

        
# LoRA Adapter for LLaMA 7B trained on more datasets than tloen/alpaca-lora-7b

This repo contains a low-rank adapter for **LLaMA-7b** fit on datasets part of the OpenAssistant project.


You can see sampling results [here](https://open-assistant.github.io/oasst-model-eval/?f=https%3A%2F%2Fraw.githubusercontent.com%2FOpen-Assistant%2Foasst-model-eval%2Fmain%2Fsampling_reports%2Foasst-sft%2F2023-03-18_llama_30b_oasst_latcyr_400_sampling_noprefix_lottery.json%0Ahttps%3A%2F%2Fraw.githubusercontent.com%2FOpen-Assistant%2Foasst-model-eval%2F8e90ce6504c159d4046991bf37757c108aed913f%2Fsampling_reports%2Foasst-sft%2Freport_file_jordiclive_alpaca_gpt4-dolly_15k-vicuna-lora-7b_full_lottery_no_prefix.json). Note the sampling params are not necessarily the optimum—they are OpenAssistant defaults for comparing models.

This version of the weights was trained with the following hyperparameters:

- Epochs: 8
- Batch size: 128
- Max Length: 2048
- Learning rate: 8e-6
- Lora _r_: 16
- Lora Alpha: 32
- Lora target modules: q_proj, k_proj, v_proj, o_proj

The model was trained with flash attention and gradient checkpointing.

## Dataset Details
  - dolly15k:
      val_split: 0.05
      max_val_set: 300
  - oasst_export:
      lang: "bg,ca,cs,da,de,en,es,fr,hr,hu,it,nl,pl,pt,ro,ru,sl,sr,sv,uk"
      input_file_path: 2023-04-12_oasst_release_ready_synth.jsonl.gz
      val_split: 0.05
  - vicuna:
      val_split: 0.05
      max_val_set: 800
      fraction: 0.8
  - dolly15k:
      val_split: 0.05
      max_val_set: 300
  - grade_school_math_instructions:
      val_split: 0.05
  - code_alpaca:
      val_split: 0.05
      max_val_set: 250
  - alpaca_gpt4:
      val_split: 0.02
      max_val_set: 250

## Model Details

- **Developed** as part of the OpenAssistant Project
- **Model type:** PEFT Adapter for frozen LLaMA
- **Language:** English

## Prompting

Two special tokens are used to mark the beginning of user and assistant turns:
`<|prompter|>` and `<|assistant|>`. Each turn ends with a `<|endoftext|>` token.

Input prompt example:
```
<|prompter|>What is a meme, and what's the history behind this word?</s><|assistant|>
```
The input ends with the `<|assistant|>` token to signal that the model should 
start generating the assistant reply.


