license: apache-2.0
library_name: transformers
tags:
- general-purpose
- roleplay
- storywriting
- merge
- finetune
- llama-cpp
- gguf-my-repo
base_model: elinas/Chronos-Gold-12B-1.0
model-index:
- name: Chronos-Gold-12B-1.0
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: 31.66
name: strict accuracy
source:
url: >-
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=elinas/Chronos-Gold-12B-1.0
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: 35.91
name: normalized accuracy
source:
url: >-
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=elinas/Chronos-Gold-12B-1.0
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: 4.38
name: exact match
source:
url: >-
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=elinas/Chronos-Gold-12B-1.0
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: 9.06
name: acc_norm
source:
url: >-
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=elinas/Chronos-Gold-12B-1.0
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: 19.42
name: acc_norm
source:
url: >-
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=elinas/Chronos-Gold-12B-1.0
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: 27.98
name: accuracy
source:
url: >-
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=elinas/Chronos-Gold-12B-1.0
name: Open LLM Leaderboard
Triangle104/Chronos-Gold-12B-1.0-Q4_K_S-GGUF
This model was converted to GGUF format from elinas/Chronos-Gold-12B-1.0 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:
Chronos Gold 12B 1.0 is a very unique model that applies to domain areas such as general chatbot functionatliy, roleplay, and storywriting. The model has been observed to write up to 2250 tokens in a single sequence. The model was trained at a sequence length of 16384 (16k) and will still retain the apparent 128k context length from Mistral-Nemo, though it deteriorates over time like regular Nemo does based on the RULER Test
As a result, is recommended to keep your sequence length max at 16384, or you will experience performance degredation.
The base model is mistralai/Mistral-Nemo-Base-2407 which was heavily modified to produce a more coherent model, comparable to much larger models.
Chronos Gold 12B-1.0 re-creates the uniqueness of the original Chronos with significiantly enhanced prompt adherence (following), coherence, a modern dataset, as well as supporting a majority of "character card" formats in applications like SillyTavern.
It went through an iterative and objective merge process as my previous models and was further finetuned on a dataset curated for it.
The specifics of the model will not be disclosed at the time due to dataset ownership.
Instruct Template This model uses ChatML - below is an example. It is a preset in many frontends.
<|im_start|>system A system prompt describing how you'd like your bot to act.<|im_end|> <|im_start|>user Hello there!<|im_end|> <|im_start|>assistant I can assist you or we can discuss other things?<|im_end|> <|im_start|>user I was wondering how transformers work?<|im_end|> <|im_start|>assistant
Sampling Settings Nemo is a bit sensitive to high temperatures, so I use lower. Here are my settings:
Temp - 0.7 (0.9 max) Presence Penalty - 1.0 Repetition Penalty range - 2800 Min P - 0.10
Additional Details This model was created by elinas on discord. Thank you to @kalomaze for providing a model that made this merge possible.
This is one of multiple models to come out in the series by size and model architecture, so look forward to it!
Contact me on Discord for inquiries.
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/Chronos-Gold-12B-1.0-Q4_K_S-GGUF --hf-file chronos-gold-12b-1.0-q4_k_s.gguf -p "The meaning to life and the universe is"
Server:
llama-server --hf-repo Triangle104/Chronos-Gold-12B-1.0-Q4_K_S-GGUF --hf-file chronos-gold-12b-1.0-q4_k_s.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/Chronos-Gold-12B-1.0-Q4_K_S-GGUF --hf-file chronos-gold-12b-1.0-q4_k_s.gguf -p "The meaning to life and the universe is"
or
./llama-server --hf-repo Triangle104/Chronos-Gold-12B-1.0-Q4_K_S-GGUF --hf-file chronos-gold-12b-1.0-q4_k_s.gguf -c 2048