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README.md

@@ -1,210 +1,95 @@
 ---
-base_model: unsloth/llama-3.2-1b-instruct-unsloth-bnb-4bit
-library_name: peft
-pipeline_tag: text-generation
 tags:
-- base_model:adapter:unsloth/llama-3.2-1b-instruct-unsloth-bnb-4bit
-- lora
-- sft
-- transformers
-- trl
+- quality-management
+- iso-standards
 - unsloth
+- llama-3.2
+- lora
+- gguf
+- ollama
+- auditing
+- continuous-improvement
+license: other
+language:
+- en
+base_model: unsloth/Llama-3.2-1B-Instruct
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
-
-
-## 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
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- 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.
-
-[More Information Needed]
-
-## 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]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### 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
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
+# Quality Management / ISO Standards LoRA — Llama 3.2 1B
 
-#### Hardware
+This is a LoRA adapter fine-tuned on **500,000** synthetic instruction-style samples covering quality-management systems, ISO standards, auditing, risk-based thinking, CAPA, Six Sigma, Lean, SPC, FMEA, and related topics.
 
-[More Information Needed]
+**Completed by Aboutknowledge (Hong Kong) Limited — Alex Lee.**
 
-#### Software
+**License note:** the LoRA weights in this repository are released under the same terms as the base model, the Llama 3.2 Community License Agreement. Please review Meta's license before using or redistributing the merged weights.
 
-[More Information Needed]
+## Model details
 
-## Citation [optional]
+| Item | Value |
+|------|-------|
+| Base model | `unsloth/Llama-3.2-1B-Instruct` |
+| Fine-tuning framework | Unsloth |
+| LoRA rank (r) | 16 |
+| LoRA alpha | 16 |
+| LoRA dropout | 0.0 |
+| Quantized training | 4-bit NF4 (bnb) |
+| Training records | 500,000 (subset of a 1,000,000 synthetic dataset) |
+| Training steps | 10,000 |
+| Final train loss | 0.1137 |
+| Sequence length | 2048 |
 
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+## Included files
 
-**BibTeX:**
+- `adapter_model.safetensors` / `adapter_config.json` — standard PEFT LoRA adapter.
+- `quality_lora_1m.q8_0.gguf` — Q8_0 GGUF file ready for Ollama.
+- `Modelfile` — example Ollama Modelfile (edit the `FROM` path after downloading).
 
-[More Information Needed]
+## Use with transformers / Unsloth
 
-**APA:**
+```python
+from unsloth import FastLanguageModel
 
-[More Information Needed]
+model, tokenizer = FastLanguageModel.from_pretrained(
+    model_name="unsloth/Llama-3.2-1B-Instruct",
+    max_seq_length=2048,
+    dtype=None,
+    load_in_4bit=True,
+)
+model = FastLanguageModel.get_peft_model(model, r=16, lora_alpha=16)
+model.load_adapter("alexlkc28/quality-lora-llama32-1b-1m", adapter_name="default")
 
-## Glossary [optional]
+messages = [
+    {"role": "system", "content": "You are an expert in quality management systems and ISO standards."},
+    {"role": "user", "content": "What does ISO 9001:2015 clause 8.7 require?"},
+]
+inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", tokenize=True).to("cuda")
+outputs = model.generate(inputs, max_new_tokens=256)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
 
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+## Use with Ollama
 
-[More Information Needed]
+1. Download the GGUF and `Modelfile` from this repo.
+2. Update the `FROM` line in the `Modelfile` to point to the downloaded GGUF path.
+3. Create the model:
 
-## More Information [optional]
+```bash
+ollama create quality-lora-1m -f /path/to/Modelfile
+ollama run quality-lora-1m
+```
 
-[More Information Needed]
+## Training data
 
-## Model Card Authors [optional]
+The data was generated synthetically from a compact ISO/quality-management knowledge base covering:
 
-[More Information Needed]
+- ISO 9001, 14001, 45001, 27001, 13485, 50001
+- IATF 16949 (automotive), AS9100D (aerospace)
+- PDCA, risk-based thinking, process approach, CAPA, 8D, 5 Whys, FMEA, SPC, MSA, 5S, Lean, Six Sigma
+- Audit checklists, nonconformity reports, KPI suggestions, interview questions
 
-## Model Card Contact
+The full 1,000,000-record dataset is available locally in the project directory as `data/quality_1m.jsonl`.
 
-[More Information Needed]
-### Framework versions
+## Limitations
 
-- PEFT 0.18.1
+- Synthetic data can contain occasional grammar artifacts or mix standards in generic answers; always verify against the official standard text for compliance decisions.
+- This is a small 1B model; while useful for Q&A and drafting, it should not replace human auditors or regulatory review.