JX-Coder-7B-Agent-Behavior

<div align="center"> <a href="https://jumplander.org"> <img src="https://www.jumplander.org/assets/images/logo/logo-jumplander-v2.png" alt="JumpLander logo" width="130"> </a>

JX Coder 7B Agent Behavior

A specialized behavioral-policy adapter for controlled software-engineering agents

Website · Hugging Face · Dataset · Base Model

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Table of Contents

• Model Overview
• Why This Model Exists
• Release Positioning
• Model Architecture
• Training Data
• Training Objective
• Behavioral Capabilities
• Training Configuration
• Installation
• Quick Start
• Four-Bit Loading
• Chat Inference
• Structured Agent Inference
• Merging the Adapter
• Using the Model in an Agent Runtime
• Recommended Prompts
• Expected Output Behavior
• Limitations
• Evaluation Status
• Safety and Deployment Notes
• Versioning
• Roadmap
• License and Attribution
• Citation
• About JumpLander

---

Model Overview

JX Coder 7B Agent Behavior is a Parameter-Efficient Fine-Tuning adapter developed by JumpLander for controlled software-engineering agents.

The release is built on top of:

• Base model: Qwen/Qwen2.5-Coder-7B-Instruct
• Fine-tuning method: 4-bit QLoRA / PEFT
• Primary dataset: jumplander/JL-AgentBehavior-10K
• Primary language: English
• Artifact type: LoRA adapter
• Primary purpose: coding-agent behavioral policy
• Developer: JumpLander

This repository contains the trained adapter weights, not a standalone copy of the full 7B base model.

At inference time, the adapter is loaded on top of Qwen2.5-Coder-7B-Instruct:

Qwen2.5-Coder-7B-Instruct
            +
JX Coder 7B Agent Behavior Adapter
            =
JX Coder 7B Agent Behavior

The small adapter file size is expected. The base model provides general language and coding capability, while the JX adapter modifies the model toward a more controlled agent policy.

---

Why This Model Exists

Many coding models are optimized to generate an answer or code block immediately after receiving a request.

That behavior is useful for code completion, but it is not sufficient for a reliable software-engineering agent operating on a real repository.

A repository-level agent must make a sequence of bounded decisions:

user request
    ↓
interpret the task
    ↓
identify constraints and approval boundaries
    ↓
inspect repository evidence
    ↓
build a proportional plan
    ↓
select the correct tool
    ↓
make a scoped change
    ↓
run relevant verification
    ↓
diagnose failures
    ↓
report only what evidence supports

The objective of this release is not to replace the coding ability of the base model. Qwen2.5-Coder already provides strong code-oriented language-model capabilities.

The objective is to specialize the model toward behaviors that matter inside an agent runtime:

• understanding the actual requested outcome;
• separating facts from assumptions;
• grounding repository references in available evidence;
• respecting explicit constraints;
• avoiding unrelated edits;
• requesting approval before sensitive operations;
• validating changes before claiming success;
• changing the hypothesis after a failed attempt;
• producing structured decisions that a runtime can execute.

JumpLander is developing JX as a controlled environment connecting language models to repositories, files, terminal commands, tests, memory, diffs, and user approval. This model is one component of that larger system.

Learn more about the project at jumplander.org.

---

Release Positioning

This release should be understood as:

A behavioral-policy warm-start for software-engineering agents.

It should not be described as:

• a model trained from random initialization;
• a fully autonomous coding agent;
• a runtime-verified repository repair model;
• a replacement for repository execution;
• a standalone benchmark winner;
• a fully bilingual English–Persian model.

The adapter is developed and fine-tuned by JumpLander, while the underlying language-model architecture and base weights come from Qwen2.5-Coder-7B-Instruct.

---

Model Architecture

The adapter is designed to be loaded with the peft library.

---

Training Data

Primary Dataset

The primary data source is:

jumplander/JL-AgentBehavior-10K

JL-AgentBehavior-10K is a JumpLander research-preview dataset designed to study and train behavioral policy for repository-level coding agents.

The dataset emphasizes the process around software changes rather than only the final answer.

