Instructions to use sethuiyer/CodeCalc-Mistral-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sethuiyer/CodeCalc-Mistral-7B with Transformers:

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

pipe = pipeline("text-generation", model="sethuiyer/CodeCalc-Mistral-7B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("sethuiyer/CodeCalc-Mistral-7B")
model = AutoModelForCausalLM.from_pretrained("sethuiyer/CodeCalc-Mistral-7B")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use sethuiyer/CodeCalc-Mistral-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sethuiyer/CodeCalc-Mistral-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sethuiyer/CodeCalc-Mistral-7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/sethuiyer/CodeCalc-Mistral-7B

SGLang

How to use sethuiyer/CodeCalc-Mistral-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 "sethuiyer/CodeCalc-Mistral-7B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sethuiyer/CodeCalc-Mistral-7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "sethuiyer/CodeCalc-Mistral-7B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sethuiyer/CodeCalc-Mistral-7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use sethuiyer/CodeCalc-Mistral-7B with Docker Model Runner:
```
docker model run hf.co/sethuiyer/CodeCalc-Mistral-7B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

CodeCalc-Mistral-7B

CodeCalc

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

Downloads last month: 72

Safetensors

Model size

7B params

Tensor type

BF16

Model tree for sethuiyer/CodeCalc-Mistral-7B

upaya07/Arithmo2-Mistral-7B

uukuguy/speechless-code-mistral-7b-v1.0

Merge model

this model

Quantizations

3 models

Evaluation results

normalized accuracy on AI2 Reasoning Challenge (25-Shot)
test set Open LLM Leaderboard

61.950
normalized accuracy on HellaSwag (10-Shot)
validation set Open LLM Leaderboard

83.640
accuracy on MMLU (5-Shot)
test set Open LLM Leaderboard

62.780
mc2 on TruthfulQA (0-shot)
validation set Open LLM Leaderboard

47.490
accuracy on Winogrande (5-shot)
validation set Open LLM Leaderboard

78.300
accuracy on GSM8k (5-shot)
test set Open LLM Leaderboard

63.530