Instructions to use BossCrafts/Runeforge_Core_mk1-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use BossCrafts/Runeforge_Core_mk1-7b with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="BossCrafts/Runeforge_Core_mk1-7b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("BossCrafts/Runeforge_Core_mk1-7b")
model = AutoModelForCausalLM.from_pretrained("BossCrafts/Runeforge_Core_mk1-7b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use BossCrafts/Runeforge_Core_mk1-7b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "BossCrafts/Runeforge_Core_mk1-7b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "BossCrafts/Runeforge_Core_mk1-7b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/BossCrafts/Runeforge_Core_mk1-7b

SGLang

How to use BossCrafts/Runeforge_Core_mk1-7b with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "BossCrafts/Runeforge_Core_mk1-7b" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "BossCrafts/Runeforge_Core_mk1-7b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "BossCrafts/Runeforge_Core_mk1-7b" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "BossCrafts/Runeforge_Core_mk1-7b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use BossCrafts/Runeforge_Core_mk1-7b with Docker Model Runner:
```
docker model run hf.co/BossCrafts/Runeforge_Core_mk1-7b
```

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This repository is publicly accessible, but you have to accept the conditions to access its files and content.

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.

Downloads last month: -

Safetensors

Model size

7B params

Tensor type

F16

Model tree for BossCrafts/Runeforge_Core_mk1-7b

dreamgen/WizardLM-2-7B

mistralai/Mistral-7B-v0.3

uukuguy/speechless-code-mistral-7b-v2.0

Merge model

this model

Paper for BossCrafts/Runeforge_Core_mk1-7b

Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time

Paper • 2203.05482 • Published Mar 10, 2022 • 8