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")

Inference
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 20, 2024

Commit

deb9a61

verified ·

1 Parent(s): 4a4d22f

Update README.md

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README.md CHANGED Viewed

@@ -128,6 +128,28 @@ pipeline_tag: text-generation
 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:

 This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
+## Usage
+Alpaca Instruction Format and [Divine Intellect](https://raw.githubusercontent.com/oobabooga/text-generation-webui/ae8cd449ae3e0236ecb3775892bb1eea23f9ed68/presets/Divine%20Intellect.yaml) preset.
+```
+You are an intelligent programming assistant.
+### Instruction:
+Implement a linked list in C++
+### Response:
+```
+Preset:
+```text
+temperature: 1.31
+top_p: 0.14
+repetition_penalty: 1.17
+top_k: 49
+```
 ### Configuration
 The following YAML configuration was used to produce this model: