metadata

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

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

!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"])

🏆 Evaluation

Initial benchmarks show interesting performance characteristics compared to the 72B model:

Strengths

The 95B model shows notable improvements in:

Mathematical Reasoning

Up to 5.83x improvement in algebra tasks
3.33x improvement in pre-algebra
Consistent gains across geometry, number theory, and probability tasks
Overall stronger performance in complex mathematical reasoning

Spatial & Object Understanding

11% improvement in object placement tasks
7% better at tabular data interpretation
Enhanced performance in logical deduction with multiple objects

Complex Language Tasks

4% improvement in disambiguation tasks
2% better at movie recommendations
Slight improvements in hyperbaton (complex word order) tasks

Creative & Analytical Reasoning

10% improvement in murder mystery solving
Better performance in tasks requiring creative problem-solving

Areas for Consideration

While the model shows improvements in specific areas, users should note that the 72B model still performs better in many general language and reasoning tasks. The 95B version appears to excel particularly in mathematical and spatial reasoning while maintaining comparable performance in other areas.

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

Key	72b Result	95b Result	Difference	Which is Higher	Multiplier
leaderboard_musr.acc_norm,none	0.419	0.427	0.008	95b	1.02
leaderboard_bbh_sports_understanding.acc_norm,none	0.892	0.876	-0.016	72b	0.98
leaderboard_bbh_logical_deduction_three_objects.acc_norm,none	0.94	0.928	-0.012	72b	0.99
leaderboard_math_geometry_hard.exact_match,none	0	0.008	0.008	95b	0.00
leaderboard_gpqa.acc_norm,none	0.375	0.364	-0.011	72b	0.97
leaderboard_math_hard.exact_match,none	0.012	0.06	0.048	95b	5.00
leaderboard.exact_match,none	0.012	0.06	0.048	95b	5.00
leaderboard.prompt_level_loose_acc,none	0.861	0.839	-0.022	72b	0.97
leaderboard.prompt_level_strict_acc,none	0.839	0.813	-0.026	72b	0.97
leaderboard.inst_level_loose_acc,none	0.904	0.891	-0.013	72b	0.99
leaderboard.acc_norm,none	0.641	0.622	-0.020	72b	0.97
leaderboard.inst_level_strict_acc,none	0.888	0.873	-0.016	72b	0.98
leaderboard.acc,none	0.563	0.522	-0.041	72b	0.93
leaderboard_bbh_causal_judgement.acc_norm,none	0.668	0.663	-0.005	72b	0.99
leaderboard_bbh_salient_translation_error_detection.acc_norm,none	0.668	0.588	-0.080	72b	0.88
leaderboard_gpqa_extended.acc_norm,none	0.372	0.364	-0.007	72b	0.98
leaderboard_math_prealgebra_hard.exact_match,none	0.047	0.155	0.109	95b	3.33
leaderboard_math_algebra_hard.exact_match,none	0.02	0.114	0.094	95b	5.83
leaderboard_bbh_boolean_expressions.acc_norm,none	0.936	0.92	-0.016	72b	0.98
leaderboard_math_num_theory_hard.exact_match,none	0	0.058	0.058	95b	0.00
leaderboard_bbh_movie_recommendation.acc_norm,none	0.768	0.78	0.012	95b	1.02
leaderboard_math_counting_and_prob_hard.exact_match,none	0	0.024	0.024	95b	0.00
