This model is a Proof of Concept. First 2 Llama-3-8B models has been merged using Mergekit and pre-training continued using QLora and Unsloth for 1000 samples from roneneldan/TinyStories.
Loss still decreases each epoch so I believe this is a successful experiment where there is a lot of room to experiment.
Llama-3-11.5B is a merge of the following models using
LazyMergekit:
1slices:
2 - sources:
3 - model: meta-llama/Meta-Llama-3-8B
4 layer_range: [0, 24]
5 - sources:
6 - model: meta-llama/Meta-Llama-3-8B
7 layer_range: [8, 32]
8merge_method: passthrough
9dtype: bfloat16
1!pip install -qU transformers accelerate
2
3from transformers import AutoTokenizer
4import transformers
5import torch
6
7model = "beratcmn/Llama-3-11.5B"
8messages = [{"role": "user", "content": "What is a large language model?"}]
9
10tokenizer = AutoTokenizer.from_pretrained(model)
11prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
12pipeline = transformers.pipeline(
13 "text-generation",
14 model=model,
15 torch_dtype=torch.float16,
16 device_map="auto",
17)
18
19outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
20print(outputs[0]["generated_text"])
This llama model was trained 2x faster with
Unsloth and Huggingface's TRL library.
