README.md

metadata

language:
  - en
license: other
library_name: transformers
tags:
  - axolotl
  - finetune
  - facebook
  - meta
  - pytorch
  - llama
  - llama-3
base_model: MaziyarPanahi/Llama-3-8B-Instruct-v0.9
model_name: Llama-3-8B-Instruct-v0.10
pipeline_tag: text-generation
license_name: llama3
license_link: LICENSE
inference: false
model_creator: MaziyarPanahi

Llama-3-8B-Instruct-v0.10

This model was developed based on MaziyarPanahi/Llama-3-8B-Instruct-v0.9 model.

⚡ Quantized GGUF

All GGUF models are available here: MaziyarPanahi/Llama-3-8B-Instruct-v0.10-GGUF

🏆 Open LLM Leaderboard Evaluation Results

coming soon!

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-v0.10 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-v0.10"

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=0.105,
)
print(outputs[0]["generated_text"][len(prompt):])