base_model:
- tokyotech-llm/Llama-3.1-Swallow-70B-v0.1
- meta-llama/Llama-3.1-70B
- meta-llama/Llama-3.3-70B-Instruct
library_name: transformers
tags:
- mergekit
- merge
- chat
language:
- ja
- en
pipeline_tag: text-generation
license: llama3.3
Llama-3.3-FakeSwallow-70B-Instruct-v0.1
This is a merge of pre-trained language models created using mergekit.
Test environment
This model was tested using text-generation-webui. I use preset min_p with temperature=1 for Generation.
Usage
This format must be adhered to strictly, as deviations may result in less optimal outputs from the model.
The template used to construct a prompt for the instruct model is specified as follows:
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
{SYSTEM_PROMPT}<|eot_id|><|start_header_id|>user<|end_header_id|>
{USER_MESSAGE}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
For the "{SYSTEM_PROMPT}" part, We recommend using "あなたは誠実で優秀な日本人のアシスタントです。" or "You are a helpful assistant."
For the "{USER_MESSAGE}" part, We recommend using {instruction}\n{input}
In other words, We recommend the following:
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
あなたは誠実で優秀な日本人のアシスタントです。<|eot_id|><|start_header_id|>user<|end_header_id|>
{instruction}
{input}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
Use the instruct model
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "nitky/Llama-3.3-FakeSwallow-70B-Instruct-v0.1"
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)
prompt = "Give me a short introduction to large language model."
messages = [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
generated_ids = model.generate(
**model_inputs,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
Merge Details
Merge Method
This model was merged using the task arithmetic merge method using meta-llama/Llama-3.1-70B as a base.
Models Merged
The following models were included in the merge:
Configuration
The following YAML configuration was used to produce this model:
merge_method: task_arithmetic
base_model: meta-llama/Llama-3.1-70B
models:
- model: tokyotech-llm/Llama-3.1-Swallow-70B-v0.1
parameters:
weight: 1.0
- model: meta-llama/Llama-3.3-70B-Instruct
parameters:
weight: 0.8
dtype: bfloat16
name: Llama-3.3-FakeSwallow-70B-Instruct-v0.1