Update README.md

README.md

@@ -1,199 +1,111 @@
 ---
+language:
+- en
+- code
+language_bcp47:
+- en
+- javascript
+license: apache-2.0
+tags:
+- text-generation
+- code
+- javascript
+- coding-assistant
+- fine-tuning
+- lora
+- unsloth
+- gpt-oss
+- vllm
+base_model: openai/gpt-oss-20b
 library_name: transformers
-tags: []
+pipeline_tag: text-generation
+model-index:
+- name: gpt-oss-coder-v0.1-javascript
+  results: []
 ---
 
-# Model Card for Model ID
+# gpt-oss-coder-v0.1-javascript
 
-<!-- Provide a quick summary of what the model is/does. -->
+A **language-specialized coding model for JavaScript**, fine-tuned from OpenAI’s open-weight **gpt-oss** base with **very small, curated JS data** using **Unsloth**.  
+This release prioritizes **practical code generation quality** over benchmark scores and has been **qualitatively validated** on real prompts (e.g., completions, refactors, docstrings, tests).
 
+> **Status**: Experimental preview (`v0.1-javascript`).  
+> **Focus**: JS coding tasks (function-level completion, small refactors, idiomatic patterns).  
+> **Why small-data?** Faster iteration and lower cost while proving specialization value.
 
+---
 
 ## Model Details
 
-### Model Description
-
-<!-- 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.
-
-- **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
+- **Model type**: Causal LM (decoder-only), JS-specialized fine-tune  
+- **Base model**: `openai/gpt-oss-20b` (open-weight, Apache-2.0)  
+- **Fine-tuning**: LoRA via **Unsloth**, minimal curated dataset (code snippets, tasks, transformations)  
+- **License**: Apache-2.0 (derivative weights released under Apache-2.0)  
+- **Author / Maintainer**: `hokar3361`  
+- **Intended Languages**: JavaScript (ES6+); English prompts recommended
 
-- **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]
-
-#### Hardware
-
-[More Information Needed]
+---
 
-#### Software
+## Intended Use & Limitations
 
-[More Information Needed]
+### Intended Use
+- Code completion and synthesis for **JavaScript**
+- Small refactors, idiomatic rewrites, test scaffolding, JSDoc/docstrings
+- Snippet-level reasoning and bug fixes
 
-## Citation [optional]
+### Out of Scope / Limitations
+- Not a substitute for static analysis, linters, or security review
+- May hallucinate APIs or types; verify before production use
+- Trained on **small** domain data → expect gaps on rare frameworks or edge APIs
 
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+---
 
-**BibTeX:**
+## Quickstart
 
-[More Information Needed]
+### 1) Transformers (merged weights)
 
-**APA:**
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
 
-[More Information Needed]
+repo = "hokar3361/gpt-oss-coderjs-v0.1"
+tok  = AutoTokenizer.from_pretrained(repo, use_fast=True)
+model = AutoModelForCausalLM.from_pretrained(
+    repo,
+    torch_dtype=torch.bfloat16,
+    device_map="auto"
+)
 
-## Glossary [optional]
+prompt = "```js\n// Write a function that flattens a nested array of numbers\n"
+inputs = tok(prompt, return_tensors="pt").to(model.device)
+out = model.generate(**inputs, max_new_tokens=128, temperature=0.3, do_sample=False)
+print(tok.decode(out[0], skip_special_tokens=True))
+2) vLLM (recommended)
+bash
+コードをコピーする
+vllm serve hokar3361/gpt-oss-coderjs-v0.1 \
+  --async-scheduling \
+  --max-model-len 4096 \
+  --gpu-memory-utilization 0.90
+For LoRA-only repos, add --lora-modules as per vLLM documentation.
 
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+For merged weights, the above command is sufficient.
 
-[More Information Needed]
+Acknowledgements
+This work was made possible thanks to the open-weight release of gpt-oss by OpenAI, which provided a strong foundation under the Apache-2.0 license.
+Special thanks to the open-source community around Unsloth for enabling memory-efficient and rapid LoRA fine-tuning on limited hardware.
+We also thank the Hugging Face and vLLM ecosystems for lowering the barrier to experimentation.
 
-## More Information [optional]
+Disclaimer & Experimental Status
+This model (v0.1-javascript) is highly experimental:
 
-[More Information Needed]
+Small data: Fine-tuned on a very small JavaScript-focused dataset, mainly to validate the workflow and feasibility of language specialization.
 
-## Model Card Authors [optional]
+Not production-ready: The model may generate incomplete, insecure, or non-idiomatic code; do not rely on it for production use without careful review.
 
-[More Information Needed]
+Benchmarks not representative: Due to issues in the current verification scripts, benchmark scores are not included. Assessment is based only on qualitative inspection of outputs, which show promising improvements but remain anecdotal.
 
-## Model Card Contact
+Early stage: This is only an initial exploration; future versions with larger, more diverse training corpora are expected to improve stability and coverage.
 
-[More Information Needed]
+We share this release to contribute to the community and gather early feedback.
+Use responsibly, validate outputs, and treat this as a proof-of-concept.