RichardErkhov/MaziyarPanahi_-_calme-2.2-llama3-70b-gguf

Quantization made by Richard Erkhov.

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calme-2.2-llama3-70b - GGUF

• —Model creator: https://huggingface.co/MaziyarPanahi/
• —Original model: https://huggingface.co/MaziyarPanahi/calme-2.2-llama3-70b/

Original model description: --- language:

• —en license: llama3 library_name: transformers tags:
• —axolotl
• —finetune
• —dpo
• —facebook
• —meta
• —pytorch
• —llama
• —llama-3
• —chatml base_model: meta-llama/Meta-Llama-3-70B-Instruct datasets:
• —Intel/orcadpopairs pipelinetag: text-generation licensename: llama3 licenselink: LICENSE inference: false modelcreator: MaziyarPanahi quantized_by: MaziyarPanahi model-index:
• —name: calme-2.2-llama3-70b results:
• —task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2arc config: ARC-Challenge split: test args: numfew_shot: 25 metrics:
• —type: accnorm value: 72.53 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/openllm_leaderboard?query=MaziyarPanahi/calme-2.2-llama3-70b name: Open LLM Leaderboard
• —task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: numfewshot: 10 metrics:
• —type: accnorm value: 86.22 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/openllm_leaderboard?query=MaziyarPanahi/calme-2.2-llama3-70b name: Open LLM Leaderboard
• —task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: numfewshot: 5 metrics:
• —type: acc value: 80.41 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/openllmleaderboard?query=MaziyarPanahi/calme-2.2-llama3-70b name: Open LLM Leaderboard
• —task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthfulqa config: multiplechoice split: validation args: numfewshot: 0 metrics:
• —type: mc2 value: 63.57 source: url: https://huggingface.co/spaces/HuggingFaceH4/openllmleaderboard?query=MaziyarPanahi/calme-2.2-llama3-70b name: Open LLM Leaderboard
• —task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winograndexl split: validation args: numfew_shot: 5 metrics:
• —type: acc value: 82.79 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/openllmleaderboard?query=MaziyarPanahi/calme-2.2-llama3-70b name: Open LLM Leaderboard
• —task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: numfewshot: 5 metrics:
• —type: acc value: 88.25 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/openllmleaderboard?query=MaziyarPanahi/calme-2.2-llama3-70b name: Open LLM Leaderboard
• —task: type: text-generation name: Text Generation dataset: name: IFEval (0-Shot) type: HuggingFaceH4/ifeval args: numfewshot: 0 metrics:
• —type: instlevelstrictacc and promptlevelstrictacc value: 82.08 name: strict accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/openllmleaderboard?query=MaziyarPanahi/calme-2.2-llama3-70b name: Open LLM Leaderboard
• —task: type: text-generation name: Text Generation dataset: name: BBH (3-Shot) type: BBH args: numfewshot: 3 metrics:
• —type: accnorm value: 48.57 name: normalized accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/openllm_leaderboard?query=MaziyarPanahi/calme-2.2-llama3-70b name: Open LLM Leaderboard
• —task: type: text-generation name: Text Generation dataset: name: MATH Lvl 5 (4-Shot) type: hendrycks/competitionmath args: numfew_shot: 4 metrics:
• —type: exactmatch value: 22.96 name: exact match source: url: https://huggingface.co/spaces/open-llm-leaderboard/openllm_leaderboard?query=MaziyarPanahi/calme-2.2-llama3-70b name: Open LLM Leaderboard
• —task: type: text-generation name: Text Generation dataset: name: GPQA (0-shot) type: Idavidrein/gpqa args: numfewshot: 0 metrics:
• —type: accnorm value: 12.19 name: accnorm source: url: https://huggingface.co/spaces/open-llm-leaderboard/openllmleaderboard?query=MaziyarPanahi/calme-2.2-llama3-70b name: Open LLM Leaderboard
• —task: type: text-generation name: Text Generation dataset: name: MuSR (0-shot) type: TAUR-Lab/MuSR args: numfewshot: 0 metrics:
• —type: accnorm value: 15.3 name: accnorm source: url: https://huggingface.co/spaces/open-llm-leaderboard/openllmleaderboard?query=MaziyarPanahi/calme-2.2-llama3-70b name: Open LLM Leaderboard
• —task: type: text-generation name: Text Generation dataset: name: MMLU-PRO (5-shot) type: TIGER-Lab/MMLU-Pro config: main split: test args: numfewshot: 5 metrics:
• —type: acc value: 46.74 name: accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/openllmleaderboard?query=MaziyarPanahi/calme-2.2-llama3-70b name: Open LLM Leaderboard ---

<img src="./llama-3-merges.webp" alt="Llama-3 DPO Logo" width="500" style="margin-left:'auto' margin-right:'auto' display:'block'"/>

MaziyarPanahi/calme-2.2-llama3-70b

This model is a fine-tune (DPO) of meta-llama/Meta-Llama-3-70B-Instruct model.

PS: This fine-tuned model was previously known as MaziyarPanahi/Llama-3-70B-Instruct-DPO-v0.2. It was renamed to avoid any confusion with the original model.

⚡ Quantized GGUF

All GGUF models are available here: MaziyarPanahi/calme-2.2-llama3-70b-GGUF

🏆 Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Top 10 models on the Leaderboard <img src="./llama-qwen2-leaderboard.png" alt="Llama-3-70B finet-tuned models" style="margin-left:'auto' margin-right:'auto' display:'block'"/>

Prompt Template

This model uses ChatML prompt template:

<|im_start|>system
{System}
<|im_end|>
<|im_start|>user
{User}
<|im_end|>
<|im_start|>assistant
{Assistant}

How to use

You can use this model by using MaziyarPanahi/calme-2.2-llama3-70b 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/calme-2.2-llama3-70b"

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("<|im_end|>"),
    tokenizer.convert_tokens_to_ids("<|eot_id|>") # safer to have this too
]

outputs = pipeline(
    prompt,
    max_new_tokens=2048,
    eos_token_id=terminators,
    do_sample=True,
    temperature=0.6,
    top_p=0.95,
)
print(outputs[0]["generated_text"][len(prompt):])