Update README.md

README.md

@@ -3,205 +3,153 @@ base_model: mistralai/Mistral-7B-Instruct-v0.3
 library_name: peft
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
-
+# Model Card for MistralF
 
 ## Model Details
 
 ### Model Description
 
-<!-- Provide a longer summary of what this model is. -->
-
-
+This model is an adapter fine-tuned on top of `mistralai/Mistral-7B-Instruct-v0.3`, a 7-billion-parameter instruction-following language model developed by Mistral AI. The adapter was fine-tuned using the PEFT (Parameter-Efficient Fine-Tuning) library to adapt the base model for a specific task while keeping the original weights frozen. The fine-tuning task and dataset details are not specified, but this adapter can be used for natural language generation tasks such as text completion, instruction following, or dialogue generation.
 
-- **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]
+- **Developed by:** Danna8
+- **Funded by [optional]:** Not applicable
+- **Shared by [optional]:** Danna8
+- **Model type:** Adapter for a Causal Language Model (Mistral-7B-Instruct-v0.3)
+- **Language(s) (NLP):** English (assumed; adjust if different)
+- **License:** Apache 2.0 (same as the base model; adjust if you prefer a different license for the adapter)
+- **Finetuned from model [optional]:** mistralai/Mistral-7B-Instruct-v0.3
 
-### Model Sources [optional]
+### Model Sources
 
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- **Repository:** https://huggingface.co/Danna8/MistralF
+- **Paper [optional]:** Not applicable
+- **Demo [optional]:** Not available
 
 ## 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]
+This model can be used for natural language generation tasks, such as generating responses to instructions, completing text, or engaging in dialogue. It is intended for users who want to leverage the capabilities of `mistralai/Mistral-7B-Instruct-v0.3` with additional fine-tuning for a specific use case.
 
 ### 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]
+The model can be further fine-tuned or integrated into larger applications, such as chatbots, virtual assistants, or content generation tools.
 
 ### Out-of-Scope Use
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
+This model should not be used for generating harmful, biased, or misleading content. It may not perform well on tasks outside its fine-tuning domain or on languages other than English (if fine-tuned on English data).
 
 ## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
+As a fine-tuned version of `mistralai/Mistral-7B-Instruct-v0.3`, this model inherits the biases and limitations of the base model, including potential biases in the training data (e.g., Wikipedia, web crawls). The fine-tuning process may introduce additional biases depending on the dataset used. The model may generate incorrect or inappropriate responses, especially if the fine-tuning task was narrow or the input is out of scope.
 
 ### 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.
+Users should evaluate the model’s outputs for accuracy and appropriateness, especially in sensitive applications. Consider implementing post-processing or filtering to mitigate risks of harmful or biased content.
 
 ## How to Get Started with the Model
 
-Use the code below to get started with the model.
-
-[More Information Needed]
+See the usage section below for a code example to load and use the model.
 
 ## 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]
+The specific dataset used for fine-tuning is not specified. Users are encouraged to contact the model developer (Danna8) for more details about the fine-tuning data.
 
 ### 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]
-
+Not specified. Assumed to follow the standard preprocessing for `mistralai/Mistral-7B-Instruct-v0.3`, including tokenization with the provided tokenizer files.
 
 #### 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 -->
+- **Training regime:** fp16 mixed precision (assumed; adjust if different)
 
 #### Speeds, Sizes, Times [optional]
 
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
+Not specified.
 
 ## 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]
+Not specified.
 
 #### Factors
 
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
+Not specified.
 
 #### Metrics
 
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
+Not specified.
 
 ### Results
 
-[More Information Needed]
-
-#### Summary
-
-
+Not specified.
 
 ## Model Examination [optional]
 
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
+Not specified.
 
 ## 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]
+- **Hardware Type:** Not specified (e.g., NVIDIA A100 GPU; adjust if known)
+- **Hours used:** Not specified
+- **Cloud Provider:** Not specified
+- **Compute Region:** Not specified
+- **Carbon Emitted:** Not specified
 
 ## Technical Specifications [optional]
 
 ### Model Architecture and Objective
 
-[More Information Needed]
+The base model (`mistralai/Mistral-7B-Instruct-v0.3`) is a transformer-based causal language model with 7 billion parameters, optimized for instruction-following tasks. The adapter adds a small set of trainable parameters to adapt the model for a specific task, using the PEFT library.
 
 ### Compute Infrastructure
 
-[More Information Needed]
+Not specified.
 
 #### Hardware
 
-[More Information Needed]
+Not specified.
 
 #### Software
 
-[More Information Needed]
+- Transformers library (Hugging Face)
+- PEFT 0.14.0
 
 ## 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]
+Not applicable.
 
 ## Glossary [optional]
 
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
+- **PEFT**: Parameter-Efficient Fine-Tuning, a method to fine-tune large language models by training only a small set of additional parameters (adapters) while keeping the base model frozen.
 
 ## More Information [optional]
 
-[More Information Needed]
+Contact the model developer (Danna8) for more details.
 
 ## Model Card Authors [optional]
 
-[More Information Needed]
+Danna8
 
 ## Model Card Contact
 
-[More Information Needed]
-### Framework versions
+Contact Danna8 via the Hugging Face Hub.
+
+### Framework Versions
 
 - PEFT 0.14.0
+- Transformers (version not specified; recommended to use the latest version)
 
-- ## Usage
+## Usage
 
 This adapter is fine-tuned on top of `mistralai/Mistral-7B-Instruct-v0.3`. To use it:
 
@@ -215,11 +163,22 @@ adapter_model_name = "Danna8/MistralF"
 tokenizer = AutoTokenizer.from_pretrained(adapter_model_name)
 
 # Load the base model and apply the adapter
-model = AutoModelForCausalLM.from_pretrained(base_model_name)
+model = AutoModelForCausalLM.from_pretrained(
+    base_model_name,
+    torch_dtype=torch.float16,  # Use FP16 for efficiency
+    device_map="auto"  # Automatically map to GPU if available
+)
 model.load_adapter(adapter_model_name)
 model.set_active_adapters("default")  # Adjust the adapter name if needed
 
 # Example inference
-inputs = tokenizer("Hello, how are you?", return_tensors="pt")
-outputs = model.generate(**inputs)
-print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+inputs = tokenizer("Hello, how are you?", return_tensors="pt").to("cuda")
+outputs = model.generate(
+    **inputs,
+    max_new_tokens=50,
+    do_sample=True,
+    top_p=0.95,
+    temperature=0.7
+)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+