Its behavioral structure includes concepts such as:

task
→ repository evidence
→ bounded plan
→ tool selection
→ scoped edit strategy
→ verification
→ failure diagnosis and repair
→ evidence-based final report

The dataset contains structured supervision for:

• trajectory decisions;
• selected and rejected behaviors;
• failure diagnosis and repair;
• repository grounding;
• tool selection;
• bounded editing;
• verification;
• approval boundaries;
• evidence-aware reporting.

Local Training Snapshot

The local preprocessing pipeline used for this adapter produced:

The local snapshot and preprocessing view counts describe this training run. They should not be interpreted as replacing the official dataset card, package splits, or version history.

Data Language

The behavioral supervision used in this release is primarily English.

Persian-language examples were not present at a scale sufficient to claim strong Persian generation quality.

Data Evidence Level

The dataset is intended for behavioral-policy research and training. Synthetic tool descriptions, candidate commands, expected observations, or repair paths do not prove that real repository operations were executed.

Users should review the complete dataset documentation before making claims about runtime correctness:

• Dataset card
• JumpLander organization
• JumpLander website

---

Training Objective

The adapter was trained to make the base model more likely to follow a controlled software-engineering policy.

Core Objectives

1. Goal grounding

   Identify the requested outcome instead of reacting only to keywords.

2. Constraint extraction

   Preserve restrictions such as:

   • do not modify unrelated files;
   • do not add dependencies;
   • keep the public API stable;
   • inspect before editing;
   • ask before destructive actions.

3. Repository grounding

   Avoid inventing files, functions, tests, command outputs, or repository state.

4. Authority awareness

   Distinguish actions that can proceed automatically from actions requiring explicit approval.

5. Tool selection

   Select a tool that matches the current information need.

6. Bounded planning

   Build a plan proportional to the task rather than producing unnecessary broad changes.

7. Verification discipline

   Avoid claiming a fix is complete without relevant evidence.

8. Failure diagnosis

   Update the hypothesis after a failed test or unexpected observation.

9. Critique and repair

   Identify why a trajectory was unsafe, unsupported, or ineffective and propose a bounded correction.

10. Evidence-aware reporting

    Clearly separate:

    • verified results;
    • observed facts;
    • assumptions;
    • unresolved risks;
    • suggested next actions.

---

Behavioral Capabilities

This release is intended to improve policy behavior in the following areas.

Goal Grounding

The model can structure an incoming task into an interpreted request, missing information, relevant constraints, and a next action.

Repository-Aware Planning

When repository evidence is available, the model can use it to recommend an inspection or edit sequence.

Tool-Oriented Decisions

The model can produce decisions suitable for mapping to runtime tools such as:

search
list_directory
read_file
update_plan
apply_patch
run_tests
run_linter
git_diff
diagnose_failure
review_diff
request_approval

The runtime must map these abstract actions to its actual interfaces.

Constraint Handling

The model is trained to treat user constraints as part of the task contract, not as optional preferences.

Failure Recovery

The model can critique a failed attempt, revise the diagnosis, and suggest a more bounded repair sequence.

Evidence-Based Completion

The model is intended to avoid unsupported statements such as “the issue is fixed” when no test or runtime evidence has been provided.

---

Training Configuration

The following configuration describes the training setup used for this adapter.

Hardware note: RTX 3090 24GB is recorded here as the reported hardware for the release. Maintainers should reconcile this field with the archived training log before treating it as independently verified metadata.

Training Behavior Observed

Training loss decreased rapidly and token-level training accuracy became very high.

This indicates that the adapter strongly learned the structured output patterns present in the training views. It also creates a risk of over-structuring: the model may emit agent-style JSON for ordinary conversational requests.

This behavior is documented as a limitation rather than hidden.

---

Installation

Create a Python environment and install the required libraries:

pip install -U torch transformers accelerate peft bitsandbytes safetensors

Recommended versions should be selected according to the local CUDA and PyTorch environment.

Check CUDA availability:

import torch

print("PyTorch:", torch.__version__)
print("CUDA available:", torch.cuda.is_available())

if torch.cuda.is_available():
    print("GPU:", torch.cuda.get_device_name(0))

---

Quick Start

This adapter requires the base model.

