Update README.md

README.md

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 ---
 library_name: transformers
-tags: []
+tags:
+- coding
+- reasoning
+- language
+- python
+- javascript
+- sql
+- agentic
+- lora
+- sft
+- qwen
+license: mit
+base_model: Qwen/Qwen3.5-4B-Thinking
+language:
+- en
+pipeline_tag: text-generation
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
+# aiAI Coder V2
 
+**4B Parameters • Production-Grade Code Generation • Full-Stack System Design**
 
+---
 
 ## Model Details
 
-### Model Description
+- **Base Model:** Qwen/Qwen3.5-4B-Thinking
+- **Parameter Count:** 4B
+- **Language:** English
+- **License:** MIT
 
-<!-- Provide a longer summary of what this model is. -->
+---
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+## Model Description
 
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
+aiAI Coder V2 is a fine-tuned version of Qwen3.5-4B-Thinking, trained on a custom dataset distilled from Grok. This release represents a significant step forward from V1.
 
-### Model Sources [optional]
+V1 (~5.6K examples) produced competent interview-style code. V2 (~28K examples) generates production-grade code across Python, JavaScript, and SQL, and can design scalable, load-balanced applications with proper prompting.
 
-<!-- Provide the basic links for the model. -->
+This model was trained on a single cloud 5090 in under 2 hours, keeping costs low while achieving major capability gains.
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+For user convenience, we have merged the LoRA adapter with the base model so you can download and test directly with Transformers without needing to load the base model separately.
 
-## Uses
+---
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+## Milestone Status
 
-### Direct Use
+- Code Generation ✅ Achieved
+- Debugging ✅ Achieved
+- System Design ✅ Achieved
+- Production Readiness ✅ Achieved
+- Scaling Thinking ✅ Achieved
+- Security Awareness ✅ Achieved
+- Full-Stack Knowledge ✅ Achieved
+- Agentic Capability 🚧 In Progress (V3)
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+---
 
-[More Information Needed]
+## Uses
 
-### Downstream Use [optional]
+### Direct Use
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+This model is intended for:
 
-[More Information Needed]
+- Code generation and completion
+- Debugging and bug fixing
+- System design and architecture planning
+- SQL query generation and optimization
+- Educational purposes and prototyping
 
 ### Out-of-Scope Use
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+- Production deployment without human review
+- Safety-critical systems without validation
+- Generating malicious code
+- Any use violating applicable laws
 
-[More Information Needed]
+---
 
 ## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
+- May occasionally hallucinate or produce incorrect code.
+- Generated code should be reviewed for security vulnerabilities.
+- Primarily trained on English data; other languages may perform poorly.
+- Context window is large (262K tokens) but may degrade at extreme lengths.
+- Not fully agentic; best used with tooling wrappers like OpenCode.
 
 ### Recommendations
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+- Review all generated code before deployment.
+- Run generated code in sandboxed environments.
+- Use safety filters for disallowed content.
 
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+---
 
 ## How to Get Started with the Model
 
-Use the code below to get started with the model.
-
-[More Information Needed]
+### Load with Transformers
 
-## Training Details
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
 
-### Training Data
+model_name = "aiAI_coder_V2_4B"
 
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype=torch.bfloat16,
+    device_map="auto"
+)
 
-[More Information Needed]
+messages = [
+    {"role": "system", "content": "You are a helpful coding assistant."},
+    {"role": "user", "content": "Write a Python function to reverse a linked list in-place."}
+]
 
-### Training Procedure
+inputs = tokenizer.apply_chat_template(
+    messages,
+    add_generation_prompt=True,
+    return_tensors="pt"
+).to(model.device)
 
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+outputs = model.generate(
+    inputs,
+    max_new_tokens=1024,
+    temperature=0.2,
+    do_sample=True
+)
 
-#### Preprocessing [optional]
+response = tokenizer.decode(outputs[0][inputs.shape[1]:], skip_special_tokens=True)
+print(response)
+```
 
-[More Information Needed]
+Load with vLLM (Production)
 
+```bash
+vllm serve aiAI_coder_V2_4B \
+    --max-model-len 8192 \
+    --tensor-parallel-size 1 \
+    --dtype bfloat16
+```
 
-#### Training Hyperparameters
+Load with Ollama
 
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+Create a Modelfile:
 
-#### Speeds, Sizes, Times [optional]
+```dockerfile
+FROM aiAI_coder_V2_4B
 
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+PARAMETER temperature 0.2
+PARAMETER num_ctx 8192
 
