RichardErkhov/abhinand_-_Llama-3-Galen-8B-32k-v1-gguf
0490
Quantization made by Richard Erkhov.
Llama-3-Galen-8B-32k-v1 - GGUF
- Model creator: https://huggingface.co/abhinand/
- Original model: https://huggingface.co/abhinand/Llama-3-Galen-8B-32k-v1/
Original model description: --- tags:
- merge
- mergekit
- lazymergekit
- aaditya/Llama3-OpenBioLLM-8B base_model:
- aaditya/Llama3-OpenBioLLM-8B license: llama3 language:
- en ---
Llama-3-Galen-8B-32k-v1
<img src="https://cdn-uploads.huggingface.co/production/uploads/60c8619d95d852a24572b025/R73wGdZE3GWeF9QZPvruG.jpeg" width="600" />
Llama-3-Galen-8B-32k-v1 is a RoPE scaled, DARE TIES merge of the following models using LazyMergekit:
This model is capable of handling a context size of 32K right out of the box, enabled with Dynamic RoPE scaling.
🧩 Configuration
models:
- model: johnsnowlabs/JSL-MedLlama-3-8B-v2.0
# No parameters necessary for base model
- model: aaditya/Llama3-OpenBioLLM-8B
parameters:
density: 0.53
weight: 0.5
merge_method: dare_ties
base_model: johnsnowlabs/JSL-MedLlama-3-8B-v2.0
parameters:
int8_mask: true
dtype: bfloat16💻 Usage
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "abhinand/Llama-3-Galen-8B-32k-v1"
messages = [{"role": "user", "content": "How long does it take to recover from COVID-19?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])