license: apache-2.0
library_name: transformers
tags:
- mergekit
- merge
base_model:
- mistralai/Mistral-7B-Instruct-v0.2
model-index:
- name: bigstral-12b-32k
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: 41.94
name: strict accuracy
source:
url: >-
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=abacusai/bigstral-12b-32k
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: 25.56
name: normalized accuracy
source:
url: >-
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=abacusai/bigstral-12b-32k
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: 0.98
name: exact match
source:
url: >-
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=abacusai/bigstral-12b-32k
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: 5.7
name: acc_norm
source:
url: >-
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=abacusai/bigstral-12b-32k
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: 15.86
name: acc_norm
source:
url: >-
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=abacusai/bigstral-12b-32k
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: 18.24
name: accuracy
source:
url: >-
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=abacusai/bigstral-12b-32k
name: Open LLM Leaderboard
bigstral-12b-32k
ollama run ehartford/bigstral
I reached for SOLAR but realized that its only 4k... Decided to make my own 32k version.
This is mistralai/Mistral-7B-Instruct-v0.2 with layers interleaved to create a larger 12b model.
This is a merge of pre-trained language models created using mergekit.
Prompt Format
<s>[INST] What is your favourite condiment? [/INST]
Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!</s>
[INST] Do you have dangerously spicy mayonnaise recipes? [/INST]
Example Output
give me some code that is very simple in javascript but would be very difficult in ANSI-C
In JavaScript, functions are first-class values, which means you can assign them to variables, pass them as arguments to other functions, and even nest them inside other functions. This leads to concise and expressive code.
Here's an example of a simple recursive function that calculates the factorial of a given number in JavaScript:
const factorial = n => n === 0 ? 1 : n * factorial(n - 1); console.log(factorial(5)); // Output: 120Now, let's try to write an equivalent code snippet in ANSI-C with similar functionality:
#include <stdio.h> #define MAX_RECURSION_DEPTH 1000 int factorial(int n) { if (n == 0) return 1; else { int result; result = factorial(--n); if (++depth > MAX_RECURSION_DEPTH) while(1){} // Recursion stack overflow return n * result; } } int main() { int i, result; int n = 5; int depth = 0; result = factorial(n); printf("Factorial of %d is: %d\n", n, result); return 0; }As you can see, the ANSI-C code becomes more verbose and has several disadvantages compared to JavaScript:
- Need to define a maximum recursion depth using a constant or an array index, which can lead to hardcoded limits and potential stack overflow issues.
- The use of preprocessor directives (#define) for defining functions and macros can make the code less readable and harder to understand.
- The lack of closures in ANSI-C requires using static variables or passing arguments as arguments to nested functions, making the code more verbose and less concise.
Merge Details
Merge Method
This model was merged using the passthrough merge method.
Models Merged
The following models were included in the merge:
Configuration
The following YAML configuration was used to produce this model:
dtype: float16
merge_method: passthrough
slices:
- sources:
- layer_range: [0, 8]
model: mistralai/Mistral-7B-Instruct-v0.2
- sources:
- layer_range: [4, 12]
model: mistralai/Mistral-7B-Instruct-v0.2
- sources:
- layer_range: [8, 16]
model: mistralai/Mistral-7B-Instruct-v0.2
- sources:
- layer_range: [12, 20]
model: mistralai/Mistral-7B-Instruct-v0.2
- sources:
- layer_range: [16, 24]
model: mistralai/Mistral-7B-Instruct-v0.2
- sources:
- layer_range: [20, 28]
model: mistralai/Mistral-7B-Instruct-v0.2
- sources:
- layer_range: [24, 32]
model: mistralai/Mistral-7B-Instruct-v0.2
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 18.05 |
| IFEval (0-Shot) | 41.94 |
| BBH (3-Shot) | 25.56 |
| MATH Lvl 5 (4-Shot) | 0.98 |
| GPQA (0-shot) | 5.70 |
| MuSR (0-shot) | 15.86 |
| MMLU-PRO (5-shot) | 18.24 |