-[More Information Needed]
+TEMPLATE """{{ if .System }}<|im_start|>system
+{{ .System }}<|im_end|>
+{{ end }}{{ if .Prompt }}<|im_start|>user
+{{ .Prompt }}<|im_end|>
+{{ end }}<|im_start|>assistant
+"""
+```
 
-## Evaluation
+Then run:
 
-<!-- This section describes the evaluation protocols and provides the results. -->
+```bash
+ollama create aiAI-coder -f Modelfile
+ollama run aiAI-coder
+```
 
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
+---
 
-#### Factors
+Training Details
 
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+Training Data
 
-[More Information Needed]
+· Source: Distilled completions from Grok
+· Size: ~28,000 examples
+· Format: Multi-turn conversations with reasoning blocks
+· Focus Areas: Python, JavaScript, SQL, debugging, system design
 
-#### Metrics
+Training Procedure
 
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+· Method: LoRA Supervised Fine-Tuning (SFT)
+· Epochs: 1
+· Hardware: Single NVIDIA 5090 (32GB VRAM)
+· Training Time: <2 hours
 
-[More Information Needed]
+Training Hyperparameters
 
-### Results
+Parameter Value
+LoRA Rank (r) 16
+LoRA Alpha 32
+Learning Rate 2e-4
+Batch Size 4
+Context Length 2048
+Optimizer AdamW
+Scheduler Cosine with warmup
 
-[More Information Needed]
+---
 
-#### Summary
+Evaluation
 
+Benchmarks
 
+· HumanEval (Pass@1): Coming Soon
+· LiveCodeBench-v6: 54.2% (base model score)
 
-## Model Examination [optional]
+Internal Testing
 
-<!-- Relevant interpretability work for the model goes here -->
+The model passed comprehensive custom tests across:
 
-[More Information Needed]
+· Advanced algorithms (Manacher's O(n) palindrome)
+· System design (URL shortener with scaling)
+· Debugging (identifying subtle bugs with explanations)
+· OOP (encapsulation, validation)
+· Async (concurrent downloads)
+· Database design (PostgreSQL schema)
+· Security (rate limiting, injection prevention)
 
-## Environmental Impact
+---
 
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+Environmental Impact
 
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+· Hardware Type: NVIDIA 5090 (32GB VRAM)
+· Hours Used: <2 hours
+· Cloud Provider: AutoDL
+· Carbon Emitted: Estimated ~0.5–1.0 kg CO2 equivalent
 
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
+---
 
-## Technical Specifications [optional]
+Technical Specifications
 
-### Model Architecture and Objective
+Hardware
 
-[More Information Needed]
+· NVIDIA 5090 with 32GB VRAM
 
-### Compute Infrastructure
+Software
 
-[More Information Needed]
+· Transformers
+· PEFT (LoRA)
+· PyTorch
 
-#### Hardware
+---
 
-[More Information Needed]
+Citation
 
-#### Software
+If you use this model in your research or applications, please cite:
 
-[More Information Needed]
+```bibtex
+@misc{aiAI-coder-V2,
+  author = {aiAI},
+  title = {aiAI Coder V2: Production-Grade 4B Coding Assistant},
+  year = {2026},
+  publisher = {Hugging Face},
+  howpublished = {\url{https://huggingface.co/aiAI_coder_V2_4B}}
+}
+```
 
-## Citation [optional]
+---
 
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+More Information
 
-**BibTeX:**
+V3 is in development and will focus on:
 
-[More Information Needed]
+· Full agentic capabilities
+· Multi-turn task completion
+· Tool calling integration
+· Enhanced reasoning
+· Expanded language support
 
-**APA:**
+---
 
-[More Information Needed]
+Model Card Authors
 
-## Glossary [optional]
+· aiAI
+· nitrous-0xide (funding)
 
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+Model Card Contact
 
-[More Information Needed]
+wefam67@proton.me
 
-## More Information [optional]
+---
 
-[More Information Needed]
+Acknowledgments
 
-## Model Card Authors [optional]
+· Base Model: Qwen/Qwen3.5-4B-Thinking by Alibaba
+· Distillation Source: Grok
+· Training Infrastructure: AutoDL
 
-[More Information Needed]
+---
 
-## Model Card Contact
+This model is imperfect — V3 will be better. We're iterating fast. Expect improvements in the coming days/weeks.
 
-[More Information Needed]
+Happy coding! 🚀