BossCrafts/Runeforge_Core_mk1-7b

Runforge_Core-7b

This is a merge of pre-trained language models created using mergekit.

Merge Details

Merge Method

This model was merged using the Linear merge method using mistralai/Mistral-7B-v0.3 as a base.

Models Merged

The following models were included in the merge:

• dreamgen/WizardLM-2-7B
• uukuguy/speechless-code-mistral-7b-v2.0

Configuration

The following YAML configuration was used to produce this model:

# Clean 3-way dense merge rebuild for runeforge_core-7b
models:
  - model: mistralai/Mistral-7B-v0.3
    parameters:
      weight: 0.4
  - model: dreamgen/WizardLM-2-7B
    parameters:
      weight: 0.3
  - model: uukuguy/speechless-code-mistral-7b-v2.0
    parameters:
      weight: 0.3
merge_method: linear
base_model: mistralai/Mistral-7B-v0.3
dtype: float16
out_dtype: float16

Evaluation

Setup

• Date: 2026-03-14
• Runtime: local GPU inference in WSL
• Loader: Transformers/Unsloth with 4-bit quantization (load_in_4bit)
• Benchmarks:
  • ARC-Challenge (multiple-choice)
  • HellaSwag (multiple-choice)
  • Winogrande XL (multiple-choice)
  • TruthfulQA MC1 (multiple-choice)
• Metric: Accuracy per benchmark and macro average across the four tasks

Primary Comparison (200 samples per benchmark)

Model	ARC	HellaSwag	Winogrande	TruthfulQA MC1	Macro Avg
runeforge_core-7b (this model)	0.7650	0.7050	0.6000	0.5800	0.6625
mistral-7b baseline	0.7000	0.6000	0.4600	0.5900	0.5875

Interpretation: runeforge_core-7b outperformed the local Mistral baseline by +0.0750 macro accuracy on this evaluation run.

Expanded Comparison (30 samples per benchmark)

Model	ARC	HellaSwag	Winogrande	TruthfulQA MC1	Macro Avg
runeforge_core-7b (this model)	0.8000	0.7333	0.5000	0.4333	0.6167
mistral-7b baseline	0.7000	0.6000	0.4667	0.5000	0.5667
speechless-code-mistral-7b-v2.0	0.6000	0.3000	0.5333	0.6000	0.5083
dreamgen/WizardLM-2-7B	0.2000	0.2333	0.6667	0.7667	0.4667
runeforge_mk1_merged_from_7922	0.0000	0.0000	0.0000	0.0000	0.0000

Note: the expanded table uses a smaller sample size and is more variance-prone; use the 200-sample comparison as the primary signal.

Coding Sanity Check (Executable)

A separate executable coding sanity check (5 unit-tested tasks) was also run:

Model	Passes	Total	Pass Rate
runeforge_core-7b (this model)	5	5	1.00
runeforge_mk1_merged_from_7922	0	5	0.00

Reproducibility Files

Repository-relative references (from this model folder):

• ../Making_Runeforge/evaluate_general_models.py
• ../Making_Runeforge/evaluate_coding_exec.py
• ../Making_Runeforge/eval_general_runeforge_core_200.json
• ../Making_Runeforge/eval_general_mistral_base_200.json
• ../Making_Runeforge/eval_general_leaderboard.json
• ../Making_Runeforge/runeforge_coding_exec_eval.json

Intended Use

• General-purpose assistant and instruction-following use cases.
• Strong performance on local multiple-choice reasoning benchmarks relative to the local Mistral baseline used in this project.
• Suitable as a base for additional task-specific fine-tuning where broad instruction quality is desired.

Limitations

• Reported metrics are from local, sampled benchmark runs (not full official leaderboard submissions).
• Quantized inference (load_in_4bit) was used for evaluation; scores may shift under different precision/runtime setups.
• Expanded 5-model comparison used 30 samples per benchmark and should be treated as directional.
• A separate merged artifact (runeforge_mk1_merged_from_7922) showed severe degradation (near-zero on sampled general benchmarks and 0/5 on coding executable sanity checks).

Evaluation Notes

• The 200-sample comparison is the primary result set for this card.
• The 30-sample expanded table is included for breadth across additional local peer models.
• All benchmark scripts and JSON outputs are listed above for reproducibility.