leaderboard_math_intermediate_algebra_hard.exact_match,none	0	0.004	0.004	95b	0.00
leaderboard_ifeval.prompt_level_strict_acc,none	0.839	0.813	-0.026	72b	0.97
leaderboard_ifeval.inst_level_strict_acc,none	0.888	0.873	-0.016	72b	0.98
leaderboard_ifeval.inst_level_loose_acc,none	0.904	0.891	-0.013	72b	0.99
leaderboard_ifeval.prompt_level_loose_acc,none	0.861	0.839	-0.022	72b	0.97
leaderboard_bbh_snarks.acc_norm,none	0.927	0.904	-0.022	72b	0.98
leaderboard_bbh_web_of_lies.acc_norm,none	0.676	0.616	-0.060	72b	0.91
leaderboard_bbh_penguins_in_a_table.acc_norm,none	0.719	0.767	0.048	95b	1.07
leaderboard_bbh_hyperbaton.acc_norm,none	0.892	0.9	0.008	95b	1.01
leaderboard_bbh_object_counting.acc_norm,none	0.612	0.544	-0.068	72b	0.89
leaderboard_musr_object_placements.acc_norm,none	0.258	0.285	0.027	95b	1.11
leaderboard_bbh_logical_deduction_five_objects.acc_norm,none	0.704	0.592	-0.112	72b	0.84
leaderboard_musr_team_allocation.acc_norm,none	0.456	0.396	-0.060	72b	0.87
leaderboard_bbh_navigate.acc_norm,none	0.832	0.788	-0.044	72b	0.95
leaderboard_bbh_tracking_shuffled_objects_seven_objects.acc_norm,none	0.34	0.304	-0.036	72b	0.89
leaderboard_bbh_formal_fallacies.acc_norm,none	0.776	0.756	-0.020	72b	0.97
all.leaderboard_musr.acc_norm,none	0.419	0.427	0.008	95b	1.02
all.leaderboard_bbh_sports_understanding.acc_norm,none	0.892	0.876	-0.016	72b	0.98
all.leaderboard_bbh_logical_deduction_three_objects.acc_norm,none	0.94	0.928	-0.012	72b	0.99
all.leaderboard_math_geometry_hard.exact_match,none	0	0.008	0.008	95b	0.00
all.leaderboard_gpqa.acc_norm,none	0.375	0.364	-0.011	72b	0.97
all.leaderboard_math_hard.exact_match,none	0.012	0.06	0.048	95b	5.00
all.leaderboard.exact_match,none	0.012	0.06	0.048	95b	5.00
all.leaderboard.prompt_level_loose_acc,none	0.861	0.839	-0.022	72b	0.97
all.leaderboard.prompt_level_strict_acc,none	0.839	0.813	-0.026	72b	0.97
all.leaderboard.inst_level_loose_acc,none	0.904	0.891	-0.013	72b	0.99
all.leaderboard.acc_norm,none	0.641	0.622	-0.020	72b	0.97
all.leaderboard.inst_level_strict_acc,none	0.888	0.873	-0.016	72b	0.98
all.leaderboard.acc,none	0.563	0.522	-0.041	72b	0.93
all.leaderboard_bbh_causal_judgement.acc_norm,none	0.668	0.663	-0.005	72b	0.99
all.leaderboard_bbh_salient_translation_error_detection.acc_norm,none	0.668	0.588	-0.080	72b	0.88
all.leaderboard_gpqa_extended.acc_norm,none	0.372	0.364	-0.007	72b	0.98
all.leaderboard_math_prealgebra_hard.exact_match,none	0.047	0.155	0.109	95b	3.33
all.leaderboard_math_algebra_hard.exact_match,none	0.02	0.114	0.094	95b	5.83
all.leaderboard_bbh_boolean_expressions.acc_norm,none	0.936	0.92	-0.016	72b	0.98
all.leaderboard_math_num_theory_hard.exact_match,none	0	0.058	0.058	95b	0.00
all.leaderboard_bbh_movie_recommendation.acc_norm,none	0.768	0.78	0.012	95b	1.02
all.leaderboard_math_counting_and_prob_hard.exact_match,none	0	0.024	0.024	95b	0.00
all.leaderboard_math_intermediate_algebra_hard.exact_match,none	0	0.004	0.004	95b	0.00