Replace the adapter identifier below with the final Hugging Face repository ID if it differs.

import torch
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

BASE_MODEL_ID = "Qwen/Qwen2.5-Coder-7B-Instruct"
ADAPTER_ID = "jumplander/JX-Coder-7B-Agent-Behavior"

tokenizer = AutoTokenizer.from_pretrained(
    ADAPTER_ID,
    trust_remote_code=True,
)

base_model = AutoModelForCausalLM.from_pretrained(
    BASE_MODEL_ID,
    torch_dtype="auto",
    device_map="auto",
    trust_remote_code=True,
)

model = PeftModel.from_pretrained(
    base_model,
    ADAPTER_ID,
)

model.eval()

messages = [
    {
        "role": "system",
        "content": (
            "You are JX Coder 7B Agent Behavior, developed by JumpLander "
            "on top of Qwen2.5-Coder-7B-Instruct. "
            "Ground decisions in available evidence. "
            "Do not claim that repository operations were executed unless "
            "the runtime provides execution results."
        ),
    },
    {
        "role": "user",
        "content": (
            "A user reports that authentication redirects back to the login "
            "page after a successful sign-in. Do not edit files yet. "
            "Explain what repository evidence should be inspected first."
        ),
    },
]

prompt = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
)

inputs = tokenizer(
    prompt,
    return_tensors="pt",
).to(model.device)

with torch.inference_mode():
    output_ids = model.generate(
        **inputs,
        max_new_tokens=512,
        temperature=0.2,
        do_sample=True,
        top_p=0.9,
        repetition_penalty=1.05,
    )

generated_ids = output_ids[0, inputs["input_ids"].shape[-1]:]

response = tokenizer.decode(
    generated_ids,
    skip_special_tokens=True,
)

print(response)

---

Four-Bit Loading

For lower VRAM usage, load the base model in 4-bit.

import torch
from peft import PeftModel
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
    BitsAndBytesConfig,
)

BASE_MODEL_ID = "Qwen/Qwen2.5-Coder-7B-Instruct"
ADAPTER_ID = "jumplander/JX-Coder-7B-Agent-Behavior"

compute_dtype = (
    torch.bfloat16
    if torch.cuda.is_available() and torch.cuda.is_bf16_supported()
    else torch.float16
)

quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_use_double_quant=True,
    bnb_4bit_compute_dtype=compute_dtype,
)

tokenizer = AutoTokenizer.from_pretrained(
    ADAPTER_ID,
    trust_remote_code=True,
)

base_model = AutoModelForCausalLM.from_pretrained(
    BASE_MODEL_ID,
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True,
)

model = PeftModel.from_pretrained(
    base_model,
    ADAPTER_ID,
)

model.eval()

---

Chat Inference

The adapter is strongly biased toward structured agent outputs.

For normal conversational usage, use an explicit chat-mode system instruction.

CHAT_SYSTEM_PROMPT = """
You are JX Coder 7B Agent Behavior, developed by JumpLander.
Respond naturally and directly.
Do not return agent JSON unless the user explicitly requests structured output.
Do not claim to have accessed files, executed commands, or run tests.
"""

messages = [
    {"role": "system", "content": CHAT_SYSTEM_PROMPT},
    {"role": "user", "content": "Explain dependency injection in PHP."},
]

A system prompt can reduce unnecessary structuring, but it cannot fully remove behavior learned during fine-tuning.

For production use, JumpLander recommends a runtime-level mode selector.

chat
coding
debug
review
agent

Each mode should use a distinct system prompt and output contract.

---

Structured Agent Inference

Use an explicit schema when the output will be consumed by software.

import json

AGENT_SYSTEM_PROMPT = """
You are JX Coder 7B Agent Behavior, a behavioral-policy model developed by JumpLander.

