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RichardErkhov/
ssmits_-_Qwen2.5-95B-Instruct-gguf

Name: ssmits_-_Qwen2.5-95B-Instruct-gguf
Availability: InStock

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Quantization made by Richard Erkhov.

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Qwen2.5-95B-Instruct - GGUF

Model creator: https://huggingface.co/ssmits/
Original model: https://huggingface.co/ssmits/Qwen2.5-95B-Instruct/

Name	Quant method	Size
Qwen2.5-95B-Instruct.Q2_K.gguf	Q2_K	35.92GB
Qwen2.5-95B-Instruct.IQ3_XS.gguf	IQ3_XS	39.6GB
Qwen2.5-95B-Instruct.IQ3_S.gguf	IQ3_S	41.6GB
Qwen2.5-95B-Instruct.Q3_K_S.gguf	Q3_K_S	41.49GB
Qwen2.5-95B-Instruct.IQ3_M.gguf	IQ3_M	42.84GB
Qwen2.5-95B-Instruct.Q3_K.gguf	Q3_K	45.52GB
Qwen2.5-95B-Instruct.Q3_K_M.gguf	Q3_K_M	45.52GB
Qwen2.5-95B-Instruct.Q3_K_L.gguf	Q3_K_L	47.83GB
Qwen2.5-95B-Instruct.IQ4_XS.gguf	IQ4_XS	48.6GB
Qwen2.5-95B-Instruct.Q4_0.gguf	Q4_0	49.9GB
Qwen2.5-95B-Instruct.IQ4_NL.gguf	IQ4_NL	50.55GB
Qwen2.5-95B-Instruct.Q4_K_S.gguf	Q4_K_S	53.05GB
Qwen2.5-95B-Instruct.Q4_K.gguf	Q4_K	57.45GB
Qwen2.5-95B-Instruct.Q4_K_M.gguf	Q4_K_M	57.45GB
Qwen2.5-95B-Instruct.Q4_1.gguf	Q4_1	55.33GB
Qwen2.5-95B-Instruct.Q5_0.gguf	Q5_0	60.77GB
Qwen2.5-95B-Instruct.Q5_K_S.gguf	Q5_K_S	62.25GB
Qwen2.5-95B-Instruct.Q5_K.gguf	Q5_K	66.04GB
Qwen2.5-95B-Instruct.Q5_K_M.gguf	Q5_K_M	66.04GB
Qwen2.5-95B-Instruct.Q5_1.gguf	Q5_1	66.21GB
Qwen2.5-95B-Instruct.Q6_K.gguf	Q6_K	78.06GB
Qwen2.5-95B-Instruct.Q8_0.gguf	Q8_0	93.67GB

Original model description:

license: other license_name: qwen license_link: https://huggingface.co/Qwen/Qwen2.5-72B-Instruct/blob/main/LICENSE language:

en pipeline_tag: text-generation base_model:
Qwen/Qwen2.5-72B-Instruct model-index:
name: Qwen2.5-95B-Instruct results:
- 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: 84.31 name: strict accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=ssmits/Qwen2.5-95B-Instruct 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: 58.53 name: normalized accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=ssmits/Qwen2.5-95B-Instruct 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: 6.04 name: exact match source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=ssmits/Qwen2.5-95B-Instruct 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: 15.21 name: acc_norm source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=ssmits/Qwen2.5-95B-Instruct 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: 13.61 name: acc_norm source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=ssmits/Qwen2.5-95B-Instruct 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: 46.85 name: accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=ssmits/Qwen2.5-95B-Instruct name: Open LLM Leaderboard tags:
chat

Qwen2.5-95B-Instruct

Qwen2.5-95B-Instruct is a Qwen/Qwen2.5-72B-Instruct self-merge made with MergeKit.

The layer ranges chosen for this merge were inspired by a rough estimate of the layer similarity analysis of ssmits/Falcon2-5.5B-multilingual. Layer similarity analysis involves examining the outputs of different layers in a neural network to determine how similar or different they are. This technique can help identify which layers contribute most significantly to the model's performance. In the context of the Falcon-11B model, layer similarity analysis across multiple languages revealed that the first half of the layers were more important for maintaining performance. Additionally, this analysis can be used to more rigidly slice and add extra layers for optimal Next Token Prediction, allowing for possibly a model architecture that's more creative and powerful.

Special thanks to Eric Hartford for both inspiring and evaluating the original model, to Charles Goddard for creating MergeKit, and to Mathieu Labonne for creating the Meta-Llama-3-120B-Instruct model that served as the main inspiration for this merge.

🔍 Applications

This model is probably good for creative writing tasks. It uses the Qwen chat template with a default context window of 128K.

The model could be quite creative and maybe even better than the 72B model at some tasks.

⚡ Quantized models

To be quantized.

GGUF: [Link to GGUF model]
EXL2: [Link to EXL2 model]
mlx: [Link to mlx model]

🏆 Evaluation

This model has yet to be thoroughly evaluated. It is expected to excel in creative writing and more but may have limitations in other tasks. Use it with caution and don't expect it to outperform state-of-the-art models outside of specific creative use cases.

Once the model is created and tested, this section will be updated with:

Links to evaluation threads on social media platforms
Examples of the model's performance in creative writing tasks
Comparisons with other large language models in various applications
Community feedback and use cases

We encourage users to share their experiences and evaluations to help build a comprehensive understanding of the model's capabilities and limitations.

🧩 Configuration

yaml

slices:
- sources:
  - layer_range: [0, 10]
    model: Qwen/Qwen2.5-72B-Instruct
- sources:
  - layer_range: [5, 15]
    model: Qwen/Qwen2.5-72B-Instruct
- sources:
  - layer_range: [10, 20]
    model: Qwen/Qwen2.5-72B-Instruct
- sources:
  - layer_range: [15, 25]
    model: Qwen/Qwen2.5-72B-Instruct
- sources:
  - layer_range: [20, 30]
    model: Qwen/Qwen2.5-72B-Instruct
- sources:
  - layer_range: [25, 80]
    model: Qwen/Qwen2.5-72B-Instruct
dtype: bfloat16
merge_method: passthrough

💻 Usage

python

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "ssmits/Qwen2.5-95B-Instruct"
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"])

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	37.43
IFEval (0-Shot)	84.31
BBH (3-Shot)	58.53
MATH Lvl 5 (4-Shot)	6.04
GPQA (0-shot)	15.21
MuSR (0-shot)	13.61
MMLU-PRO (5-shot)	46.85

RichardErkhov/ssmits_-_Qwen2.5-95B-Instruct-gguf