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
```

sethuiyer commited on Feb 19, 2024

Commit

1d41dcb

verified ·

1 Parent(s): 9e3226e

Update README.md

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@@ -8,20 +8,10 @@ tags:
 - merge
 ---
-# merge
 This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
-## Merge Details
-### Merge Method
-This model was merged using the [TIES](https://arxiv.org/abs/2306.01708) merge method using [uukuguy/speechless-code-mistral-7b-v1.0](https://huggingface.co/uukuguy/speechless-code-mistral-7b-v1.0) as a base.
-### Models Merged
-The following models were included in the merge:
-* [upaya07/Arithmo2-Mistral-7B](https://huggingface.co/upaya07/Arithmo2-Mistral-7B)
 ### Configuration
 The following YAML configuration was used to produce this model:

 - merge
 ---
+# CodeCalc-Mistral-7B
 This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
 ### Configuration
 The following YAML configuration was used to produce this model: