| --- |
| license: openmdw-1.1 |
| library_name: transformers |
| pipeline_tag: text-generation |
| tags: |
| - qwen3.5 |
| - text-generation |
| - agent-memory |
| - trace-summarization |
| - text-only |
| - lora |
| base_model: |
| - Qwen/Qwen3.5-0.8B |
| --- |
| |
| # compctx-0.8b |
|
|
| **A compact model that turns AI agent execution traces into structured memory notes.** |
|
|
| `compctx` takes an agent's work log and compresses it into a concise, structured summary of the task, constraints, completed actions, failures, decisions, and next steps. |
|
|
| It is designed for agent memory systems where preserving useful execution context matters more than generating conversational responses. |
|
|
| ## Overview |
|
|
| - **Base model:** [Qwen/Qwen3.5-0.8B](https://huggingface.co/Qwen/Qwen3.5-0.8B) |
| - **Architecture:** `Qwen3_5ForCausalLM` |
| - **Parameters:** 752M |
| - **Modality:** Text-only |
| - **Fine-tuning:** Supervised fine-tuning (SFT) with LoRA |
| - **LoRA configuration:** rank 128, alpha 128 |
| - **Weights:** LoRA merged into the base model |
|
|
| The original multimodal architecture was converted into a text-only causal language model. The trained language-model weights were transferred, and the LoRA adapter was converted and merged into the resulting checkpoint. |
|
|
| ## Output format |
|
|
| The model produces notes using a fixed structure: |
|
|
| ```text |
| Goal: <what the user wants, one sentence> |
| Constraints: <rules and limits, or "none"> |
| State: |
| - [done] <completed action and its result> |
| - [failed] <action and exact error> |
| - [planned] <intended action without a confirmed result> |
| Decisions: <approach chosen or rejected, and why> |
| Next: <next unfinished step, or "unclear"> |
| ``` |
|
|
| The model distinguishes between completed actions, failures, and planned work. It is instructed to use only information present in the trace and avoid inventing missing details. |
|
|
| ## Usage |
|
|
| The tokenizer's chat template includes the system prompt and a few-shot demonstration. Provide a single user message containing the agent trace: |
|
|
| ```python |
| from transformers import AutoModelForCausalLM, AutoTokenizer |
| import torch |
| |
| model_id = "aksern/compctx-0.8b" |
| |
| tokenizer = AutoTokenizer.from_pretrained(model_id) |
| model = AutoModelForCausalLM.from_pretrained( |
| model_id, |
| torch_dtype=torch.bfloat16, |
| device_map="auto", |
| ) |
| |
| trace = """ |
| [TRACE] [2026-10-30 06:24:00] [AGENT: Orchestrator] |
| User Prompt: "Find the errors in the file script.py and fix them." |
| |
| [SPAN] [Thinking Process] |
| "To solve the task, I first need to read the contents of the file script.py. I will use the view_file tool." |
| |
| [SPAN] [Tool Call: view_file] |
| Arguments: {"path": "script.py"} |
| Response: "def main():\n print('Hello' + 5)" |
| |
| [SPAN] [Thinking Process] |
| "There is a TypeError in the code: you cannot add a string and a number together. You need to replace 5 with '5' or use an f-string. I am invoking the patch_file tool." |
| |
| [SPAN] [Tool Call: patch_file] |
| Arguments: {"path": "script.py", "edit": "print(f'Hello {5}')"} |
| Response: "File patched successfully." |
| |
| [TRACE END] Agent completed the task. |
| """ |
| |
| messages = [ |
| {"role": "user", "content": trace}, |
| ] |
| |
| prompt = tokenizer.apply_chat_template( |
| messages, |
| tokenize=False, |
| add_generation_prompt=True, |
| ) |
| |
| inputs = tokenizer( |
| prompt, |
| return_tensors="pt", |
| add_special_tokens=False, |
| ).to(model.device) |
| |
| with torch.inference_mode(): |
| output = model.generate( |
| **inputs, |
| max_new_tokens=512, |
| do_sample=False, |
| ) |
| |
| result = tokenizer.decode( |
| output[0][inputs["input_ids"].shape[1]:], |
| skip_special_tokens=True, |
| ) |
| |
| print(result) |
| ``` |
|
|
| The chat template automatically: |
| - Adds the system instructions. |
| - Includes the few-shot example used during training. |
| - Wraps the input in `<trace>` tags. |
| - Adds the assistant generation prefix. |
|
|
| **Input:** one user message containing a plain-text agent trace. |
|
|
| **Output:** a structured memory note. |
|
|
| ## Intended use |
|
|
| - Compressing AI agent execution logs. |
| - Preserving task progress between agent sessions. |
| - Extracting completed actions and unresolved errors. |
| - Creating compact context for long-running agent workflows. |
| - Building lightweight memory components for tool-using agents. |
|
|
| ## Limitations |
|
|
| - Not a general-purpose conversational assistant. |
| - Designed around the specific structured output format above. |
| - Accuracy depends on the information available in the input trace. |
| - May omit details when the input exceeds its context or output budget. |
|
|
| ## Training |
|
|
| Fine-tuned from Qwen3.5-0.8B using supervised fine-tuning and LoRA. |
|
|
| The training objective was to transform agent execution traces into structured memory notes while preserving exact technical details such as paths, commands, identifiers, and error messages. |
|
|
| The resulting adapter was merged into a standalone text-only checkpoint. |
|
|
| ## License |
|
|
| This model is based on Qwen3.5-0.8B. Check the base model's license and applicable terms before redistribution or commercial use. The license for this model is OpenMDW-1.1 |
