Quantization made by Richard Erkhov.
Llama-3-8B-Instruct-80K-QLoRA-Merged - GGUF
- Model creator: https://huggingface.co./namespace-Pt/
- Original model: https://huggingface.co./namespace-Pt/Llama-3-8B-Instruct-80K-QLoRA-Merged/
Original model description:
license: mit pipeline_tag: text-generation
Llama-3-8B-Instruct-80K-QLoRA-Merged
We extend the context length of Llama-3-8B-Instruct to 80K using QLoRA and 3.5K long-context training data synthesized from GPT-4. The entire training cycle is super efficient, which takes 8 hours on a 8xA800 (80G) machine. Yet, the resulted model achieves remarkable performance on a series of downstream long-context evaluation benchmarks.
NOTE: This model is the result of merging meta-llama/Meta-Llama-3-8B-Instruct and namespace-Pt/Llama-3-8B-Instruct-80K-QLoRA.
Evaluation
All the following evaluation results can be reproduced following instructions here.
Needle in a Haystack
We evaluate the model on the Needle-In-A-HayStack task using the official setting. The blue vertical line indicates the training context length, i.e. 80K.
LongBench
We evaluate the model on LongBench using 32K context length and the official prompt template. For meta-llama/Meta-Llama-3-8B-Instruct, we use 8K context length.
| Model | Single-Doc QA | Multi-Doc QA | Summarization | Few-Shot Learning | Synthetic | Code | Avg |
|---|---|---|---|---|---|---|---|
| meta-llama/Meta-Llama-3-8B-Instruct | 37.33 | 36.04 | 26.83 | 69.56 | 37.75 | 53.24 | 43.20 |
| gradientai/Llama-3-8B-Instruct-262k | 37.29 | 31.20 | 26.18 | 67.25 | 44.25 | 62.71 | 43.73 |
| Llama-3-8B-Instruct-80K-QLoRA-Merged | 43.57 | 43.07 | 28.93 | 69.15 | 48.50 | 51.95 | 47.19 |
InfiniteBench
We evaluate the model on InfiniteBench using 80K context length and the official prompt template. The results of GPT-4 is copied from the paper. For meta-llama/Meta-Llama-3-8B-Instruct, we use 8K context length.
| Model | LongBookQA Eng | LongBookSum Eng |
|---|---|---|
| GPT-4 | 22.22 | 14.73 |
| meta-llama/Meta-Llama-3-8B-Instruct | 7.00 | 16.40 |
| gradientai/Llama-3-8B-Instruct-262k | 20.30 | 10.34 |
| Llama-3-8B-Instruct-80K-QLoRA-Merged | 30.92 | 14.73 |
Topic Retrieval
We evaluate the model on Topic Retrieval task with [5,10,15,20,25,30,40,50,60,70] topics.
MMLU
We evaluate the model's zero-shot performance on MMLU benchmark as a reflection of its short-context capability.
| Model | STEM | Social Sciences | Humanities | Others | Avg |
|---|---|---|---|---|---|
| Llama-2-7B-Chat | 35.92 | 54.37 | 51.74 | 51.42 | 47.22 |
| Mistral-7B-v0.2-Instruct | 48.79 | 69.95 | 64.99 | 61.64 | 60.10 |
| meta-llama/Meta-Llama-3-8B-Instruct | 53.87 | 75.66 | 69.44 | 69.75 | 65.91 |
| gradientai/Llama-3-8B-Instruct-262k | 52.10 | 73.26 | 67.15 | 69.80 | 64.34 |
| Llama-3-8B-Instruct-80K-QLoRA-Merged | 53.10 | 73.24 | 67.32 | 68.79 | 64.44 |
Environment
torch==2.2.2
flash_attn==2.5.6
transformers==4.39.3
Usage
import json
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "namespace-Pt/Llama-3-8B-Instruct-80K-QLoRA-Merged"
torch_dtype = torch.bfloat16
# place the model on GPU
device_map = {"": "cuda"}
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype=torch.bfloat16,
device_map=device_map,
attn_implementation="flash_attention_2",
).eval()
with torch.no_grad():
# short context
messages = [{"role": "user", "content": "Tell me about yourself."}]
inputs = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt", return_dict=True).to("cuda")
outputs = model.generate(**inputs, max_new_tokens=50)[:, inputs["input_ids"].shape[1]:]
print(f"Input Length: {inputs['input_ids'].shape[1]}")
print(f"Output: {tokenizer.decode(outputs[0])}")
# long context
with open("data/narrativeqa.json", encoding="utf-8") as f:
example = json.load(f)
messages = [{"role": "user", "content": example["context"]}]
inputs = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt", return_dict=True).to("cuda")
outputs = model.generate(**inputs, do_sample=False, top_p=1, temperature=1, max_new_tokens=20)[:, inputs["input_ids"].shape[1]:]
print("*"*20)
print(f"Input Length: {inputs['input_ids'].shape[1]}")
print(f"Answers: {example['answer']}")
print(f"Prediction: {tokenizer.decode(outputs[0])}")
You may observe messages like:
This is a friendly reminder - the current text generation call will exceed the model's predefined maximum length (8192). Depending on the model, you may observe exceptions, performance degradation, or nothing at all. or Setting pad_token_id to eos_token_id:128001 for open-end generation. They do not matter. Just ignore them.
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