Add model card with usage instructions and training details

README.md

@@ -2,208 +2,209 @@
 base_model: Qwen/Qwen2.5-Coder-7B-Instruct
 library_name: peft
 pipeline_tag: text-generation
+license: apache-2.0
+language:
+- en
 tags:
 - base_model:adapter:Qwen/Qwen2.5-Coder-7B-Instruct
 - lora
 - sft
 - transformers
 - trl
+- jac
+- jaclang
+- code-generation
+- data-spatial-programming
 ---
 
-# Model Card for Model ID
+# Jac Coder 7B LoRA
 
-<!-- Provide a quick summary of what the model is/does. -->
+A LoRA adapter fine-tuned on the [Jac programming language](https://www.jac-lang.org/) for code generation, built on top of [Qwen2.5-Coder-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-7B-Instruct).
 
+Jac is a data-spatial programming language featuring walkers, nodes, edges, and graph-based computation. This adapter teaches the base model to generate idiomatic Jac backend code including node/edge definitions, walker APIs, graph traversals, and ability implementations.
 
+## Example Outputs
 
-## Model Details
-
-### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
-
-
-
-- **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]
-
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-
-## 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. -->
-
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
-
-### Out-of-Scope Use
+**Prompt:** "Write a Jac node for a User with name, email, and age fields"
+```jac
+node User {
+    has name: str;
+    has email: str;
+    has age: int = 0;
+}
+```
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+**Prompt:** "Write a Jac walker for a REST API endpoint that creates a new todo item"
+```jac
+node Todo {
+    has title: str;
+    has done: bool = False;
+}
 
-[More Information Needed]
+walker CreateTodo {
+    has title: str;
 
-## Bias, Risks, and Limitations
+    can create with Root entry {
+        here ++> Todo(title=self.title);
+        report [-->];
+    }
+}
+```
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-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.
+## Model Details
 
-[More Information Needed]
+- **Base model:** Qwen/Qwen2.5-Coder-7B-Instruct
+- **Adapter type:** LoRA (rank 64, alpha 128)
+- **Trainable params:** 161,480,704 / 7,777,097,216 (2.08%)
+- **Target modules:** q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
+- **Developed by:** [farhan98ahzan](https://huggingface.co/farhan98ahzan)
+- **License:** Apache 2.0
+
+## How to Use
+
+### With PEFT (recommended)
+
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
+from peft import PeftModel
+
+BASE_MODEL = "Qwen/Qwen2.5-Coder-7B-Instruct"
+ADAPTER = "farhan98ahzan/jac-coder-7b-lora"
+
+tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL, trust_remote_code=True)
+
+# Load base model in 4-bit (for low VRAM)
+bnb_config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_quant_type="nf4",
+    bnb_4bit_compute_dtype=torch.bfloat16,
+)
+model = AutoModelForCausalLM.from_pretrained(
+    BASE_MODEL,
+    quantization_config=bnb_config,
+    device_map="auto",
+    trust_remote_code=True,
+)
+
+# Apply LoRA adapter
+model = PeftModel.from_pretrained(model, ADAPTER)
+model.eval()
+
+# Generate
+messages = [
+    {"role": "system", "content": "You are an expert Jac programming language assistant."},
+    {"role": "user", "content": "Write a Jac walker that lists all users"},
+]
+text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+inputs = tokenizer(text, return_tensors="pt").to(model.device)
+
+with torch.no_grad():
+    outputs = model.generate(**inputs, max_new_tokens=512, temperature=0.7, top_p=0.9, do_sample=True)
+
+generated = outputs[0][inputs["input_ids"].shape[1]:]
+print(tokenizer.decode(generated, skip_special_tokens=True))
+```
+
+### Merging the adapter (for full model export)
+
+To merge LoRA weights into the base model, load the base model in **bf16 (not 4-bit)** to avoid rounding errors:
+
+```python
+from transformers import AutoModelForCausalLM
+from peft import PeftModel
+
+base = AutoModelForCausalLM.from_pretrained(
+    "Qwen/Qwen2.5-Coder-7B-Instruct",
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+    trust_remote_code=True,
+)
+model = PeftModel.from_pretrained(base, "farhan98ahzan/jac-coder-7b-lora")
+merged = model.merge_and_unload()
+merged.save_pretrained("jac-coder-7b-merged")
+```
+
+> **Warning:** Do not merge into a 4-bit quantized base model -- this produces corrupted weights and gibberish output.
 
