Added "how to use"

README.md

@@ -27,174 +27,73 @@ Asimov will be a series of language models that are trained to act as a useful w
 - **License:** [More Information Needed]
 - **Finetuned from model [optional]:** [More Information Needed]
 
-### Model Sources [optional]
-
-
-- **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
 
 Asimov-7B-v2 has not been aligned to human preferences, so the model can produce problematic outputs (especially when prompted to do so). It is also unknown what the size and composition of the corpus was used to train the base model (mistralai/Mistral-7B-v0.1), however it is likely to have included a mix of Web data and technical sources like books and code. See the Falcon 180B model card for an example of this.
 
-### 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]
-
+```python
+import torch
+from transformers import  AutoModelForCausalLM, AutoTokenizer,GenerationConfig
+from peft import PeftModel, PeftConfig
+
+model_name = "prithivida/Asimov-7B-v2"
+peft_config = PeftConfig.from_pretrained(model_name)
+
+base_model = AutoModelForCausalLM.from_pretrained(
+        peft_config.base_model_name_or_path,
+        return_dict=True,
+        device_map="auto",
+        torch_dtype=torch.float16,
+        low_cpu_mem_usage=True,
+)
+
+model = PeftModel.from_pretrained(
+        base_model,
+        model_name,
+        torch_dtype=torch.float16,
+        device_map="auto",
+)
+
+tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True)
+model.config.pad_token_id = tokenizer.unk_token_id
+
+def run_inference(messages):
+    chat = []
+    for i, message in enumerate(messages):
+      if i % 2 ==0:
+          chat.append({"role": "Human", "content": f"{message}"})
+      else:
+          chat.append({"role": "Assistant", "content": f"{message}"})
+
+
+    prompt = tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True)
+
+    inputs = tokenizer(prompt, return_tensors="pt")
+    input_ids = inputs["input_ids"].cuda()
+
+    generation_output = model.generate(
+            input_ids=input_ids,
+            generation_config=GenerationConfig(pad_token_id=tokenizer.pad_token_id,
+                                               do_sample=True,
+                                               temperature=1.0,
+                                               top_k=50,
+                                               top_p=0.95),
+            return_dict_in_generate=True,
+            output_scores=True,
+            max_new_tokens=128
+    )
+
+    for seq in generation_output.sequences:
+        output = tokenizer.decode(seq)
+        print(output.split("### Assistant: ")[1].strip())
+
+
+run_inference(["What's the longest side of the right angled triangle called and how is it related to the Pythagoras theorem?"])
+```