ijktech
/

ByteGPT-r1

@@ -1,199 +1,147 @@
 ---
 library_name: transformers
-tags: []
 ---
-# 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. -->
-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
-- **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
-[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]
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-[More Information Needed]
-## More Information [optional]
-[More Information Needed]
-## Model Card Authors [optional]
-[More Information Needed]
-## Model Card Contact
-[More Information Needed]

 ---
 library_name: transformers
+tags:
+  - gpt
+  - distillation
+  - mobile
+  - embedded
+  - onnx
+license: cc-by-nc-4.0
+datasets:
+  - custom
+  - web
+language: en
+widget:
+  - text: "In order to make pancakes, you need to"
+  - text: "Once upon a time"
 ---
+<p align="center">
+  <img src="logo.png" alt="IJK Technology" width="150">
+</p>
+<h1 align="center">IJK Technology – ByteGPT-r1</h1>
+**ByteGPT-r1** is a distilled version of DeepSeek's QWEN 1.5B model, optimized specifically for mobile and edge computing environments. It maintains impressive language capabilities while being designed for compute- and memory-constrained devices.
+## 🚀 Overview
+- **Model Type:** Distilled GPT-style causal language model
+- **Base Model:** DeepSeek's QWEN 1.5B
+- **Intended Use:** Edge devices, mobile phones, embedded systems
+- **Size:** Optimized for mobile deployment
+- **Training:** Knowledge distillation from QWEN 1.5B
+## 🧠 Why ByteGPT-r1?
+ByteGPT-r1 offers several advantages for mobile and edge deployment:
+1. **Efficient Knowledge Distillation:**
+   Carefully distilled from DeepSeek's QWEN 1.5B model to preserve capabilities while reducing computational requirements.
+2. **Mobile-First Design:**
+   Architected specifically for the constraints of mobile devices, with optimizations for both inference speed and memory usage.
+3. **Balanced Performance:**
+   Maintains a good balance between model size and language generation capabilities, making it practical for real-world mobile applications.
+## 💡 Future Plans
+This model is part of our ongoing effort to bring powerful language models to edge devices. Upcoming releases will include:
+- **Specialized Variants:** Domain-specific versions optimized for particular use cases
+- **Further Optimizations:** Continued improvements in efficiency and performance
+- **Benchmark Results:** Comparative performance on various mobile devices
+- **Integration Examples:** More code samples for popular mobile frameworks
+## 💻 Usage
+### **Quick Start (with `transformers`):**
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+model = AutoModelForCausalLM.from_pretrained("ijktech/ByteGPT-r1", trust_remote_code=True)
+tokenizer = AutoTokenizer.from_pretrained("ijktech/ByteGPT-r1")
+input_text = "What is the capital of France?"
+inputs = tokenizer(input_text, return_tensors="pt")
+outputs = model.generate(**inputs, max_new_tokens=100)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+### Tokenizer
+The tokenizer is compatible with AutoTokenizer from Hugging Face:
+```python
+tokenizer = AutoTokenizer.from_pretrained("ijktech/ByteGPT-r1")
+```
+### ONNX
+The model is also available in ONNX format, and can be used with the ONNX Runtime:
+```python
+import onnxruntime as ort
+import numpy as np
+# Create ONNX Runtime session
+ort_session = ort.InferenceSession("model.onnx")
+# Helper function to generate text using the ONNX model
+def generate_with_onnx(prompt_ids, max_new_tokens=50, temperature=1.0):
+    input_ids = prompt_ids.clone()
+    for _ in range(max_new_tokens):
+        # Get the last block_size tokens if input is too long
+        if input_ids.shape[1] > model.block_size:
+            input_ids = input_ids[:, -model.block_size:]
+        # Run inference
+        ort_inputs = {
+            'input': input_ids.cpu().numpy()
+        }
+        logits = ort_session.run(None, ort_inputs)[0]
+        # Get predictions for the next token
+        logits = torch.from_numpy(logits)
+        logits = logits[:, -1, :] # Only take the last token's predictions
+        # Apply temperature
+        if temperature != 1.0:
+            logits = logits / temperature
+        # Sample from the distribution
+        probs = torch.nn.functional.softmax(logits, dim=-1)
+        next_token = torch.multinomial(probs, num_samples=1)
+        # Append the new token
+        input_ids = torch.cat([input_ids, next_token], dim=1)
+    return input_ids
+# Test the generation
+prompt = "Hello"
+prompt_ids = tok(prompt, return_tensors="pt")["input_ids"]
+generated_ids = generate_with_onnx(prompt_ids)
+generated_text = tok.decode(generated_ids[0], skip_special_tokens=True)
+print(f"Generated text: {generated_text}")
+#Generated text: Hello there! How can I assist you today? I'm a helpful AI assistant trained to provide information and answer questions on a wide range of topics.
+```
+### Android Usage
+Coming Soon!
+### iOS Usage
+Coming Soon!
+## 📜 License
+📍 **CC-BY-NC-4.0**: Free for non-commercial use.
+💼 **Commercial Use**: Contact IJK Technology Ltd for licensing at [james@ijktech.com](mailto:[email protected]).
+## 🛠️ About IJK Technology Ltd
+IJK Technology Ltd (IJKTech) develops innovative machine learning models optimized for on-device inference. Our focus is on efficiency, privacy, and usability across mobile and embedded platforms.