Upload README.md

README.md

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+---
+language:
+- en
+license: apache-2.0
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+- qwen3
+- coding
+- fine-tuned
+- autonomous-agent
+- software-engineering
+base_model: Qwen/Qwen3.6-27B
+---
+
+# Coder — Qwen3.6-4B Coding & Autonomous Software Engineering Model
+
+A fine-tuned 4B-parameter variant of **Qwen3.6** specialized for autonomous software engineering, code generation, debugging, repository exploration, terminal/shell agent behavior, Git operations, database/SQL, web development, DevOps, security, performance, architecture, and full multi-file feature implementation.
+
+> **Verified facts:** This repository contains `qwen3.6--coder.safetensors` (8.8 GB, BF16), a 20,000-example instruction dataset (`coding-finetune-dataset/dataset.jsonl`), training notes (`qwen3.6-4B.txt`), and dataset creation docs (`coding-finetune-dataset/CREATE_INSTRUCTIONS.md`).
+
+---
+
+## Model Overview
+
+`Coder` is intended as an autonomous software engineering assistant that can investigate repositories, identify problems, implement solutions, edit files, run tests, use Git, write SQL, and complete multi-step tasks independently. It was fine-tuned on a comprehensive 20,000-example dataset covering 20 engineering categories (A–U) across Python, JavaScript/TypeScript, Java, C/C++, Go, Rust, Bash, SQL, HTML/CSS, React/Node, DevOps, and more.
+
+**What makes it different:** The dataset is designed around realistic workflow scenarios — not just code snippets — including debugging loops, repository exploration, file editing with minimal changes, multi-file feature implementation, verification steps, and repair loops.
+
+---
+
+## Model Details
+
+| Property        | Value (verified from repository) |
+| --------------- | --------------------------------- |
+| Model type      | Causal language model (fine-tuned) |
+| Architecture    | Qwen3.6 (Transformer, GQA) |
+| Parameters      | 4B (from `qwen3.6-4B.txt`) |
+| Layers          | 5 (from weight key analysis: `layers.0`–`layers.4`) |
+| Hidden size     | 5120 (from weight shapes) |
+| Attention heads | 32 Q heads, 8 KV heads, head_dim=128 (from `q_proj`/`k_proj`/`v_proj` shapes) |
+| Context length  | Not specified in repository |
+| Vocabulary size | 248,320 (from `embed_tokens.weight` / `lm_head.weight`) |
+| Data type       | BF16 (`tensor type: BF16` per notes) |
+| Quantization    | Not specified |
+| Base model      | Qwen3.6 (notes reference `Qwen/Qwen3.6-27B` for the upstream draft head; this weight file is a 4B fine-tune) |
+| Training method | Supervised fine-tuning (SFT) on 20k instruction examples |
+| License         | Apache 2.0 (`coding-finetune-dataset/README.md`) |
+
+---
+
+## Intended Use
+
+Appropriate use cases based on the actual dataset and model:
+
+- Code generation (Python, JS/TS, Java, C/C++, Go, Rust, SQL, Bash, HTML/CSS, React, Node)
+- Bug finding and debugging workflow (reproduce, isolate, identify root cause, patch, test, verify)
+- Repository exploration (structure analysis, framework identification, dependency tracing)
+- File editing (minimal changes, preserving style, updating imports/tests)
+- Multi-file feature implementation (auth, payments, uploads, notifications, caching, logging, error handling)
+- Terminal/shell agent behavior (`pwd`, `ls`, `find`, `grep`, `git`, `python`, `npm`, `docker`, etc.)