Return one valid JSON object with these keys:

- mode
- interpreted_request
- constraints
- missing_information
- recommended_action
- tool
- arguments
- evidence_required
- approval_required
- completion_status

Rules:
1. Do not invent repository evidence.
2. Do not claim that a command was executed.
3. Prefer inspection before mutation.
4. Respect the user's explicit scope.
5. Request approval before sensitive or destructive actions.
6. completion_status must be "pending" unless fresh evidence proves completion.
"""

messages = [
    {"role": "system", "content": AGENT_SYSTEM_PROMPT},
    {
        "role": "user",
        "content": (
            "Fix the PHP login redirect loop. Preserve the public API, "
            "do not add dependencies, and do not modify unrelated files. "
            "No repository files have been provided yet."
        ),
    },
]

Example target shape:

{
  "mode": "repository_grounding",
  "interpreted_request": {
    "goal": "Diagnose and repair the PHP login redirect loop",
    "task_type": "bug_fix"
  },
  "constraints": [
    "Preserve the public API",
    "Do not add dependencies",
    "Do not modify unrelated files"
  ],
  "missing_information": [
    "Authentication controller or handler",
    "Session initialization code",
    "Login success redirect logic",
    "Relevant route or middleware configuration"
  ],
  "recommended_action": "Inspect authentication and session flow before editing",
  "tool": "search",
  "arguments": {
    "query": "login session redirect authentication middleware"
  },
  "evidence_required": [
    "Relevant file paths",
    "Session creation path",
    "Redirect condition",
    "Existing authentication tests"
  ],
  "approval_required": false,
  "completion_status": "pending"
}

The generated output may not always conform perfectly to a schema. Production systems should validate and repair model output before tool execution.

---

Merging the Adapter

The published artifact is an adapter.

To create a merged model locally:

import torch
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

BASE_MODEL_ID = "Qwen/Qwen2.5-Coder-7B-Instruct"
ADAPTER_ID = "jumplander/JX-Coder-7B-Agent-Behavior"
OUTPUT_DIR = "./jx-coder-7b-agent-behavior-merged"

tokenizer = AutoTokenizer.from_pretrained(
    BASE_MODEL_ID,
    trust_remote_code=True,
)

base_model = AutoModelForCausalLM.from_pretrained(
    BASE_MODEL_ID,
    torch_dtype=torch.float16,
    device_map="cpu",
    trust_remote_code=True,
)

model = PeftModel.from_pretrained(
    base_model,
    ADAPTER_ID,
)

merged_model = model.merge_and_unload()

merged_model.save_pretrained(
    OUTPUT_DIR,
    safe_serialization=True,
    max_shard_size="4GB",
)

tokenizer.save_pretrained(OUTPUT_DIR)

print(f"Merged model saved to: {OUTPUT_DIR}")

Important Notes

• Merging requires enough system RAM or VRAM.
• The merged output will be much larger than the adapter.
• The merged model remains a derivative of Qwen2.5-Coder-7B-Instruct.
• Review the base-model license before redistribution.
• Validate the merged model before publishing it as a separate repository.

---

Using the Model in an Agent Runtime

This adapter does not provide repository access by itself.

A complete runtime should supply tools, state, permission controls, and validation.

Recommended Runtime Layers

User Interface
    ↓
Mode Router
    ↓
Prompt and Context Builder
    ↓
JX Coder 7B Agent Behavior
    ↓
Schema Validator
    ↓
Permission Gateway
    ↓
Tool Runtime
    ↓
Repository / Terminal / Tests
    ↓
Observation Normalizer
    ↓
Model Re-evaluation
    ↓
Evidence-Based Final Report

Recommended Tool Interface

A runtime may expose tools such as:

{
  "name": "read_file",
  "description": "Read a repository file without modifying it.",
  "parameters": {
    "path": "string",
    "start_line": "integer or null",
    "end_line": "integer or null"
  }
}

{
  "name": "apply_patch",
  "description": "Apply a bounded patch to an allowed repository file.",
  "parameters": {
    "path": "string",
    "patch": "unified diff string"
  }
}

{
  "name": "run_tests",
  "description": "Run an approved test command and return structured output.",
  "parameters": {
    "command": "string",
    "timeout_seconds": "integer"
  }
}

Runtime Responsibilities

The runtime, not the model, must enforce:

• allowed directories;
• command allowlists;
• network permissions;
• secret handling;
• approval boundaries;
• timeouts;
• process isolation;
• patch-size limits;
• test execution;
• log capture;
• rollback;
• output-schema validation.

