Update README.md

initial description details

README.md

@@ -1,13 +1,7 @@
 ---
 library_name: peft
-base_model: unsloth/llama-3-8b-bnb-4bit
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
-
 
 ## Model Details
 
@@ -15,49 +9,33 @@ base_model: unsloth/llama-3-8b-bnb-4bit
 
 <!-- Provide a longer summary of what this model is. -->
 
+Mongolian-Llama3 is an instruction-tuned language model for Mongolian & English users with various abilities such as roleplaying & tool-using built upon the quantized Meta-Llama-3-8B model.
 
-
-- **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. -->
+Developed by: Dorjzodovsuren
+License: Llama-3 License
+Base Model: llama-3-8b-bnb-4bit
+Model Size: 8.03B
+Context length: 8K
 
 [More Information Needed]
 
-### Downstream Use [optional]
+## Bias, Risks, and Limitations
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+To combat fake news, current strategies rely heavily on synthetic and translated data. However, these approaches have inherent biases, risks, and limitations:
 
-[More Information Needed]
+1. **Synthetic Data Bias**: Algorithms may inadvertently perpetuate biases present in training data.
 
-### Out-of-Scope Use
+2. **Translation Inaccuracy**: Translations can distort meaning or lose context, leading to misinformation.
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+3. **Cultural Nuances**: Synthetic and translated data may miss cultural intricacies, risking amplification of stereotypes.
 
-[More Information Needed]
+4. **Algorithmic Limits**: Effectiveness is constrained by algorithm capabilities and training data quality.
 
-## Bias, Risks, and Limitations
+5. **Dependency on Data**: Accuracy hinges on quality and representativeness of training data.
+
+6. **Adversarial Attacks**: Malicious actors can exploit vulnerabilities to manipulate content.
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+7. **Different answer based on language**: Answer might be a bit different based on language.
 
 [More Information Needed]
 
@@ -65,138 +43,93 @@ base_model: unsloth/llama-3-8b-bnb-4bit
 
 <!-- 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.
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model.
+Due to hallucinations and pretraining datasets characteristics, some information might be misleading, and answer might be a bit different based on language. Please ask in Mongolian if possible.
 
 ## 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]
-### Framework versions
-
-- PEFT 0.10.0
+```
+import torch
+import gradio as gr
+from threading import Thread
+from peft import PeftModel, PeftConfig
+from unsloth import FastLanguageModel
+from transformers import TextStreamer
+from transformers import AutoModelForCausalLM, AutoTokenizer, StoppingCriteria, StoppingCriteriaList, TextIteratorStreamer
+
+config = PeftConfig.from_pretrained("Dorjzodovsuren/Mongolian_llama3")
+model = AutoModelForCausalLM.from_pretrained("unsloth/llama-3-8b-bnb-4bit", torch_dtype = torch.float16)
+model = PeftModel.from_pretrained(model, "Dorjzodovsuren/Mongolian_llama3")
+
+#load tokenizer
+tokenizer = AutoTokenizer.from_pretrained("Dorjzodovsuren/Mn_llama3")
+
+alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
+
+### Instruction:
+{}
+
+### Input:
+{}
+
+### Response:
+{}"""
+
+
+# Enable native 2x faster inference
+FastLanguageModel.for_inference(model)
+
+# Create a text streamer
+text_streamer = TextStreamer(tokenizer, skip_prompt=False,skip_special_tokens=True)
+
+# Get the device based on GPU availability
+device = 'cuda' if torch.cuda.is_available() else 'cpu'
+
+# Move model into device
+model = model.to(device)
+
+class StopOnTokens(StoppingCriteria):
+    def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
+        stop_ids = [29, 0]
+        for stop_id in stop_ids:
+            if input_ids[0][-1] == stop_id:
+                return True
+        return False
+
+# Current implementation does not support conversation based on previous conversation. 
+# Highly recommend to experiment on various hyper parameters to compare qualities.
+def predict(message, history):
+    stop = StopOnTokens()
+    messages = alpaca_prompt.format(
+        message,
+        "",
+        "",
+    )
+
+    model_inputs = tokenizer([messages], return_tensors="pt").to(device)
+
+    streamer = TextIteratorStreamer(tokenizer, timeout=10., skip_prompt=True, skip_special_tokens=True)
+    generate_kwargs = dict(
+        model_inputs,
+        streamer=streamer,
+        max_new_tokens=1024,
+        top_p=0.95,
+        temperature=0.001,
+        repetition_penalty=1.1,
+        stopping_criteria=StoppingCriteriaList([stop])
+        )
+    t = Thread(target=model.generate, kwargs=generate_kwargs)
+    t.start()
+
+    partial_message = ""
+    for new_token in streamer:
+        if new_token != '<':
+            partial_message += new_token
+            yield partial_message
+
+gr.ChatInterface(predict).launch(debug=True, share=True, show_api=True)
+```
+
+https://colab.research.google.com/drive/1LC0xx4i9xqFmwn9l8T6vw25RIr-BP0Tq?usp=sharing