File size: 6,952 Bytes
c1da149 918e91f db4bad1 918e91f c1da149 918e91f c1da149 918e91f c1da149 918e91f c1da149 918e91f c1da149 918e91f c1da149 918e91f 43b047b 918e91f c1da149 918e91f c1da149 918e91f c1da149 918e91f c1da149 918e91f c1da149 918e91f c1da149 918e91f c1da149 918e91f c1da149 918e91f c1da149 918e91f c1da149 918e91f c1da149 918e91f c1da149 918e91f c1da149 918e91f c1da149 918e91f c1da149 62d464a |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 |
---
license: apache-2.0
library_name: transformers
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 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:** https://premai.io/
- **Model type:** Llama
- **Language(s) (NLP):** Python
- **License:** Apache License 2.0
## Uses
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
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.
### 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
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', torch_dtype=torch.bfloat16)
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
Mentioned in blogpost: https://blog.premai.io/p/e4168cd0-36f2-4a7f-b810-50393dd65601/
### Training Procedure
Mentioned in blogpost: https://blog.premai.io/p/e4168cd0-36f2-4a7f-b810-50393dd65601/
#### Training Hyperparameters
Mentioned in blogpost: https://blog.premai.io/p/e4168cd0-36f2-4a7f-b810-50393dd65601/
## Evaluation
### Results
|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 |
## Environmental Impact
- **Hardware Type:** H100 GPUs
- **Hours used:** 8500
### Model Architecture and Objective
Llama based
### Compute Infrastructure
16-H100 GPUs
#### Hardware
H100 GPUs
#### Software
PyTorch, transformers, PyTorch Lightning
## Citation
https://blog.premai.io/p/e4168cd0-36f2-4a7f-b810-50393dd65601/
## Model Card Authors
https://huggingface.co/goku, https://huggingface.co/nsosio, https://huggingface.co/ucalyptus, https://huggingface.co/filopedraz
## Model Card Contact
https://huggingface.co/goku, https://huggingface.co/nsosio, https://huggingface.co/ucalyptus, https://huggingface.co/filopedraz |