Instructions to use hpcgroup/hpc-coder-v2-6.7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use hpcgroup/hpc-coder-v2-6.7b with Transformers:

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

pipe = pipeline("text-generation", model="hpcgroup/hpc-coder-v2-6.7b")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("hpcgroup/hpc-coder-v2-6.7b")
model = AutoModelForCausalLM.from_pretrained("hpcgroup/hpc-coder-v2-6.7b", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use hpcgroup/hpc-coder-v2-6.7b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "hpcgroup/hpc-coder-v2-6.7b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "hpcgroup/hpc-coder-v2-6.7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/hpcgroup/hpc-coder-v2-6.7b

SGLang

How to use hpcgroup/hpc-coder-v2-6.7b 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 "hpcgroup/hpc-coder-v2-6.7b" \
    --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": "hpcgroup/hpc-coder-v2-6.7b",
		"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 "hpcgroup/hpc-coder-v2-6.7b" \
        --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": "hpcgroup/hpc-coder-v2-6.7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use hpcgroup/hpc-coder-v2-6.7b with Docker Model Runner:
```
docker model run hf.co/hpcgroup/hpc-coder-v2-6.7b
```

daniellnichols commited on Jul 19, 2024

Commit

deb2a0e

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1 Parent(s): c751db2

Update README.md

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@@ -5,6 +5,13 @@ tags:
 - hpc
 - parallel
 - axonn
 ---
 # HPC-Coder-v2
@@ -34,3 +41,9 @@ Below is an instruction that describes a task. Write a response that appropriate
 ```

 - hpc
 - parallel
 - axonn
+datasets:
+- hpcgroup/hpc-instruct
+- ise-uiuc/Magicoder-OSS-Instruct-75K
+- nickrosh/Evol-Instruct-Code-80k-v1
+language:
+- en
+pipeline_tag: text-generation
 ---
 # HPC-Coder-v2
 ```
+## Quantized Models
+4 and 8 bit quantized weights are available in the GGUF format for use with [llama.cpp](https://github.com/ggerganov/llama.cpp).
+The 4 bit model requires ~3.8 GB memory and can be found [here](https://huggingface.co/hpcgroup/hpc-coder-v2-6.7b-Q4_K_S-GGUF).
+The 8 bit model requires ~7.1 GB memory and can be found [here](https://huggingface.co/hpcgroup/hpc-coder-v2-6.7b-Q8_0-GGUF).
+Further information on how to use them with llama.cpp can be found in [its documentation](https://github.com/ggerganov/llama.cpp).