# Example Inference Code (Note several embeddings need to be loaded along with the LoRA weights), assumes on GPU and torch.float16: 

```
from typing import List, NamedTuple

import torch
import transformers
from huggingface_hub import hf_hub_download
from peft import PeftModel
from transformers import GenerationConfig

device = "cuda" if torch.cuda.is_available() else "cpu"
tokenizer = transformers.AutoTokenizer.from_pretrained("jordiclive/alpaca_gpt4-dolly_15k-vicuna-lora-7b")


model = transformers.AutoModelForCausalLM.from_pretrained(
    "decapoda-research/llama-7b-hf", torch_dtype=torch.float16
)  # Load Base Model
model.resize_token_embeddings(
    len(tokenizer)
)  # This model repo also contains several embeddings for special tokens that need to be loaded.

model.config.eos_token_id = tokenizer.eos_token_id
model.config.bos_token_id = tokenizer.bos_token_id
model.config.pad_token_id = tokenizer.pad_token_id

lora_weights = "jordiclive/alpaca_gpt4-dolly_15k-vicuna-lora-7b"
model = PeftModel.from_pretrained(
    model,
    lora_weights,
    torch_dtype=torch.float16,
)  # Load Lora model

model.eos_token_id = tokenizer.eos_token_id
filename = hf_hub_download("jordiclive/alpaca_gpt4-dolly_15k-vicuna-lora-7b", "extra_embeddings.pt")
embed_weights = torch.load(
    filename, map_location=torch.device("cuda" if torch.cuda.is_available() else "cpu")
)  # Load embeddings for special tokens
model.base_model.model.model.embed_tokens.weight[32000:, :] = embed_weights.to(
    model.base_model.model.model.embed_tokens.weight.dtype
).to(
    device
)  # Add special token embeddings


model = model.half().to(device)
generation_config = GenerationConfig(
    temperature=0.1,
    top_p=0.75,
    top_k=40,
    num_beams=4,
)


def format_system_prompt(prompt, eos_token="</s>"):
    return "{}{}{}{}".format(
        "<|prompter|>",
        prompt,
        eos_token,
        "<|assistant|>"
    )

def generate(prompt, generation_config=generation_config, max_new_tokens=2048, device=device):
    prompt = format_system_prompt(prompt)  # OpenAssistant Prompt Format expected
    input_ids = tokenizer(prompt, return_tensors="pt").input_ids.to(device)
    with torch.no_grad():
        generation_output = model.generate(
            input_ids=input_ids,
            generation_config=generation_config,
            return_dict_in_generate=True,
            output_scores=True,
            max_new_tokens=max_new_tokens,
            eos_token_id=2,
        )
    s = generation_output.sequences[0]
    output = tokenizer.decode(s)
    print("Text generated:")
    print(output)
    return output


generate("What is a meme, and what's the history behind this word?")
generate("What's the Earth total population")
generate("Write a story about future of AI development")
```
{"language": ["en"], "license": "mit", "tags": ["sft"], "datasets": ["sahil2801/CodeAlpaca-20k", "yahma/alpaca-cleaned", "databricks/databricks-dolly-15k", "OpenAssistant/oasst1", "jeffwan/sharegpt_vicuna", "qwedsacf/grade-school-math-instructions", "vicgalle/alpaca-gpt4"], "pipeline_tag": "text-generation", "widget": [{"text": "<|prompter|>What is a meme, and what's the history behind this word?</s><|assistant|>"}, {"text": "<|prompter|>What's the Earth total population</s><|assistant|>"}, {"text": "<|prompter|>Write a story about future of AI development</s><|assistant|>"}]}
sft text-generation en dataset:sahil2801/CodeAlpaca-20k dataset:yahma/alpaca-cleaned dataset:databricks/databricks-dolly-15k dataset:OpenAssistant/oasst1 dataset:jeffwan/sharegpt_vicuna dataset:qwedsacf/grade-school-math-instructions dataset:vicgalle/alpaca-gpt4 license:mit region:us
TAGS
#sft #text-generation #en #dataset-sahil2801/CodeAlpaca-20k #dataset-yahma/alpaca-cleaned #dataset-databricks/databricks-dolly-15k #dataset-OpenAssistant/oasst1 #dataset-jeffwan/sharegpt_vicuna #dataset-qwedsacf/grade-school-math-instructions #dataset-vicgalle/alpaca-gpt4 #license-mit #region-us 



        
# LoRA Adapter for LLaMA 7B trained on more datasets than tloen/alpaca-lora-7b

This repo contains a low-rank adapter for LLaMA-7b fit on datasets part of the OpenAssistant project.


You can see sampling results here. Note the sampling params are not necessarily the optimum—they are OpenAssistant defaults for comparing models.

This version of the weights was trained with the following hyperparameters:

- Epochs: 8
- Batch size: 128
- Max Length: 2048
- Learning rate: 8e-6
- Lora _r_: 16
- Lora Alpha: 32
- Lora target modules: q_proj, k_proj, v_proj, o_proj

The model was trained with flash attention and gradient checkpointing.