Never execute model-generated commands without validation.

---

Recommended Prompts

Repository Grounding

A user reports that updating a session returns stale state.

Constraints:
- Preserve the public API.
- Do not add dependencies.
- Do not edit files yet.

List the repository evidence required before proposing a patch.

Bounded Planning

Create a minimal plan for fixing a login redirect loop.

Known files:
- auth/login.php
- auth/session.php
- middleware/guest.php
- tests/auth/LoginTest.php

Do not produce code. Identify the likely inspection order and the evidence needed.

Failure Diagnosis

The targeted authentication test still fails after the first patch.

Observed result:
Expected redirect: /panel
Actual redirect: /login

The session cookie is present.

Revise the hypothesis and propose the next diagnostic action.

Diff Review

Review the following patch for:
- unrelated changes;
- public API breakage;
- missing tests;
- unsupported success claims;
- security risks.

Return findings in severity order.

Approval Boundary

The proposed fix requires deleting cached session files in production.

Determine whether approval is required and explain the safest next action.

---

Expected Output Behavior

The model may produce structured objects containing fields such as:

mode
interaction
user_input
interpreted_request
constraints
missing_information
response
recommended_action
tool
arguments
request_user_action

This is expected because the adapter was trained primarily on structured behavioral supervision.

Recommended Deployment Strategy

Use separate modes:

Mode selection should happen in the application layer rather than relying entirely on the model to infer the desired format.

---

Limitations

1. English-First Release

The primary training data is English.

Persian understanding and generation are experimental and limited. The model may:

• answer in English after a Persian request;
• generate broken Persian;
• misinterpret Persian technical instructions;
• return structured JSON instead of natural Persian.

Do not market this release as fully bilingual.

2. Over-Structured Responses

The model may return agent-style JSON for simple questions.

This is a direct consequence of the training objective and data distribution.

3. No Native Tool Execution

The model cannot independently:

• read repository files;
• apply patches;
• run terminal commands;
• execute tests;
• inspect a browser;
• access private systems;
• verify production state.

These capabilities require an external runtime.

4. Synthetic Behavioral Data

Synthetic trajectories can teach useful policies, but they do not replace:

• real repository snapshots;
• executed patches;
• hidden tests;
• human code review;
• production incident evidence;
• contamination analysis;
• independent benchmarks.

5. No Standalone Correctness Claim

This release has not established general repository-repair correctness.

A model can produce a plausible plan while still being wrong.

6. Template Memorization Risk

Rapid loss reduction and high token-level training accuracy indicate strong adaptation to training templates.

This may reduce output diversity and increase schema repetition.

7. Base-Model Dependency

The adapter requires a compatible Qwen2.5-Coder-7B-Instruct base model.

Behavior can vary across:

• Transformers versions;
• PEFT versions;
• quantization settings;
• generation parameters;
• chat templates;
• runtime prompts.

8. Context Length Used During Fine-Tuning

The adapter was trained with a maximum sequence length of 1,024 tokens.

Long repository contexts were not directly represented at their full deployment length during this training run.

---

Evaluation Status

This release is a research and engineering artifact.

At publication time, claims should remain limited to:

• successful adapter training;
• strong learning of structured behavioral formats;
• observed identity and agent-policy adaptation;
• compatibility with the declared base model;
• local inference through PEFT.

The release does not yet provide a complete independent benchmark report covering:

• HumanEval;
• MBPP;
• MultiPL-E;
• SWE-bench;
• repository-level executable repair;
• tool-call accuracy;
• schema-validity rate;
• Persian benchmarks;
• safety-policy adherence;
• regression against the unmodified base model.