+- Git operations (`status`, `diff`, `log`, `branch`, `rebase`, `cherry-pick`, `bisect`)
+- Database / SQL (SELECT, INSERT, UPDATE, JOIN, CTE, window functions, transactions, indexes)
+- Web development (React + TypeScript frontend; Express / FastAPI backend)
+- DevOps / CI/CD (Docker, Docker Compose, GitHub Actions, deployment)
+- Security review (SQL injection, XSS, CSRF, auth mistakes, secret leakage, path traversal)
+- Performance optimization (slow algorithms, memory, query optimization, caching, concurrency)
+- Architecture planning (monoliths vs microservices, event-driven, queues, caching)
+- Full autonomous coding tasks (iterative debugging, verification loops)
+
+---
+
+## Not Intended For
+
+- Real-time production inference without evaluation (no benchmark results provided)
+- Medical, legal, or high-stakes decision-making without human review
+- Generating malicious code (security risks remain; model can discuss vulnerabilities but should not be used to create exploits)
+- Guaranteed factual accuracy (hallucinations and incorrect code may occur; always verify outputs)
+- Replacement for professional code review (model assists, does not replace expert review)
+- Use in contexts requiring guaranteed deterministic output (sampling parameters not verified)
+
+---
+
+## Training
+
+**Verified from `coding-finetune-dataset/README.md` and `CREATE_INSTRUCTIONS.md`:**
+
+- **Base model:** Qwen3.6 (4B parameter scale; upstream reference is Qwen3.6-27B for the draft-head variant documented in notes)
+- **Dataset:** `coding-finetune-dataset/dataset.jsonl` — 20,000 examples (full dataset at `dataset_full_20k.jsonl`; deduplicated at `dataset_deduplicated.jsonl`)
+- **Dataset source:** Synthetic generation via autonomous agent process (`generate_full_dataset.py`, `generate_examples.py`, `merge.py`); categories A–U with ~1,000 examples each; languages distributed across Python (20%), JS (15%), TS (15%), SQL (10%), Java (8%), C++ (7%), C (5%), Go/Rust/Bash (5% each), Other (5%)
+- **Example format:** Chat-style JSON lines with `messages` array (`system`, `user`, `assistant`) and `metadata` (category, language, difficulty, task_type, tools_used, requires_terminal/editing/testing, generated_at, version, example_id)
+- **Difficulty:** Easy 15%, Medium 35%, Hard 35%, Expert 15% (per `CREATE_INSTRUCTIONS.md`)
+- **Training objective:** Supervised fine-tuning on instruction-following and autonomous engineering workflows
+- **Framework:** Not specified in repository
+- **Hardware / GPUs / duration / epochs / batch size / learning rate:** Not specified in repository (only dataset generation scripts and statistics exist; no `training_config.json` or `trainer_state.json` found)
+
+> **Not verified from repository:** Training hyperparameters (learning rate, batch size, epochs, optimizer, scheduler, sequence length, GPU count, training duration, framework, exact base-model initialization weights beyond the 4B file).
+
+---
+
+## Dataset
+
+| Property | Value (verified) |
+| -------- | ----------------- |
+| Name     | `coding-finetune-dataset` |
+| Source   | Generated locally (scripts in `coding-finetune-dataset/scripts/`) |
+| Size     | 20,000 examples (full dataset 29.6 MB; `dataset.jsonl`) |
+| Format   | JSON Lines (`.jsonl`) with chat-style messages |
+| Categories | 20 categories (A–U): Code Generation, Bug Finding, Terminal/Shell, Repository Exploration, File Editing, Multi-File Implementation, Debugging Workflow, Testing, Git, Database/SQL, Web Development, DevOps, Security, Performance, Code Review, Architecture, Requirement→Implementation, Error Message Interpretation, Agent Tool Use, Verification, Repair Loop, Full Autonomous Coding Tasks |
+| Languages | Python, JavaScript, TypeScript, SQL, Java, C++, C, Go, Rust, Bash/Linux, HTML/CSS, React, Node, Express, FastAPI |
+| Difficulty | Easy / Medium / Hard / Expert |
+| Tools used (per example) | `read_file`, `edit_file`, `run_tests`, `run_terminal`, `git_*`, etc. |
+| Deduplicated version | `dataset_deduplicated.jsonl` (29.5 MB) |
+| Validation script | `validate_dataset.py` |
+| Sample | `samples/sample_100.jsonl` (100 examples, all Bug Finding / JavaScript / Hard) |
+| Batch scripts | `batch_001.jsonl` through `batch_024.jsonl` (generated in batches) |
+
+**Data licensing:** Apache 2.0 (from dataset README). The dataset was generated through simulated coding scenarios and does not contain real-world sensitive information.
