properties": {
          "cmd": {
            "type": "string"
          },
          "cwd": {
            "type": "string"
          }
        },
        "required": [
          "cmd"
        ],
        "additionalProperties": true
      }
    }
  },
  {
    "type": "function",
    "function": {
      "name": "read_file",
      "description": "Read file contents from the workspace.",
      "parameters": {
        "type": "object",
        "properties": {
          "path": {
            "type": "string"
          }
        },
        "required": [
          "path"
        ],
        "additionalProperties": true
      }
    }
  },
  {
    "type": "function",
    "function": {
      "name": "write_file",
      "description": "Write file contents in the workspace.",
      "parameters": {
        "type": "object",
        "properties": {
          "path": {
            "type": "string"
          },
          "content": {
            "type": "string"
          }
        },
        "required": [
          "path",
          "content"
        ],
        "additionalProperties": true
      }
    }
  }
]