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This model is capable of handling a context size of 32K right out of the box, enabled with Dynamic RoPE scaling.
1models:
2 - model: johnsnowlabs/JSL-MedLlama-3-8B-v2.0
3 # No parameters necessary for base model
4 - model: aaditya/Llama3-OpenBioLLM-8B
5 parameters:
6 density: 0.53
7 weight: 0.5
8merge_method: dare_ties
9base_model: johnsnowlabs/JSL-MedLlama-3-8B-v2.0
10parameters:
11 int8_mask: true
12dtype: bfloat161!pip install -qU transformers accelerate
2
3from transformers import AutoTokenizer
4import transformers
5import torch
6
7model = "abhinand/Llama-3-Galen-8B-32k-v1"
8messages = [{"role": "user", "content": "How long does it take to recover from COVID-19?"}]
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"])