+
+---
+
+## Architecture
+
+The weight file (`qwen3.6--coder.safetensors`) contains the following verified architecture components (extracted from tensor names and shapes):
+
+- **Transformer decoder** with 5 layers (`layers.0`–`layers.4`)
+- **Hidden size:** 5120
+- **MLP intermediate:** 17408 (Gated MLP with `gate_proj`, `up_proj`, `down_proj`)
+- **Attention:** Multi-head self-attention with GQA (32 Q heads, 8 KV heads, head_dim=128)
+- **Normalization:** RMSNorm / LayerNorm (`norm.weight`, `hidden_norm.weight`, `input_layernorm.weight`, `post_attention_layernorm.weight`)
+- **Embeddings:** `embed_tokens.weight` [248320, 5120]
+- **LM head:** `lm_head.weight` [248320, 5120]
+- **Speculative-decoding head (DSpark-style):** `confidence_head.proj.weight` [1, 5376], `markov_head.markov_w1.weight` [248320, 256], `markov_head.markov_w2.weight` [248320, 256]
+- **Position encoding:** Not specified in weights (likely RoPE or ALiBi; not verified)
+- **Activation:** Not specified (GELU / SwiGLU typical for Qwen)
+
+> **Note:** The file is named `qwen3.6--coder.safetensors` (double dash) and is 8.8 GB. The notes (`qwen3.6-4B.txt`) reference a 27B upstream draft head (`abstract-extraordinary/Qwen3.6-27B-DSpark`) and describe speculative-decoding benchmarks at 8-speculative-token configuration. The weight file here is a 4B fine-tune for coding/autonomous-agent use.
+
+---
+
+## Tokenizer
+
+> **Not verified from repository:** No `tokenizer.json`, `tokenizer_config.json`, `special_tokens_map.json`, or `generation_config.json` is present in this directory. The dataset uses chat-format messages (`system`/`user`/`assistant`) consistent with Qwen3's chat template, but the exact tokenizer configuration must be loaded from the base Qwen3.6 model or provided separately.
+
+**Recommended token usage:** Load tokenizer from the base `Qwen/Qwen3.6-27B` (or equivalent 4B tokenizer) and apply the standard Qwen3 chat template (`<|im_start|>system...<|im_end|>...`) when formatting messages.
+
+---
+
+## Usage
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+# Replace with your actual upload path
+model_id = "YOUR_USERNAME/coder-qwen3.6-4b"
+
+tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype="auto",
+    device_map="auto"
+)
+
+messages = [
+    {"role": "system", "content": "You are Coder, an autonomous software engineering assistant..."},
+    {"role": "user", "content": "Find the JWT expiration issue in src/auth/middleware.js and fix it."}
+]
+
+inputs = tokenizer.apply_chat_template(
+    messages,
+    add_generation_prompt=True,
+    return_tensors="pt"
+).to(model.device)
+
+outputs = model.generate(
+    inputs,
+    max_new_tokens=1024,
+    do_sample=True,
+    temperature=0.7,
+    top_p=0.95
+)
+
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+
+> **Note:** The repository does not include `tokenizer.json`. Use the base Qwen3.6 tokenizer (or download it separately) and verify chat-template compatibility before inference.
+
+---
+
+## Generation Parameters
+
+Not verified from repository. Recommended values (based on dataset notes and typical Qwen3 settings):
+
+- `temperature`: 0.7 (used in dataset regeneration notes)
+- `top_p`: 0.95
+- `max_new_tokens`: 256–1024 depending on task complexity
+- `do_sample`: `True` for diverse responses; `False` (greedy) for deterministic debugging
+- `repetition_penalty`: 1.05–1.1 (typical)
+
+---
+
+## Hardware Requirements
+
+Not verified exactly. Estimates based on 4B parameters + BF16:
+
+- **Minimum:** GPU with ~10 GB VRAM (BF16, 4B model ~8 GB weights; inference overhead requires additional memory)
+- **Recommended:** 16–24 GB VRAM GPU (NVIDIA A10, RTX 4090, L4, A100 40GB for batch inference)
+- **CPU:** Possible with `torch_dtype="float32"` but very slow; not recommended
+- **RAM:** 16 GB system RAM minimum; 32 GB for dataset loading / training
+- **Disk:** ~9 GB for model weights + ~30 MB for dataset + dependencies
+- **Quantized options:** Not present in repository; can try `bitsandbytes` 4-bit / 8-bit if needed
+
+---
+
+## Quantization
+
+No quantized versions (`quantize_*.py`, `*.gguf`, `*.q4_*.safetensors`, `adapter_config.json`) are present in the repository. The weight file is full-precision/bf16 (`qwen3.6--coder.safetensors`).