## Dataset Details
  - dolly15k:
      val_split: 0.05
      max_val_set: 300
  - oasst_export:
      lang: "bg,ca,cs,da,de,en,es,fr,hr,hu,it,nl,pl,pt,ro,ru,sl,sr,sv,uk"
      input_file_path: 2023-04-12_oasst_release_ready_synth.URL
      val_split: 0.05
  - vicuna:
      val_split: 0.05
      max_val_set: 800
      fraction: 0.8
  - dolly15k:
      val_split: 0.05
      max_val_set: 300
  - grade_school_math_instructions:
      val_split: 0.05
  - code_alpaca:
      val_split: 0.05
      max_val_set: 250
  - alpaca_gpt4:
      val_split: 0.02
      max_val_set: 250

## Model Details

- Developed as part of the OpenAssistant Project
- Model type: PEFT Adapter for frozen LLaMA
- Language: English

## Prompting

Two special tokens are used to mark the beginning of user and assistant turns:
'<|prompter|>' and '<|assistant|>'. Each turn ends with a '<|endoftext|>' token.

Input prompt example:

The input ends with the '<|assistant|>' token to signal that the model should 
start generating the assistant reply.


# Example Inference Code (Note several embeddings need to be loaded along with the LoRA weights), assumes on GPU and torch.float16: 


# LoRA Adapter for LLaMA 7B trained on more datasets than tloen/alpaca-lora-7b

This repo contains a low-rank adapter for LLaMA-7b fit on datasets part of the OpenAssistant project.


You can see sampling results here. Note the sampling params are not necessarily the optimum—they are OpenAssistant defaults for comparing models.

This version of the weights was trained with the following hyperparameters:

- Epochs: 8
- Batch size: 128
- Max Length: 2048
- Learning rate: 8e-6
- Lora _r_: 16
- Lora Alpha: 32
- Lora target modules: q_proj, k_proj, v_proj, o_proj

The model was trained with flash attention and gradient checkpointing. ## Dataset Details
  - dolly15k:
      val_split: 0.05
      max_val_set: 300
  - oasst_export:
      lang: "bg,ca,cs,da,de,en,es,fr,hr,hu,it,nl,pl,pt,ro,ru,sl,sr,sv,uk"
      input_file_path: 2023-04-12_oasst_release_ready_synth.URL
      val_split: 0.05
  - vicuna:
      val_split: 0.05
      max_val_set: 800
      fraction: 0.8
  - dolly15k:
      val_split: 0.05
      max_val_set: 300
  - grade_school_math_instructions:
      val_split: 0.05
  - code_alpaca:
      val_split: 0.05
      max_val_set: 250
  - alpaca_gpt4:
      val_split: 0.02
      max_val_set: 250 ## Model Details

- Developed as part of the OpenAssistant Project
- Model type: PEFT Adapter for frozen LLaMA
- Language: English ## Prompting

Two special tokens are used to mark the beginning of user and assistant turns:
'<|prompter|>' and '<|assistant|>'. Each turn ends with a '<|endoftext|>' token.

Input prompt example:

The input ends with the '<|assistant|>' token to signal that the model should 
start generating the assistant reply. # Example Inference Code (Note several embeddings need to be loaded along with the LoRA weights), assumes on GPU and torch.float16:
TAGS
#sft #text-generation #en #dataset-sahil2801/CodeAlpaca-20k #dataset-yahma/alpaca-cleaned #dataset-databricks/databricks-dolly-15k #dataset-OpenAssistant/oasst1 #dataset-jeffwan/sharegpt_vicuna #dataset-qwedsacf/grade-school-math-instructions #dataset-vicgalle/alpaca-gpt4 #license-mit #region-us 
 # LoRA Adapter for LLaMA 7B trained on more datasets than tloen/alpaca-lora-7b

This repo contains a low-rank adapter for LLaMA-7b fit on datasets part of the OpenAssistant project.


You can see sampling results here. Note the sampling params are not necessarily the optimum—they are OpenAssistant defaults for comparing models.

