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RichardErkhov/
mlabonne_-_ChimeraLlama-3-8B-v3-gguf

Name: mlabonne_-_ChimeraLlama-3-8B-v3-gguf
Availability: InStock

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ChimeraLlama-3-8B-v3 - GGUF

Model creator: https://huggingface.co/mlabonne/
Original model: https://huggingface.co/mlabonne/ChimeraLlama-3-8B-v3/

Name	Quant method	Size
ChimeraLlama-3-8B-v3.Q2_K.gguf	Q2_K	2.96GB
ChimeraLlama-3-8B-v3.IQ3_XS.gguf	IQ3_XS	3.28GB
ChimeraLlama-3-8B-v3.IQ3_S.gguf	IQ3_S	3.43GB
ChimeraLlama-3-8B-v3.Q3_K_S.gguf	Q3_K_S	3.41GB
ChimeraLlama-3-8B-v3.IQ3_M.gguf	IQ3_M	3.52GB
ChimeraLlama-3-8B-v3.Q3_K.gguf	Q3_K	3.74GB
ChimeraLlama-3-8B-v3.Q3_K_M.gguf	Q3_K_M	3.74GB
ChimeraLlama-3-8B-v3.Q3_K_L.gguf	Q3_K_L	4.03GB
ChimeraLlama-3-8B-v3.IQ4_XS.gguf	IQ4_XS	4.18GB
ChimeraLlama-3-8B-v3.Q4_0.gguf	Q4_0	4.34GB
ChimeraLlama-3-8B-v3.IQ4_NL.gguf	IQ4_NL	4.38GB
ChimeraLlama-3-8B-v3.Q4_K_S.gguf	Q4_K_S	4.37GB
ChimeraLlama-3-8B-v3.Q4_K.gguf	Q4_K	4.58GB
ChimeraLlama-3-8B-v3.Q4_K_M.gguf	Q4_K_M	4.58GB
ChimeraLlama-3-8B-v3.Q4_1.gguf	Q4_1	4.78GB
ChimeraLlama-3-8B-v3.Q5_0.gguf	Q5_0	5.21GB
ChimeraLlama-3-8B-v3.Q5_K_S.gguf	Q5_K_S	5.21GB
ChimeraLlama-3-8B-v3.Q5_K.gguf	Q5_K	5.34GB
ChimeraLlama-3-8B-v3.Q5_K_M.gguf	Q5_K_M	5.34GB
ChimeraLlama-3-8B-v3.Q5_1.gguf	Q5_1	5.65GB
ChimeraLlama-3-8B-v3.Q6_K.gguf	Q6_K	6.14GB
ChimeraLlama-3-8B-v3.Q8_0.gguf	Q8_0	7.95GB

Original model description:

license: other tags:

merge
mergekit
lazymergekit base_model:
NousResearch/Meta-Llama-3-8B-Instruct
mlabonne/OrpoLlama-3-8B
cognitivecomputations/dolphin-2.9-llama3-8b
Danielbrdz/Barcenas-Llama3-8b-ORPO
VAGOsolutions/Llama-3-SauerkrautLM-8b-Instruct
vicgalle/Configurable-Llama-3-8B-v0.3
MaziyarPanahi/Llama-3-8B-Instruct-DPO-v0.3

ChimeraLlama-3-8B-v3

ChimeraLlama-3-8B-v3 is a merge of the following models using LazyMergekit:

🧩 Configuration

yaml

models:
  - model: NousResearch/Meta-Llama-3-8B
    # No parameters necessary for base model
  - model: NousResearch/Meta-Llama-3-8B-Instruct
    parameters:
      density: 0.6
      weight: 0.5
  - model: mlabonne/OrpoLlama-3-8B
    parameters:
      density: 0.55
      weight: 0.05
  - model: cognitivecomputations/dolphin-2.9-llama3-8b
    parameters:
      density: 0.55
      weight: 0.05
  - model: Danielbrdz/Barcenas-Llama3-8b-ORPO
    parameters:
      density: 0.55
      weight: 0.2
  - model: VAGOsolutions/Llama-3-SauerkrautLM-8b-Instruct
    parameters:
      density: 0.55
      weight: 0.1
  - model: vicgalle/Configurable-Llama-3-8B-v0.3
    parameters:
      density: 0.55
      weight: 0.05
  - model: MaziyarPanahi/Llama-3-8B-Instruct-DPO-v0.3
    parameters:
      density: 0.55
      weight: 0.05
merge_method: dare_ties
base_model: NousResearch/Meta-Llama-3-8B
parameters:
  int8_mask: true
dtype: float16

💻 Usage

python

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "mlabonne/ChimeraLlama-3-8B-v3"
messages = [{"role": "user", "content": "What is a large language model?"}]

tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    torch_dtype=torch.float16,
    device_map="auto",
)

outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])

RichardErkhov/mlabonne_-_ChimeraLlama-3-8B-v3-gguf

Original model description:

ChimeraLlama-3-8B-v3

🧩 Configuration

💻 Usage

RichardErkhov/
mlabonne_-_ChimeraLlama-3-8B-v3-gguf