---
license: apache-2.0
language:
- en
dataset:
- chargoddard/Open-Platypus-Chat
tags:
- axolotl
base_model: ai21labs/Jamba-v0.1
---
# Jambatypus-v0.1
This model is a QLoRA fine-tuned version of [ai21labs/Jamba-v0.1](https://huggingface.co/ai21labs/Jamba-v0.1) on the [chargoddard/Open-Platypus-Chat](https://huggingface.co/datasets/chargoddard/Open-Platypus-Chat) dataset.
It has been trained on 2xA100 80 GB using my [LazyAxolotl - Jamba](https://colab.research.google.com/drive/1alsgwZFvLPPAwIgkAxeMKHQSJYfW7DeZ?usp=sharing) notebook.
This repo contains both the adapter and the merged model in FP16 precision.
I recommend using the ChatML template to use this model.
[
](https://github.com/OpenAccess-AI-Collective/axolotl)
See axolotl config
axolotl version: `0.4.0`
```yaml
base_model: ai21labs/Jamba-v0.1
trust_remote_code: true
load_in_8bit: false
load_in_4bit: true
strict: false
datasets:
- path: chargoddard/Open-Platypus-Chat
type: sharegpt
chat_template: chatml
dataset_prepared_path:
val_set_size: 0.01
output_dir: ./out
sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: false
use_wandb: true
wandb_project: axolotl
wandb_entity:
wandb_watch:
wandb_name: Jambatypus-v0.1
wandb_log_model:
adapter: qlora
lora_r: 16
lora_alpha: 32
lora_dropout: 0.05
lora_target_linear: true
low_cpu_mem_usage: true
gradient_accumulation_steps: 8
micro_batch_size: 1
num_epochs: 1
optimizer: adamw_bnb_8bit
adam_beta2: 0.95
adam_epsilon: 0.00001
max_grad_norm: 1.0
lr_scheduler: cosine
learning_rate: 0.0002
train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false
gradient_checkpointing: true
gradient_checkpointing_kwargs:
use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
warmup_steps: 10
evals_per_epoch: 4
saves_per_epoch: 4
save_total_limit: 2
debug:
deepspeed:
weight_decay: 0.0
special_tokens:
```
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 8
- total_train_batch_size: 16
- total_eval_batch_size: 2
- optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-05
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.6274 | 0.01 | 1 | 1.0298 |
| 0.44 | 0.25 | 42 | 0.9770 |
| 0.4406 | 0.5 | 84 | 0.9653 |
| 0.4445 | 0.75 | 126 | 0.9645 |
| 0.4609 | 1.0 | 168 | 0.9641 |
### Framework versions
- PEFT 0.10.0
- Transformers 4.40.0.dev0
- Pytorch 2.1.2+cu118
- Datasets 2.18.0
- Tokenizers 0.15.0
## 💻 Usage
The following code creates a Gradio chat interface with Jambatypus.
```python
!pip install -qqq -U git+https://github.com/huggingface/transformers
!pip install -qqq mamba-ssm causal-conv1d>=1.2.0
!pip install -qqq accelerate bitsandbytes torch datasets peft gradio
!pip install -qqq flash-attn --no-build-isolation
import torch
import gradio as gr
from threading import Thread
from peft import PeftModel, PeftConfig
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, TextIteratorStreamer
STOP_TOKEN = "<|im_end|>"
def predict(message, history, system_prompt, temperature, max_new_tokens, top_k, repetition_penalty, top_p):
# Format history with a given chat template
stop_token = "<|im_end|>"
instruction = '<|im_start|>system\n' + system_prompt + '\n<|im_end|>\n'
for human, assistant in history:
instruction += '<|im_start|>user\n' + human + '\n<|im_end|>\n<|im_start|>assistant\n' + assistant
instruction += '\n<|im_start|>user\n' + message + '\n<|im_end|>\n<|im_start|>assistant\n'
streamer = TextIteratorStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
enc = tokenizer([instruction], return_tensors="pt", padding=True, truncation=True)
input_ids, attention_mask = enc.input_ids, enc.attention_mask
generate_kwargs = dict(
{"input_ids": input_ids.to(device), "attention_mask": attention_mask.to(device)},
streamer=streamer,
do_sample=True,
temperature=temperature,
max_new_tokens=max_new_tokens,
top_k=top_k,
repetition_penalty=repetition_penalty,
top_p=top_p
)
t = Thread(target=model.generate, kwargs=generate_kwargs)
t.start()
outputs = []
for new_token in streamer:
if STOP_TOKEN in new_token:
outputs.append(new_token[:-len(stop_token)-1])
yield "".join(outputs)
break
outputs.append(new_token)
yield "".join(outputs)
# Load model
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
tokenizer = AutoTokenizer.from_pretrained("ai21labs/Jamba-v0.1")
# 4-bit precision quant config
quantization_config = BitsAndBytesConfig(
load_in_8bit=True,
llm_int8_skip_modules=["mamba"]
)
# Load model and tokenizer with ChatML format
model = AutoModelForCausalLM.from_pretrained(
"ai21labs/Jamba-v0.1",
trust_remote_code=True,
torch_dtype=torch.cuda.is_bf16_supported() and torch.bfloat16 or torch.float16,
attn_implementation="flash_attention_2",
low_cpu_mem_usage=True,
quantization_config=quantization_config
)
config = PeftConfig.from_pretrained("mlabonne/Jambatypus-v0.1")
model = PeftModel.from_pretrained(model, "mlabonne/Jambatypus-v0.1")
# Create Gradio interface
gr.ChatInterface(
predict,
title="Jambatypus",
description="Chat with Jambatypus!",
examples=[
["Can you solve the equation 2x + 3 = 11 for x?"],
["Write an epic poem about Ancient Rome."],
["Who was the first person to walk on the Moon?"],
["Use a list comprehension to create a list of squares for numbers from 1 to 10."],
["Recommend some popular science fiction books."],
["Can you write a short story about a time-traveling detective?"]
],
additional_inputs_accordion=gr.Accordion(label="⚙️ Parameters", open=False),
additional_inputs=[
gr.Textbox("Perform the task to the best of your ability.", label="System prompt"),
gr.Slider(0, 1, 0.8, label="Temperature"),
gr.Slider(128, 4096, 1024, label="Max new tokens"),
gr.Slider(1, 80, 40, label="Top K sampling"),
gr.Slider(0, 2, 1.1, label="Repetition penalty"),
gr.Slider(0, 1, 0.95, label="Top P sampling"),
],
theme=gr.themes.Soft(primary_hue="green"),
).queue().launch(share=True)
```