This version of the weights was trained with the following hyperparameters:

- Epochs: 8
- Batch size: 128
- Max Length: 2048
- Learning rate: 8e-6
- Lora _r_: 16
- Lora Alpha: 32
- Lora target modules: q_proj, k_proj, v_proj, o_proj

The model was trained with flash attention and gradient checkpointing. ## Dataset Details
  - dolly15k:
      val_split: 0.05
      max_val_set: 300
  - oasst_export:
      lang: "bg,ca,cs,da,de,en,es,fr,hr,hu,it,nl,pl,pt,ro,ru,sl,sr,sv,uk"
      input_file_path: 2023-04-12_oasst_release_ready_synth.URL
      val_split: 0.05
  - vicuna:
      val_split: 0.05
      max_val_set: 800
      fraction: 0.8
  - dolly15k:
      val_split: 0.05
      max_val_set: 300
  - grade_school_math_instructions:
      val_split: 0.05
  - code_alpaca:
      val_split: 0.05
      max_val_set: 250
  - alpaca_gpt4:
      val_split: 0.02
      max_val_set: 250 ## Model Details

- Developed as part of the OpenAssistant Project
- Model type: PEFT Adapter for frozen LLaMA
- Language: English ## Prompting

Two special tokens are used to mark the beginning of user and assistant turns:
'<|prompter|>' and '<|assistant|>'. Each turn ends with a '<|endoftext|>' token.

Input prompt example:

The input ends with the '<|assistant|>' token to signal that the model should 
start generating the assistant reply. # Example Inference Code (Note several embeddings need to be loaded along with the LoRA weights), assumes on GPU and torch.float16:
TAGS
#sft #text-generation #en #dataset-sahil2801/CodeAlpaca-20k #dataset-yahma/alpaca-cleaned #dataset-databricks/databricks-dolly-15k #dataset-OpenAssistant/oasst1 #dataset-jeffwan/sharegpt_vicuna #dataset-qwedsacf/grade-school-math-instructions #dataset-vicgalle/alpaca-gpt4 #license-mit #region-us 
# LoRA Adapter for LLaMA 7B trained on more datasets than tloen/alpaca-lora-7b

This repo contains a low-rank adapter for LLaMA-7b fit on datasets part of the OpenAssistant project.


You can see sampling results here. Note the sampling params are not necessarily the optimum—they are OpenAssistant defaults for comparing models.

This version of the weights was trained with the following hyperparameters:

- Epochs: 8
- Batch size: 128
- Max Length: 2048
- Learning rate: 8e-6
- Lora _r_: 16
- Lora Alpha: 32
- Lora target modules: q_proj, k_proj, v_proj, o_proj

The model was trained with flash attention and gradient checkpointing.## Dataset Details
  - dolly15k:
      val_split: 0.05
      max_val_set: 300
  - oasst_export:
      lang: "bg,ca,cs,da,de,en,es,fr,hr,hu,it,nl,pl,pt,ro,ru,sl,sr,sv,uk"
      input_file_path: 2023-04-12_oasst_release_ready_synth.URL
      val_split: 0.05
  - vicuna:
      val_split: 0.05
      max_val_set: 800
      fraction: 0.8
  - dolly15k:
      val_split: 0.05
      max_val_set: 300
  - grade_school_math_instructions:
      val_split: 0.05
  - code_alpaca:
      val_split: 0.05
      max_val_set: 250
  - alpaca_gpt4:
      val_split: 0.02
      max_val_set: 250## Model Details

- Developed as part of the OpenAssistant Project
- Model type: PEFT Adapter for frozen LLaMA
- Language: English## Prompting

Two special tokens are used to mark the beginning of user and assistant turns:
'<|prompter|>' and '<|assistant|>'. Each turn ends with a '<|endoftext|>' token.

Input prompt example:

The input ends with the '<|assistant|>' token to signal that the model should 
start generating the assistant reply.# Example Inference Code (Note several embeddings need to be loaded along with the LoRA weights), assumes on GPU and torch.float16: