sethuiyer_-_CodeCalc-Mistral-7B-gguf

RichardErkhov/sethuiyer_-_CodeCalc-Mistral-7B-gguf

sethuiyer_-_CodeCalc-Mistral-7B-gguf is a machine learning model from RichardErkhov. Use it for the machine learning task on the model card, and read the license before you ship it in a product.

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At a glance

Access: Public
Created: Sep 8, 2024
Updated: Sep 8, 2024
SHA: 5ea6c73b

Model card

From the Hugging Face model README

Quantization made by Richard Erkhov.

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CodeCalc-Mistral-7B - GGUF

Model creator: https://huggingface.co/sethuiyer/
Original model: https://huggingface.co/sethuiyer/CodeCalc-Mistral-7B/

Name	Quant method	Size
CodeCalc-Mistral-7B.Q2_K.gguf	Q2_K	2.53GB
CodeCalc-Mistral-7B.IQ3_XS.gguf	IQ3_XS	2.81GB
CodeCalc-Mistral-7B.IQ3_S.gguf	IQ3_S	2.96GB
CodeCalc-Mistral-7B.Q3_K_S.gguf	Q3_K_S	2.95GB
CodeCalc-Mistral-7B.IQ3_M.gguf	IQ3_M	3.06GB
CodeCalc-Mistral-7B.Q3_K.gguf	Q3_K	3.28GB
CodeCalc-Mistral-7B.Q3_K_M.gguf	Q3_K_M	3.28GB
CodeCalc-Mistral-7B.Q3_K_L.gguf	Q3_K_L	3.56GB
CodeCalc-Mistral-7B.IQ4_XS.gguf	IQ4_XS	3.67GB
CodeCalc-Mistral-7B.Q4_0.gguf	Q4_0	3.83GB
CodeCalc-Mistral-7B.IQ4_NL.gguf	IQ4_NL	3.87GB
CodeCalc-Mistral-7B.Q4_K_S.gguf	Q4_K_S	3.86GB
CodeCalc-Mistral-7B.Q4_K.gguf	Q4_K	4.07GB
CodeCalc-Mistral-7B.Q4_K_M.gguf	Q4_K_M	4.07GB
CodeCalc-Mistral-7B.Q4_1.gguf	Q4_1	4.24GB
CodeCalc-Mistral-7B.Q5_0.gguf	Q5_0	4.65GB
CodeCalc-Mistral-7B.Q5_K_S.gguf	Q5_K_S	4.65GB
CodeCalc-Mistral-7B.Q5_K.gguf	Q5_K	4.78GB
CodeCalc-Mistral-7B.Q5_K_M.gguf	Q5_K_M	4.78GB
CodeCalc-Mistral-7B.Q5_1.gguf	Q5_1	5.07GB
CodeCalc-Mistral-7B.Q6_K.gguf	Q6_K	5.53GB
CodeCalc-Mistral-7B.Q8_0.gguf	Q8_0	7.17GB

Original model description:

language:

en license: apache-2.0 library_name: transformers tags:
mergekit
merge base_model:
uukuguy/speechless-code-mistral-7b-v1.0
upaya07/Arithmo2-Mistral-7B pipeline_tag: text-generation model-index:
name: sethuiyer/CodeCalc-Mistral-7B results:
- task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics:
  - type: acc_norm value: 61.95 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sethuiyer/CodeCalc-Mistral-7B name: Open LLM Leaderboard
- task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics:
  - type: acc_norm value: 83.64 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sethuiyer/CodeCalc-Mistral-7B name: Open LLM Leaderboard
- task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics:
  - type: acc value: 62.78 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sethuiyer/CodeCalc-Mistral-7B name: Open LLM Leaderboard
- task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics:
  - type: mc2 value: 47.49 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sethuiyer/CodeCalc-Mistral-7B name: Open LLM Leaderboard
- task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics:
  - type: acc value: 78.3 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sethuiyer/CodeCalc-Mistral-7B name: Open LLM Leaderboard
- task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics:
  - type: acc value: 63.53 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sethuiyer/CodeCalc-Mistral-7B name: Open LLM Leaderboard

CodeCalc-Mistral-7B

Configuration

The following YAML configuration was used to produce this model:


base_model: uukuguy/speechless-code-mistral-7b-v1.0
dtype: bfloat16
merge_method: ties
models:
- model: uukuguy/speechless-code-mistral-7b-v1.0
- model: upaya07/Arithmo2-Mistral-7B
  parameters:
    density:  [0.25, 0.35, 0.45, 0.35, 0.25]
    weight: [0.1, 0.25, 0.5, 0.25, 0.1]
parameters:
  int8_mask: true

Evaluation

T	Model	Average	ARC	HellaSwag	MMLU	TruthfulQA	Winogrande	GSM8K
🔍	sethuiyer/CodeCalc-Mistral-7B	66.33	61.95	83.64	62.78	47.79	78.3	63.53
📉	uukuguy/speechless-code-mistral-7b-v1.0	63.6	61.18	83.77	63.4	47.9	78.37	47.01

The merge appears to be successful, especially considering the substantial improvement in the GSM8K benchmark while maintaining comparable performance on other metrics.

Usage

Alpaca Instruction Format and Divine Intellect preset.

You are an intelligent programming assistant.

### Instruction:
Implement a linked list in C++

### Response:

Preset:

temperature: 1.31
top_p: 0.14
repetition_penalty: 1.17
top_k: 49

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	66.33
AI2 Reasoning Challenge (25-Shot)	61.95
HellaSwag (10-Shot)	83.64
MMLU (5-Shot)	62.78
TruthfulQA (0-shot)	47.79
Winogrande (5-shot)	78.30
GSM8k (5-shot)	63.53

Created: Sep 8, 2024
Updated: Sep 8, 2024