# 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](https://huggingface.co/MaziyarPanahi/Llama-3-8B-Instruct-v0.10-GGUF) # 🏆 [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_MaziyarPanahi__Llama-3-8B-Instruct-v0.10) | Metric |Value| |-------------------|----:| |Avg. |26.66| |IFEval (0-Shot) |76.67| |BBH (3-Shot) |27.92| |MATH Lvl 5 (4-Shot)| 4.91| |GPQA (0-shot) | 7.83| |MuSR (0-shot) |10.81| |MMLU-PRO (5-shot) |31.80| # 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. ```python 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):]) ```