add model card

README.md

@@ -1,142 +1,147 @@
 ---
 library_name: transformers
-tags: []
+license: apache-2.0
+datasets:
+- cerebras/SlimPajama-627B
+- HuggingFaceH4/ultrachat_200k
+- hkust-nlp/deita-10k-v0
+- Open-Orca/SlimOrca-Dedup
+- cognitivecomputations/WizardLM_evol_instruct_V2_196k_unfiltered_merged_split
+- HuggingFaceH4/capybara
+- meta-math/MetaMathQA
+- argilla/ultrafeedback-binarized-preferences-cleaned
+- Intel/orca_dpo_pairs
+- alexredna/oasst2_dpo_pairs
+pipeline_tag: text-generation
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
-
 
 ## Model Details
 
+With great enthusiasm, we unveil the Prem-1B series, open-source, multipurpose large language models developed by Prem AI. This cutting-edge SLM offers the open community and enterprises the opportunity to harness capabilities that were once exclusively available through closed model APIs, empowering them to build their own advanced language models. Our objective is to develop a model that excels at Retrieval-Augmented Generation (RAG). While Large Language Models (LLMs) store a vast amount of information within their parameters, RAG operates differently by ingesting information during runtime. This approach suggests that for RAG applications, we may not require models of immense size. With this initiative, we aim to create a Small Language Model (SLM) with an extended context length of 8192 tokens, enabling it to handle multi-turn conversations effectively. This endeavor represents our inaugural attempt to craft an SLM tailored for RAG tasks.
+
 ### Model Description
 
 <!-- Provide a longer summary of what this model is. -->
 
 This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
 
-- **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]
+- **Developed by:** https://premai.io/
+- **Model type:** Llama
+- **Language(s) (NLP):** Python
+- **License:** Apache License 2.0
 
-<!-- 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 section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
+The Prem-1B language model is designed for commercial and research applications involving the English language. The instruction-tuned versions of the model are tailored for conversational interactions akin to a virtual assistant. On the other hand, the pretrained variants can be fine-tuned and adapted for various natural language generation tasks beyond just dialogue.
 
 ### Out-of-Scope Use
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+The model must not be used in any manner that violates applicable laws or regulations, including trade compliance laws. It is also prohibited to use the model in any way that goes against the Acceptable Use Policy and the Prem-1B Community License. While the base model is intended for English language use, developers are permitted to fine-tune the Prem-1B models for other languages, provided they comply with the Prem-1B Community License and the Acceptable Use Policy.
 
 [More Information Needed]
 
-## 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. -->
-
-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. 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]
+Using `AutoModelForCausalLM` and `AutoTokenizer`
+```py
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+# Load the model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained("premai-io/prem-1B-chat")
+model = AutoModelForCausalLM.from_pretrained('premai-io/prem-1B-chat')
+model = model.to('cuda')
+
+# Setup terminators
+terminators = [tokenizer.eos_token_id, tokenizer.encode('<|eot_id|>', add_special_tokens=False)[0]]
+
+# Prepare the prompt
+messages = [
+    {
+        "role": "system",
+        "content": "You are a helpful AI assistant. You should give concise responses to very simple questions, but provide thorough responses to more complex and open-ended questions."
+    },
+    {
+        'role': 'user',
+        'content': 'Help me understand machine learning.'
+    }
+]
+
+prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+
+# Generate
+inputs = tokenizer(prompt, return_attention_mask=False, return_tensors="pt", add_special_tokens=False)
+input_ids = inputs['input_ids']
+input_ids = input_ids.to(model.device)
+res = model.generate(input_ids=input_ids, max_new_tokens=400, pad_token_id=tokenizer.pad_token_id, eos_token_id=terminators)
+generated_text = tokenizer.decode(res[0][input_ids.shape[1]:], skip_special_tokens=True).strip()
+print(generated_text)
+```
+
+Using pipelines:
+```py
+import torch
+from transformers import pipeline
+
+# Load the pipeline
+pipe = pipeline("text-generation", model="premai-io/prem-1B-chat", torch_dtype=torch.bfloat16, device=0)
+
+# Prepare prompt
+messages = [
+    {
+        "role": "system",
+        "content": "You are a helpful AI assistant. You should give concise responses to very simple questions, but provide thorough responses to more complex and open-ended questions."
+    },
+    {
+        'role': 'user',
+        'content': 'Help me understand machine learning.'
+    }
+]
+prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+
+# Setup terminators
+terminators = [pipe.tokenizer.eos_token_id, pipe.tokenizer.encode('<|eot_id|>', add_special_tokens=False)[0]]
+
+# Generate
+outputs = pipe(prompt, max_new_tokens=400, do_sample=True, temperature=0.7, top_k=50, top_p=0.95, pad_token_id=pipe.tokenizer.pad_token_id, eos_token_id=terminators)
+print(outputs[0]["generated_text"][len(prompt):])
+```
 
 ## 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]
+Mentioned in blogpost: https://blog.premai.io/p/e4168cd0-36f2-4a7f-b810-50393dd65601/
 
 ### 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]
-
+Mentioned in blogpost: https://blog.premai.io/p/e4168cd0-36f2-4a7f-b810-50393dd65601/
 
 #### 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]
+Mentioned in blogpost: https://blog.premai.io/p/e4168cd0-36f2-4a7f-b810-50393dd65601/
 
-<!-- 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                   |Avg  |Arc-c|Arc-e|Hellaswag|MMLU |Obqa |Piqa |Winogrande|
+|------------------------|-----|-----|-----|---------|-----|-----|-----|----------|
+|prem-1B                 |42.64|24.74|57.40|42.01    |24.75|21.00|72.14|56.43     |
+|prem-1B-chat            |41.76|24.48|53.32|40.28    |25.27|22.20|70.89|55.88     |
+|TinyLlama-1.1B-Chat-v1.0|46.16|30.03|61.53|46.56    |24.72|25.80|74.21|60.29     |
+|opt-1.3b                |42.94|23.37|57.44|41.49    |24.86|23.20|71.49|58.72     |
+|pythia-1b               |40.71|24.31|56.90|37.72    |23.20|18.80|70.62|53.43     |
 
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f440d8f79c1ba4c353d0f6d/PqscXKPvnwvymNxqYAxjR.png)
 
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
 
 ## Environmental Impact
 
@@ -144,56 +149,35 @@ Use the code below to get started with the model.
 
 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:** H100 GPUs
+- **Hours used:** 8500
 
-## Technical Specifications [optional]
 
 ### Model Architecture and Objective
 
-[More Information Needed]
+Llama based
 
 ### Compute Infrastructure
 
-[More Information Needed]
+16-H100 GPUs
 
 #### Hardware
 
-[More Information Needed]
+H100 GPUs
 
 #### 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]
+PyTorch, transformers, PyTorch Lightning
 
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+## Citation
 
-[More Information Needed]
-
-## More Information [optional]
+https://blog.premai.io/p/e4168cd0-36f2-4a7f-b810-50393dd65601/
 
-[More Information Needed]
 
-## Model Card Authors [optional]
+## Model Card Authors
 
-[More Information Needed]
+https://huggingface.co/goku, https://huggingface.co/nsosio, https://huggingface.co/ucalyptus, https://huggingface.co/filopedraz
 
 ## Model Card Contact
 
-[More Information Needed]
+https://huggingface.co/goku, https://huggingface.co/nsosio, https://huggingface.co/ucalyptus, https://huggingface.co/filopedraz