RichardErkhov/abideen_-_MonarchCoder-7B-gguf

Quantization made by Richard Erkhov.

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MonarchCoder-7B - GGUF

• Model creator: https://huggingface.co/abideen/
• Original model: https://huggingface.co/abideen/MonarchCoder-7B/

Name	Quant method	Size
MonarchCoder-7B.Q2_K.gguf	Q2_K	2.01GB
MonarchCoder-7B.IQ3_XS.gguf	IQ3_XS	2.81GB
MonarchCoder-7B.IQ3_S.gguf	IQ3_S	2.96GB
MonarchCoder-7B.Q3_K_S.gguf	Q3_K_S	2.95GB
MonarchCoder-7B.IQ3_M.gguf	IQ3_M	3.06GB
MonarchCoder-7B.Q3_K.gguf	Q3_K	2.59GB
MonarchCoder-7B.Q3_K_M.gguf	Q3_K_M	3.28GB
MonarchCoder-7B.Q3_K_L.gguf	Q3_K_L	3.56GB
MonarchCoder-7B.IQ4_XS.gguf	IQ4_XS	3.67GB
MonarchCoder-7B.Q4_0.gguf	Q4_0	3.83GB
MonarchCoder-7B.IQ4_NL.gguf	IQ4_NL	3.87GB
MonarchCoder-7B.Q4_K_S.gguf	Q4_K_S	3.86GB
MonarchCoder-7B.Q4_K.gguf	Q4_K	4.07GB
MonarchCoder-7B.Q4_K_M.gguf	Q4_K_M	4.07GB
MonarchCoder-7B.Q4_1.gguf	Q4_1	4.24GB
MonarchCoder-7B.Q5_0.gguf	Q5_0	4.65GB
MonarchCoder-7B.Q5_K_S.gguf	Q5_K_S	4.65GB
MonarchCoder-7B.Q5_K.gguf	Q5_K	4.78GB
MonarchCoder-7B.Q5_K_M.gguf	Q5_K_M	4.78GB
MonarchCoder-7B.Q5_1.gguf	Q5_1	5.07GB
MonarchCoder-7B.Q6_K.gguf	Q6_K	5.53GB
MonarchCoder-7B.Q8_0.gguf	Q8_0	7.17GB

Original model description:

license: apache-2.0 tags: - merge - mergekit - lazymergekit - Syed-Hasan-8503/Tess-Coder-7B-Mistral-v1.0 - mlabonne/AlphaMonarch-7B base_model: - Syed-Hasan-8503/Tess-Coder-7B-Mistral-v1.0 - mlabonne/AlphaMonarch-7B model-index: - name: MonarchCoder-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: 68.52 name: normalized accuracy source: url: >- https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=abideen/MonarchCoder-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: 87.3 name: normalized accuracy source: url: >- https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=abideen/MonarchCoder-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: 64.65 name: accuracy source: url: >- https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=abideen/MonarchCoder-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: 61.21 source: url: >- https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=abideen/MonarchCoder-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: 80.19 name: accuracy source: url: >- https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=abideen/MonarchCoder-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: 65.13 name: accuracy source: url: >- https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=abideen/MonarchCoder-7B name: Open LLM Leaderboard language: - en library_name: transformers

MonarchCoder-7B

MonarchCoder-7B is a slerp merge of the following models using LazyMergekit:

• Syed-Hasan-8503/Tess-Coder-7B-Mistral-v1.0
• mlabonne/AlphaMonarch-7B

The main aim behind creating this model is to create a model that performs well in reasoning, conversation, and coding. AlphaMonarch pperforms amazing on reasoning and conversation tasks. Merging AlphaMonarch with a coding model yielded MonarchCoder-7B which performs better on OpenLLM, Nous, and HumanEval benchmark. Although MonarchCoder-2x7B performs better than MonarchCoder-7B.

🏆 Evaluation results

|             Metric              |MonarchCoder-Moe-2x7B||MonarchCoder-7B||AlphaMonarch|
|---------------------------------|---------------------|-----------------|------------|
|Avg.                             |       74.23         |      71.17      |   75.99    |
|HumanEval                        |       41.15         |      39.02      |   34.14    |
|HumanEval+                       |       29.87         |      31.70      |   29.26    |
|MBPP                             |       40.60         |       *         |     *      |
|AI2 Reasoning Challenge (25-Shot)|       70.99         |      68.52      |   73.04    |
|HellaSwag (10-Shot)              |       87.99         |      87.30      |   89.18    |
|MMLU (5-Shot)                    |       65.11         |      64.65      |   64.40    |
|TruthfulQA (0-shot)              |       71.25         |      61.21      |   77.91    |
|Winogrande (5-shot)              |       80.66         |      80.19     .|   84.69    |
|GSM8k (5-shot)           .       |       69.37         |      65.13      |   66.72    | 

🧩 Configuration

slices:
  - sources:
      - model: Syed-Hasan-8503/Tess-Coder-7B-Mistral-v1.0
        layer_range: [0, 32]
      - model: mlabonne/AlphaMonarch-7B
        layer_range: [0, 32]
merge_method: slerp
base_model: mlabonne/AlphaMonarch-7B
parameters:
  t:
    - filter: self_attn
      value: [0, 0.5, 0.3, 0.7, 1]
    - filter: mlp
      value: [1, 0.5, 0.7, 0.3, 0]
    - value: 0.5
dtype: bfloat16

💻 Usage

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "abideen/MonarchCoder-7B"
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"])