 ## Training Details
 
 ### Training Data
 
-<!-- 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. -->
-
-[More Information Needed]
-
-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
+The adapter was trained on 3,200 curated Jac code samples sourced from:
 
-#### Training Hyperparameters
+| Source | Description |
+|---|---|
+| jaseci/jaseci | Core Jac compiler repo -- examples, tests, reference implementations |
+| BeaconLens | Full-stack Jac application (review analysis platform) |
+| jac-visual-builder | Visual graph schema builder in Jac |
+| Jac documentation | 936 code examples extracted from official docs |
 
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+All source files were validated with `jac check --parse_only` for syntactic correctness. Only backend Jac code was included (frontend/UI files filtered out).
 
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
+**Dataset composition:**
 
-<!-- This section describes the evaluation protocols and provides the results. -->
+| Type | Count | Description |
+|---|---|---|
+| full_file | 800 | Complete valid Jac source files |
+| construct_completion | 800 | Walker/node/ability signature to body completion |
+| completion | 800 | Import + partial code to complete the rest |
+| doc_example | 800 | Documentation description to Jac code |
 
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-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:** [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]
-
-### Model Architecture and Objective
+### Training Procedure
 
-[More Information Needed]
+- **Method:** QLoRA (4-bit NF4 quantization + LoRA)
+- **Framework:** Hugging Face TRL (SFTTrainer)
+- **Epochs:** 1
+- **Batch size:** 2 per device, gradient accumulation 4 (effective batch 8)
+- **Learning rate:** 2e-4 with cosine schedule
+- **Max sequence length:** 512 tokens
+- **Precision:** bf16
+- **Gradient checkpointing:** enabled
+- **Packing:** disabled (required for correctness without flash attention)
 
 ### Compute Infrastructure
 
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
+- **Hardware:** 2x NVIDIA Tesla T4 (15.6 GB VRAM each)
+- **Platform:** Kaggle Notebooks (free tier)
+- **Training time:** ~5.5 hours
+- **Total steps:** 380
 
-[More Information Needed]
-
-## 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. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
+## Evaluation
 
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+Qualitative evaluation on held-out prompts:
 
-[More Information Needed]
+| Prompt | Result |
+|---|---|
+| Node definition with typed fields | Correct `node` with `has` fields and defaults |
+| Walker with graph traversal | Correct `walker` with `[-->]` traversal and `report` |
+| REST API endpoint walker | Correct walker with `Root entry`, node creation (`++>`), and response |
 
-## More Information [optional]
+The model generates syntactically valid Jac code with proper use of language-specific constructs: `node`, `walker`, `has`, `can`, `with ... entry`, `++>`, `[-->]`, `report`, and `disengage`.
 
-[More Information Needed]
+## Limitations
 
-## Model Card Authors [optional]
+- Trained on 1 epoch of 3,200 samples -- may not cover all Jac patterns
+- Max training sequence length was 512 tokens -- longer code may be truncated
+- Backend-only -- does not generate Jac frontend/UI code (`.cl.jac`)
+- Based on Jac language version 0.13.5 -- syntax may differ in newer versions
 
-[More Information Needed]
+## Citation
 
-## Model Card Contact
+```bibtex
+@misc{jac-coder-7b-lora,
+  title={Jac Coder 7B LoRA},
+  author={Farhan Ahzan},
+  year={2026},
+  publisher={HuggingFace},
+  url={https://huggingface.co/farhan98ahzan/jac-coder-7b-lora}
+}
+```
 
-[More Information Needed]
-### Framework versions
+### Framework Versions
 
-- PEFT 0.18.1
+- PEFT 0.18.1
+- Transformers 4.51.3
+- TRL 0.18.1
+- PyTorch 2.6.0
+- BitsAndBytes 0.45.5