Update README.md

README.md

@@ -1,202 +1,120 @@
 ---
 base_model: unsloth/Qwen3-0.6B
 library_name: peft
+license: mit
+datasets:
+- unsloth/OpenMathReasoning-mini
+- mlabonne/FineTome-100k
+language:
+- en
+pipeline_tag: question-answering
+tags:
+- Math
 ---
 
 # 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 model is fine-tuned version of Qwen/Qwen3-0.6B using the Unsloth library and LoRA for parameter-efficient training.
+This model is trained on two datasets:
+- unsloth/OpenMathReason-mini — for enhancing mathematical reasoning skills.
+- mlabonne/FineTome-100k — to improve general conversational abilities.
 
 
 
-- **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]
+- **Developed by:** Rustam Shiriyev
+- **Language(s) (NLP):** English
 - **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-
-### Model Sources [optional]
+- **Finetuned from model:** unsloth/Qwen3-0.6B 
 
-<!-- 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 model can be used as a lightweight assistant capable of solving basic to intermediate math problems (OpenMathReason tasks).
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+### Downstream Use
 
-[More Information Needed]
+- Can be integrated into educational chatbots for STEM learning.
 
 ### Out-of-Scope Use
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
+- Not suitable for high-stakes decision-making.
 
 ## 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. -->
+- Mathematical reasoning is limited to the scope of the OpenMathReason-mini dataset.
+- Conversational quality may degrade with complex or multi-turn inputs.
 
-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]
+```python 
+from transformers import TextStreamer
+from huggingface_hub import login
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from peft import PeftModel
 
 
-#### Training Hyperparameters
+login(token="")  
 
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+tokenizer = AutoTokenizer.from_pretrained("unsloth/Qwen3-0.6B",)
+base_model = AutoModelForCausalLM.from_pretrained(
+    "unsloth/Qwen3-0.6B",
+    device_map="auto", token=""
+)
 
-#### Speeds, Sizes, Times [optional]
+model = PeftModel.from_pretrained(base_model,"Rustamshry/Qwen3-0.6B-OpenMathReason")
 
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+question = "Solve (x + 2)^2 = 0"
 
-[More Information Needed]
+messages = [
+    {"role" : "user", "content" : question}
+]
 
-## Evaluation
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize = False,
+    add_generation_prompt = True, 
+    enable_thinking = True,
+)
 
-<!-- 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]
+_ = model.generate(
+    **tokenizer(text, return_tensors = "pt").to(model.device),
+    max_new_tokens = 2048,
+    temperature = 0.6, top_p = 0.95, top_k = 20,
+    streamer = TextStreamer(tokenizer, skip_prompt = True),
+)
+```
+## Training Details
 
-#### Factors
+### Training Data
 
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+- unsloth/OpenMathReason-mini: 10k+ instruction-following examples focused on math.
+- mlabonne/FineTome-100k: 100k examples of diverse, high-quality chat data.
 
-[More Information Needed]
+### Training Procedure
 
-#### Metrics
+- batch size=8,
+- gradient accumulation steps=2,
+- optimizer=adamw_torch,
+- learning rate=2e-5,
+- warmup steps=100,
+- fp16=True,
+- dataloader_num_workers=16,
+- num_train_epochs=1,
+- weight_decay=0.01,
+- lr_scheduler_type = "linear"
 
-<!-- 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
+- Loss Value > 0.56
 
-[More Information Needed]
 ### Framework versions
 
 - PEFT 0.14.0