Recommended Evaluation Plan

Future evaluation should compare:

Base Qwen2.5-Coder-7B-Instruct
vs.
Base + JX Agent Behavior Adapter

Suggested metrics:

• goal extraction accuracy;
• constraint retention;
• repository hallucination rate;
• correct first tool choice;
• invalid tool-argument rate;
• approval-boundary accuracy;
• success-claim calibration;
• failure-recovery quality;
• JSON schema validity;
• patch-scope compliance;
• targeted test selection;
• natural-chat degradation.

---

Safety and Deployment Notes

This model can generate code, shell commands, configuration changes, and operational instructions.

Deployment systems should:

1. treat generated content as untrusted;
2. validate all JSON outputs;
3. restrict filesystem access;
4. restrict command execution;
5. isolate processes;
6. protect credentials and secrets;
7. require approval for destructive actions;
8. log tool calls and observations;
9. run targeted tests;
10. review diffs before application;
11. separate model proposals from verified results;
12. provide rollback.

The model should never be the sole authority for production deployment, security remediation, database migration, credential rotation, destructive file operations, or other high-impact actions.

---

Versioning

Model Release

Recommended repository name:

jumplander/JX-Coder-7B-Agent-Behavior

Recommended initial release label:

1.0 Research Preview

This label communicates that:

• the adapter is a real public release;
• the behavioral specialization is defined;
• the model is still under active evaluation;
• runtime-level capabilities remain outside the adapter;
• future revisions may change data balance, schemas, and inference behavior.

Suggested Version Policy

---

Roadmap

Planned research directions for the JX model family include:

• conversational and agent mode switching;
• Persian technical alignment;
• repository-grounded code repair;
• executable tool calling;
• schema-constrained decoding;
• tool-result interpretation;
• patch generation and review;
• test selection;
• failure recovery loops;
• long-context repository understanding;
• memory-aware agent behavior;
• human approval policy;
• evaluation against real repository tasks;
• smaller specialized JX models for routing, debugging, review, and verification.

Follow development through:

• JumpLander
• JumpLander on Hugging Face
• JL-AgentBehavior-10K

---

License and Attribution

Adapter

This repository is released under the license declared in the Hugging Face metadata and repository files.

Base Model

The adapter is derived from:

Qwen/Qwen2.5-Coder-7B-Instruct

Users must review and comply with the base model's license and usage terms.

Dataset

The primary JumpLander dataset is:

jumplander/JL-AgentBehavior-10K

Users should review the dataset card, provenance statements, limitations, and license before use.

Required Technical Description

When describing the model, use language similar to:

JX Coder 7B Agent Behavior is a PEFT/QLoRA adapter developed by JumpLander on top of Qwen2.5-Coder-7B-Instruct and trained with behavioral supervision derived from JL-AgentBehavior-10K.

Do not describe the adapter as a 7B model trained from scratch by JumpLander.

---

Citation

Model

@software{jumplander_jx_coder_7b_agent_behavior_2026,
  author       = {JumpLander},
  title        = {JX Coder 7B Agent Behavior},
  year         = {2026},
  version      = {1.0-research-preview},
  publisher    = {Hugging Face},
  url          = {https://huggingface.co/jumplander/JX-Coder-7B-Agent-Behavior},
  base_model   = {Qwen/Qwen2.5-Coder-7B-Instruct}
}

Dataset

@dataset{jumplander_agentbehavior_10k_2026,
  author       = {JumpLander},
  title        = {JL-AgentBehavior-10K: Structured Behavioral Supervision for Coding Agents},
  year         = {2026},
  version      = {1.0.0},
  publisher    = {Hugging Face},
  url          = {https://huggingface.co/datasets/jumplander/JL-AgentBehavior-10K}
}

---

About JumpLander

JumpLander is an AI research and engineering project focused on:

• agent systems;
• specialized models and training;
• agentic datasets and evaluation;
• intelligent software engineering;
• repository intelligence;
• controlled tool execution;
• knowledge systems;
• developer infrastructure.

JX is JumpLander's controlled software-engineering agent environment. Its purpose is to connect models to repositories, files, diffs, tools, terminal commands, tests, memory, and human approval through an observable and bounded workflow.

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Build. Learn. Research. Innovate.

Visit JumpLander · Explore the Dataset · View the Base Model

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