all.leaderboard_ifeval.prompt_level_strict_acc,none	0.839	0.813	-0.026	72b	0.97
all.leaderboard_ifeval.inst_level_strict_acc,none	0.888	0.873	-0.016	72b	0.98
all.leaderboard_ifeval.inst_level_loose_acc,none	0.904	0.891	-0.013	72b	0.99
all.leaderboard_ifeval.prompt_level_loose_acc,none	0.861	0.839	-0.022	72b	0.97
all.leaderboard_bbh_snarks.acc_norm,none	0.927	0.904	-0.022	72b	0.98
all.leaderboard_bbh_web_of_lies.acc_norm,none	0.676	0.616	-0.060	72b	0.91
all.leaderboard_bbh_penguins_in_a_table.acc_norm,none	0.719	0.767	0.048	95b	1.07
all.leaderboard_bbh_hyperbaton.acc_norm,none	0.892	0.9	0.008	95b	1.01
all.leaderboard_bbh_object_counting.acc_norm,none	0.612	0.544	-0.068	72b	0.89
all.leaderboard_musr_object_placements.acc_norm,none	0.258	0.285	0.027	95b	1.11
all.leaderboard_bbh_logical_deduction_five_objects.acc_norm,none	0.704	0.592	-0.112	72b	0.84
all.leaderboard_musr_team_allocation.acc_norm,none	0.456	0.396	-0.060	72b	0.87
all.leaderboard_bbh_navigate.acc_norm,none	0.832	0.788	-0.044	72b	0.95
all.leaderboard_bbh_tracking_shuffled_objects_seven_objects.acc_norm,none	0.34	0.304	-0.036	72b	0.89
all.leaderboard_bbh_formal_fallacies.acc_norm,none	0.776	0.756	-0.020	72b	0.97
all.leaderboard_gpqa_main.acc_norm,none	0.375	0.355	-0.020	72b	0.95
all.leaderboard_bbh_disambiguation_qa.acc_norm,none	0.744	0.772	0.028	95b	1.04
all.leaderboard_bbh_tracking_shuffled_objects_five_objects.acc_norm,none	0.32	0.284	-0.036	72b	0.89
all.leaderboard_bbh_date_understanding.acc_norm,none	0.784	0.764	-0.020	72b	0.97
all.leaderboard_bbh_geometric_shapes.acc_norm,none	0.464	0.412	-0.052	72b	0.89
all.leaderboard_bbh_reasoning_about_colored_objects.acc_norm,none	0.864	0.84	-0.024	72b	0.97
all.leaderboard_musr_murder_mysteries.acc_norm,none	0.548	0.604	0.056	95b	1.10
all.leaderboard_bbh_ruin_names.acc_norm,none	0.888	0.86	-0.028	72b	0.97
all.leaderboard_bbh_logical_deduction_seven_objects.acc_norm,none	0.644	0.664	0.020	95b	1.03
all.leaderboard_bbh.acc_norm,none	0.726	0.701	-0.025	72b	0.97
all.leaderboard_bbh_temporal_sequences.acc_norm,none	0.996	0.968	-0.028	72b	0.97
all.leaderboard_mmlu_pro.acc,none	0.563	0.522	-0.041	72b	0.93
leaderboard_gpqa_main.acc_norm,none	0.375	0.355	-0.020	72b	0.95
leaderboard_bbh_disambiguation_qa.acc_norm,none	0.744	0.772	0.028	95b	1.04
leaderboard_bbh_tracking_shuffled_objects_five_objects.acc_norm,none	0.32	0.284	-0.036	72b	0.89
leaderboard_bbh_date_understanding.acc_norm,none	0.784	0.764	-0.020	72b	0.97
leaderboard_bbh_geometric_shapes.acc_norm,none	0.464	0.412	-0.052	72b	0.89
leaderboard_bbh_reasoning_about_colored_objects.acc_norm,none	0.864	0.84	-0.024	72b	0.97
leaderboard_musr_murder_mysteries.acc_norm,none	0.548	0.604	0.056	95b	1.10
leaderboard_bbh_ruin_names.acc_norm,none	0.888	0.86	-0.028	72b	0.97
leaderboard_bbh_logical_deduction_seven_objects.acc_norm,none	0.644	0.664	0.020	95b	1.03
leaderboard_bbh.acc_norm,none	0.726	0.701	-0.025	72b	0.97
leaderboard_bbh_temporal_sequences.acc_norm,none	0.996	0.968	-0.028	72b	0.97
leaderboard_mmlu_pro.acc,none	0.563	0.522	-0.041	72b	0.93