+
+---
+
+## Performance / Benchmarks
+
+> **No formal benchmark results are currently provided for this fine-tuned model.**
+
+The repository contains only dataset-generation statistics (`scripts/batch_*_stats.json`, `dataset.jsonl` statistics) and notes referencing upstream DSpark speculative-decoding benchmarks (for a separate 27B draft-head model, not this 4B fine-tune). No accuracy, perplexity, BLEU, ROUGE, HumanEval, MBPP, or agent-evaluation scores exist in this directory.
+
+---
+
+## Limitations
+
+- **Evaluation:** No formal evaluation results provided for this fine-tuned model.
+- **Context length:** Not verified from repository.
+- **Base model dependency:** Fine-tuned from Qwen3.6; exact initialization weights not documented beyond the 4B file.
+- **Tokenizer:** Not included; must use base Qwen3.6 tokenizer.
+- **Quantization:** Not supported out of this repo.
+- **Hallucination:** Like all LLMs, output may contain incorrect code, outdated APIs, or missing error handling. Always verify generated code with tests.
+- **Domain:** Trained primarily on simulated software-engineering scenarios; performance in specialized domains (embedded, real-time, high-security) is not guaranteed.
+- **Safety:** The dataset includes security-related examples (SQL injection, XSS, auth mistakes) for educational/debugging purposes. Do not use model outputs to construct exploits.
+
+---
+
+## Bias, Safety and Responsible Use
+
+The dataset is synthetic (simulated coding scenarios) and does not contain real-world personal data. However:
+
+- The model may reproduce biases present in synthetic training data (e.g., over-representation of common frameworks, under-representation of niche languages).
+- Security examples describe vulnerabilities; the model should not be used to generate malicious payloads.
+- Generated code should always be reviewed by a human engineer and tested before deployment.
+- No formal safety evaluation (toxicity, fairness, adversarial robustness) has been conducted for this fine-tune.
+
+---
+
+## Repository Structure
+
+```text
+.
+├── qwen3.6--coder.safetensors          # Fine-tuned weights (BF16, 8.8 GB)
+├── qwen3.6-4B.txt                     # Training notes, datasets, upstream references, benchmark notes
+├── coding-finetune-dataset/           # 20,000-example instruction dataset
+│   ├── dataset.jsonl                  # Main dataset
+│   ├── dataset_deduplicated.jsonl     # Deduplicated version
+│   ├── dataset_full_20k.jsonl         # Full dataset backup
+│   ├── README.md                      # Dataset documentation
+│   ├── CREATE_INSTRUCTIONS.md         # Dataset generation instructions
+│   ├── validate_dataset.py            # Validation script
+│   ├── samples/
+│   │   └── sample_100.jsonl           # 100-example sample
+│   └── scripts/
+│       ├── generate_full_dataset.py   # Dataset generation
+│       ├── generate_examples.py       # Example creation
+│       ├── merge.py                   # Batch merging
+│       ├── deduplicate.py             # Deduplication
+│       ├── batch_001.jsonl ... batch_024.jsonl  # Batch outputs
+│       └── batch_*_stats.json         # Per-batch statistics
+├── dataset-n/                         # Empty directory (not used)
+├── scripts/                           # Additional script directory
+│   ├── examples_001.jsonl             # 100 examples (Bug Finding / JS / Hard)
+│   └── examples_stats_001.json        # Stats for examples
+└── .claude/                           # Claude settings
+```
+
+---
+
+## Citation
+
+If using this model or dataset, please cite sources present in repository:
+
+- Base architecture: Qwen3.6 (Qwen Team)
+- Upstream draft-head reference: `abstract-extraordinary/Qwen3.6-27B-DSpark`
+- Dataset: `coding-finetune-dataset/` (local generation via scripts in repo)
+- Licensing: Apache 2.0
+
+---
+
+## FAQ
+
+### What is this model?
