base_model: Qwen/Qwen3.5-4B
library_name: transformers
license: apache-2.0
language:
- en
pipeline_tag: text-generation
tags:
- qwen3.5
- coding
- reasoning
- lora
- sft
- agentic
- python
- javascript
- sql
base_model_relation: finetune
THE MUCH IMPROVED V2 IS NOW RELEASE @ wefamm/aiAI_coder_V2_4B
aiAI_coder_V1.4B
4B parameters • Fine-tuned for coding & agentic tasks • <1 hour training
Model Overview
aiAI_coder_V1.4B is a specialized coding and agentic assistant fine-tuned from Qwen/Qwen3.5-4B. It is designed to excel in:
- Multi-language Code Generation: Python, JavaScript, TypeScript, and SQL
- Reasoning & Problem-Solving: Step-by-step thinking with
<think> tag support
- Agentic Workflows: Tool calling, multi-turn interactions, and task completion
- Instruction Adherence: Following complex, constrained prompts with high accuracy
- Cost Efficiency: Optimized for low-latency inference on consumer hardware
This model was distilled from high-quality Grok 4.6 completions and trained with a highly efficient Supervised Fine-Tuning (SFT) recipe, achieving strong coding benchmark performance at a fraction of the cost of larger models [citation:1][citation:7].
Model Details
Model Description
aiAI_coder_V1.4B is an instruction-tuned language model optimized for code synthesis, debugging, and agentic assistance. It supports:
- Fast, deterministic responses for coding tasks
- Accurate code generation in Python, JavaScript, TypeScript, and SQL
- Multi-turn reasoning with explicit thinking separation (
<think>...</think>)
- Native support for tool calling and structured outputs
The model can be used as a lightweight, cost-effective alternative to frontier models in many developer workflows.
- Developed by: [aiAI]
- Funded by: [nitrous-0xide (owner & founder)]
- Model type: Text-generation / Instruction-following
- Language(s): English
- License: Apache-2.0
- Finetuned from: Qwen/Qwen3.5-4B [citation:1][citation:10]
Uses
Direct Use
The model can be used as-is for:
- Interactive coding assistants and chatbots
- Code completion and debugging in IDEs
- Generating unit tests and documentation
- SQL query generation and optimization
- Agentic workflows requiring planning and tool use [citation:1]
Out-of-Scope Use
- Generating malicious code or content that violates applicable laws
- Real-time decision-making in safety-critical systems
- Any use that violates the Apache-2.0 license
Bias, Risks, and Limitations
- Hallucination: May occasionally produce plausible but incorrect code or explanations
- Security: Generated code should be reviewed for security vulnerabilities
- Context Window: While optimized for 262K context, performance may degrade at extreme lengths [citation:7]
- Language Coverage: Primarily trained on English data; performance on other languages is limited
Recommendations
- Human-in-the-loop review of generated code before deployment
- Use explicit safety filters for disallowed content
- Test outputs in sandboxed environments when executing generated code
