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

Name: MaziyarPanahi_-_calme-2.2-llama3-70b-gguf
Availability: InStock

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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/

Name	Quant method	Size
calme-2.2-llama3-70b.Q2_K.gguf	Q2_K	24.56GB
calme-2.2-llama3-70b.IQ3_XS.gguf	IQ3_XS	27.29GB
calme-2.2-llama3-70b.IQ3_S.gguf	IQ3_S	28.79GB
calme-2.2-llama3-70b.Q3_K_S.gguf	Q3_K_S	28.79GB
calme-2.2-llama3-70b.IQ3_M.gguf	IQ3_M	29.74GB
calme-2.2-llama3-70b.Q3_K.gguf	Q3_K	31.91GB
calme-2.2-llama3-70b.Q3_K_M.gguf	Q3_K_M	31.91GB
calme-2.2-llama3-70b.Q3_K_L.gguf	Q3_K_L	34.59GB
calme-2.2-llama3-70b.IQ4_XS.gguf	IQ4_XS	35.64GB
calme-2.2-llama3-70b.Q4_0.gguf	Q4_0	37.22GB
calme-2.2-llama3-70b.IQ4_NL.gguf	IQ4_NL	37.58GB
calme-2.2-llama3-70b.Q4_K_S.gguf	Q4_K_S	37.58GB
calme-2.2-llama3-70b.Q4_K.gguf	Q4_K	39.6GB
calme-2.2-llama3-70b.Q4_K_M.gguf	Q4_K_M	39.6GB
calme-2.2-llama3-70b.Q4_1.gguf	Q4_1	41.27GB
calme-2.2-llama3-70b.Q5_0.gguf	Q5_0	45.32GB
calme-2.2-llama3-70b.Q5_K_S.gguf	Q5_K_S	45.32GB
calme-2.2-llama3-70b.Q5_K.gguf	Q5_K	46.52GB
calme-2.2-llama3-70b.Q5_K_M.gguf	Q5_K_M	46.52GB
calme-2.2-llama3-70b.Q5_1.gguf	Q5_1	49.36GB
calme-2.2-llama3-70b.Q6_K.gguf	Q6_K	53.91GB
calme-2.2-llama3-70b.Q8_0.gguf	Q8_0	69.83GB

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/orca_dpo_pairs pipeline_tag: text-generation license_name: llama3 license_link: LICENSE inference: false model_creator: 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: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics:
  - type: acc_norm value: 72.53 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_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: num_few_shot: 10 metrics:
  - type: acc_norm value: 86.22 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_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: num_few_shot: 5 metrics:
  - type: acc value: 80.41 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=MaziyarPanahi/calme-2.2-llama3-70b name: Open LLM Leaderboard
- task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics:
  - type: mc2 value: 63.57 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?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: winogrande_xl split: validation args: num_few_shot: 5 metrics:
  - type: acc value: 82.79 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?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: num_few_shot: 5 metrics:
  - type: acc value: 88.25 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?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: num_few_shot: 0 metrics:
  - type: inst_level_strict_acc and prompt_level_strict_acc value: 82.08 name: strict accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?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: num_few_shot: 3 metrics:
  - type: acc_norm value: 48.57 name: normalized accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_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/competition_math args: num_few_shot: 4 metrics:
  - type: exact_match value: 22.96 name: exact match source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_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: num_few_shot: 0 metrics:
  - type: acc_norm value: 12.19 name: acc_norm source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?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: num_few_shot: 0 metrics:
  - type: acc_norm value: 15.3 name: acc_norm source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?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: num_few_shot: 5 metrics:
  - type: acc value: 46.74 name: accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=MaziyarPanahi/calme-2.2-llama3-70b name: Open LLM Leaderboard

Llama-3 DPO Logo

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

Metric	Value
Avg.	37.98
IFEval (0-Shot)	82.08
BBH (3-Shot)	48.57
MATH Lvl 5 (4-Shot)	22.96
GPQA (0-shot)	12.19
MuSR (0-shot)	15.30
MMLU-PRO (5-shot)	46.74

Metric	Value
Avg.	78.96
AI2 Reasoning Challenge (25-Shot)	72.53
HellaSwag (10-Shot)	86.22
MMLU (5-Shot)	80.41
TruthfulQA (0-shot)	63.57
Winogrande (5-shot)	82.79
GSM8k (5-shot)	88.25

Top 10 models on the Leaderboard

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.

python

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):])

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