base_model, library_name, tags, language, pipeline_tag, license, license_name, license_link, inference, model_creator, model_name, quantized_by, datasets

base_model	library_name	tags	language	pipeline_tag	license	license_name	license_link	inference	model_creator	model_name	quantized_by	datasets
winglian/Llama-3-8b-64k-PoSE	transformers	axolotl finetune dpo facebook meta pytorch llama llama-3 64k pose	en	text-generation	llama3	llama3	LICENSE	false	MaziyarPanahi	Llama-3-8B-Instruct-64k	MaziyarPanahi	Intel/orca_dpo_pairs

MaziyarPanahi/Llama-3-8B-Instruct-64k

This model has been made based on a great of @winglian with his latest model winglian/Llama-3-8b-64k-PoSE

This model uses PoSE to extend Llama's context length from 8k to 64k @ rope_theta: 500000.0. We used PoSE with continued pretraining on 300M tokens from the RedPajama V1 dataset using data between 6k-8k tokens. We have further set rope_theta to 2M after continued pre-training to potentially further extend the context past 64k. This was trained on a subset of the RedPajama v1 dataset with text between 6k-8k context. We trained a rank stabilized LoRA of rank 256. WandB

Quantized GGUF

All GGUF models come with context length of 64000: MaziyarPanahi/Llama-3-8B-Instruct-64k-GGUF

How to use

You can use this model by using MaziyarPanahi/Llama-3-8B-Instruct-DPO-v0.3 as the model name in Hugging Face's transformers library.

from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
from transformers import pipeline
import torch

model_id = "MaziyarPanahi/Llama-3-8B-Instruct-64k"

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True,
    # attn_implementation="flash_attention_2"
)

tokenizer = AutoTokenizer.from_pretrained(
    model_id,
    trust_remote_code=True
)

streamer = TextStreamer(tokenizer)

pipeline = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    model_kwargs={"torch_dtype": torch.bfloat16},
    streamer=streamer
)

# Then you can use the pipeline to generate text.

messages = [
    {"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
    {"role": "user", "content": "Who are you?"},
]

prompt = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)

terminators = [
    tokenizer.eos_token_id,
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

outputs = pipeline(
    prompt,
    max_new_tokens=8192,
    eos_token_id=terminators,
    do_sample=True,
    temperature=0.6,
    top_p=0.95,
)
print(outputs[0]["generated_text"][len(prompt):])