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Llama-3-8B-Instruct-v0.4 - GGUF
- Model creator: https://huggingface.co/MaziyarPanahi/
- Original model: https://huggingface.co/MaziyarPanahi/Llama-3-8B-Instruct-v0.4/
Original model description:
base_model: meta-llama/Meta-Llama-3-8B-Instruct library_name: transformers tags: - axolotl - finetune - facebook - meta - pytorch - llama - llama-3 language: - en pipeline_tag: text-generation license: other license_name: llama3 license_link: LICENSE inference: false model_creator: MaziyarPanahi model_name: Llama-3-8B-Instruct-v0.4 quantized_by: MaziyarPanahi
Llama-3-8B-Instruct-v0.4
This model was developed based on meta-llama/Meta-Llama-3-8B-Instruct model.
Quantized GGUF
All GGUF models are available here: MaziyarPanahi/Llama-3-8B-Instruct-v0.4-GGUF
Prompt Template
This model uses ChatML prompt template:
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
{system_prompt}<|eot_id|><|start_header_id|>user<|end_header_id|>
{prompt}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
How to use
You can use this model by using MaziyarPanahi/Llama-3-8B-Instruct-v0.4 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-v0.4"
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("<|eot_id|>")
]
outputs = pipeline(
prompt,
max_new_tokens=512,
eos_token_id=terminators,
do_sample=True,
temperature=0.6,
top_p=0.95,
)
print(outputs[0]["generated_text"][len(prompt):])
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