Quantization made by Richard Erkhov.
Llama-3-8B-Instruct-64k - GGUF
- Model creator: https://huggingface.co/MaziyarPanahi/
- Original model: https://huggingface.co/MaziyarPanahi/Llama-3-8B-Instruct-64k/
Original model description:
base_model: winglian/Llama-3-8b-64k-PoSE library_name: transformers tags: - axolotl - finetune - dpo - facebook - meta - pytorch - llama - llama-3 - 64k - pose language: - en pipeline_tag: text-generation license: llama3 license_name: llama3 license_link: LICENSE inference: false model_creator: MaziyarPanahi model_name: Llama-3-8B-Instruct-64k quantized_by: MaziyarPanahi datasets: - 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):])
- Downloads last month
- 261