Update README.md

README.md

@@ -1,16 +1,16 @@
 ---
-base_model: unsloth/qwen2.5-math-1.5b-bnb-4bit
+base_model: unsloth/qwen2.5-math-1.5b
 library_name: peft
 pipeline_tag: text-generation
 tags:
-- base_model:adapter:unsloth/qwen2.5-math-1.5b-bnb-4bit
-- lora
-- sft
-- transformers
-- trl
-- unsloth
+  - base_model:unsloth/qwen2.5-math-1.5b
+  - lora
+  - sft
+  - transformers
+  - trl
+  - unsloth
 license: apache-2.0
-title: TAV
+title: TAV (CPU Version)
 sdk: gradio
 emoji: 👀
 colorFrom: green
@@ -19,200 +19,60 @@ sdk_version: 5.49.1
 hf_oauth: true
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
-
+# Model Card for TAV CPU Version
 
 ## Model Details
 
 ### Model Description
+This is the TAV model (CPU compatible) for text-generation tasks.  
+It is based on `unsloth/qwen2.5-math-1.5b` and uses PEFT adapters for fine-tuning.  
+Optimized to run on CPU environments without 4-bit quantization or bitsandbytes dependencies.
 
-<!-- Provide a longer summary of what this model is. -->
-
-
+- **Developed by:** [Your Name / Organization]
+- **Shared by:** [Your Name / Organization]
+- **Model type:** Causal Language Model (Text Generation)
+- **Language(s):** English (with math/technical capability)
+- **License:** Apache-2.0
+- **Finetuned from model:** unsloth/qwen2.5-math-1.5b
 
-- **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]
+### Model Sources
+- **Repository:** [Hugging Face Model Link]
+- **Demo:** [Hugging Face Space Link]
 
 ## 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
+- Generate math/technical answers in English.
+- Use as a chatbot for educational purposes.
+- Integrate into CPU-only environments.
 
-<!-- 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]
+### Downstream Use
+- Can be further fine-tuned for domain-specific tasks.
+- Suitable for research or teaching applications.
 
 ### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
+- Not optimized for GPU-heavy inference or extremely long sequences (>1024 tokens).
+- Not suitable for real-time production under heavy load.
 
 ## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
+- May produce biased or incorrect answers.
+- CPU inference is slower than GPU inference.
+- Limited context window due to CPU memory constraints.
 
 ### Recommendations
+- Use with moderate token limits to avoid long processing times.
+- Not intended for high-throughput production environments.
 
-<!-- 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]
+## How to Get Started
+Use the CPU-compatible pipeline in Python:
 
-[More Information Needed]
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
 
-## Model Card Contact
+tokenizer = AutoTokenizer.from_pretrained("unsloth/qwen2.5-math-1.5b")
+model = AutoModelForCausalLM.from_pretrained("unsloth/qwen2.5-math-1.5b", device_map="cpu")
 
-[More Information Needed]
-### Framework versions
+generator = pipeline("text-generation", model=model, tokenizer=tokenizer, device=-1)
 
-- PEFT 0.17.1
+output = generator("Hi, how are you?", max_new_tokens=128, do_sample=True)
+print(output[0]["generated_text"])