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---
base_model: MaziyarPanahi/Llama-3-8B-Instruct-v0.4
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-v0.8
quantized_by: MaziyarPanahi
---
<img src="./llama-3-merges.webp" alt="Llama-3 DPO Logo" width="500" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
# Llama-3-8B-Instruct-v0.8
This model was developed based on `MaziyarPanahi/Llama-3-8B-Instruct-v0.4` model.
# Quantized GGUF
All GGUF models are available here: [MaziyarPanahi/Llama-3-8B-Instruct-v0.8-GGUF](https://huggingface.co/MaziyarPanahi/Llama-3-8B-Instruct-v0.8-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-v0.8` as the model name in Hugging Face's
transformers library.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
from transformers import pipeline
import torch
model_id = "MaziyarPanahi/Llama-3-8B-Instruct-v0.8"
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):])
``` |