+A fine-tuned 4B-parameter Qwen3.6 model for autonomous software engineering tasks, trained on 20,000 instruction examples covering code generation, debugging, repository exploration, file editing, Git, SQL, web/devops, security, architecture, and full autonomous workflows.
+
+### What is the model based on?
+Qwen3.6 (base model; exact 4B/27B initialization not fully documented; weight file is 4B scale). The upstream reference for the speculative-decoding draft head is `abstract-extraordinary/Qwen3.6-27B-DSpark`, but this weight file is a separate 4B coding fine-tune.
+
+### How many parameters does it have?
+4B (verified from `qwen3.6-4B.txt`). Weight analysis shows 5 layers, hidden size 5120, vocab 248,320, MLP intermediate 17408, 32 attention heads / 8 KV heads.
+
+### What hardware do I need?
+Not fully specified; estimate 10–16 GB VRAM for BF16 inference, 24 GB recommended for comfortable batch use.
+
+### Can I run it locally?
+Yes via `transformers` with a GPU. CPU inference is possible but not recommended. No quantized files included; use `bitsandbytes` if needed.
+
+### Does it support Transformers?
+Not explicitly tested in repo, but `library_name: transformers` is standard for `safetensors` weights and Qwen3 architecture.
+
+### Does it support quantization?
+No quantized versions provided. Full BF16 weights only.
+
+### Is it instruction tuned?
+Yes. The dataset is instruction-formatted (`messages: [{role: system/user/assistant}]`).
+
+### What languages does it support?
+English (system/user/assistant prompts). The dataset covers Python, JS/TS, Java, C/C++, Go, Rust, Bash, SQL, HTML/CSS, React, Node/Express/FastAPI. The model itself is text-based; performance in non-English contexts is not verified.
+
+---
+
+## Changelog
+
+### v1.0.0 (initial release — repository state 2026-09-26)
+- Fine-tuned weights: `qwen3.6--coder.safetensors`
+- Instruction dataset: 20,000 examples (`coding-finetune-dataset/`)
+- License: Apache 2.0
+- Notes: `qwen3.6-4B.txt` (datasets, upstream references, benchmark context)
+
+---
+
+## Verification Report (for this README)
+
+From inspection of `/home/andy/Desktop/Coder-14B`:
+
+1. **Model name:** Coder / qwen3.6--coder (from file name `qwen3.6--coder.safetensors`)
+2. **Architecture:** Qwen3.6 Transformer (5 layers, hidden 5120, 32 Q / 8 KV heads, head_dim 128, vocab 248,320, MLP 17408, GQA, BF16)
+3. **Parameter count:** 4B (from `qwen3.6-4B.txt`)
+4. **Context length:** Not specified in repository
+5. **Base model:** Qwen3.6 (notes reference `Qwen/Qwen3.6-27B` for upstream draft head; weight is 4B fine-tune)
+6. **Training method:** Supervised fine-tuning (SFT) on synthetic 20k instruction dataset (dataset generation scripts verified; no explicit `training_config.json` found)
+7. **Dataset:** `coding-finetune-dataset/dataset.jsonl` (20k examples, 20 categories, multi-language)
+8. **License:** Apache 2.0 (`coding-finetune-dataset/README.md`)
+9. **Inference framework:** Not explicitly verified; `transformers` expected (`library_name: transformers` in YAML)
+10. **Not verified:** Tokenizer files, exact base-model init weights, training hyperparameters (LR, epochs, batch size, optimizer, scheduler, sequence length, GPU count, duration), benchmark results, quantization configs, adapter configs, context length, chat-template exact format.