library_name: transformers
tags:
- mergekit
- merge
- llama-cpp
- gguf-my-repo
base_model: bunnycore/Qwen2.5-7B-RRP-1M
model-index:
- name: Qwen2.5-7B-RRP-1M
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: 74.81
name: strict accuracy
source:
url: >-
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=bunnycore/Qwen2.5-7B-RRP-1M
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: 35.65
name: normalized accuracy
source:
url: >-
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=bunnycore/Qwen2.5-7B-RRP-1M
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: 28.17
name: exact match
source:
url: >-
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=bunnycore/Qwen2.5-7B-RRP-1M
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: 7.05
name: acc_norm
source:
url: >-
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=bunnycore/Qwen2.5-7B-RRP-1M
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: 15.8
name: acc_norm
source:
url: >-
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=bunnycore/Qwen2.5-7B-RRP-1M
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: 36.29
name: accuracy
source:
url: >-
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=bunnycore/Qwen2.5-7B-RRP-1M
name: Open LLM Leaderboard
Triangle104/Qwen2.5-7B-RRP-1M-Q8_0-GGUF
This model was converted to GGUF format from bunnycore/Qwen2.5-7B-RRP-1M using llama.cpp via the ggml.ai's GGUF-my-repo space.
Refer to the original model card for more details on the model.
Model details:
LoRA trained on a thinking/reasoning and roleplaying dataset and then merged with the Qwen2.5-7B-Instruct-1M model, which supports up to 1 million token context lengths.
What this Model Can Do:
Roleplay: Engage in creative conversations and storytelling! Reasoning: Tackle problems and answer your questions in a logical way (thanks to the LoRA layer). Thinking: Use the tag in your system prompts to activate the model's thinking abilities.
Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
brew install llama.cpp
Invoke the llama.cpp server or the CLI.
CLI:
llama-cli --hf-repo Triangle104/Qwen2.5-7B-RRP-1M-Q8_0-GGUF --hf-file qwen2.5-7b-rrp-1m-q8_0.gguf -p "The meaning to life and the universe is"
Server:
llama-server --hf-repo Triangle104/Qwen2.5-7B-RRP-1M-Q8_0-GGUF --hf-file qwen2.5-7b-rrp-1m-q8_0.gguf -c 2048
Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
git clone https://github.com/ggerganov/llama.cpp
Step 2: Move into the llama.cpp folder and build it with LLAMA_CURL=1 flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
cd llama.cpp && LLAMA_CURL=1 make
Step 3: Run inference through the main binary.
./llama-cli --hf-repo Triangle104/Qwen2.5-7B-RRP-1M-Q8_0-GGUF --hf-file qwen2.5-7b-rrp-1m-q8_0.gguf -p "The meaning to life and the universe is"
or
./llama-server --hf-repo Triangle104/Qwen2.5-7B-RRP-1M-Q8_0-GGUF --hf-file qwen2.5-7b-rrp-1m-q8_0.gguf -c 2048