Instructions to use IDEA-CCNL/Ziya-Coding-34B-v1.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use IDEA-CCNL/Ziya-Coding-34B-v1.0 with Transformers:

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

pipe = pipeline("text-generation", model="IDEA-CCNL/Ziya-Coding-34B-v1.0")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("IDEA-CCNL/Ziya-Coding-34B-v1.0")
model = AutoModelForCausalLM.from_pretrained("IDEA-CCNL/Ziya-Coding-34B-v1.0", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use IDEA-CCNL/Ziya-Coding-34B-v1.0 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "IDEA-CCNL/Ziya-Coding-34B-v1.0"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "IDEA-CCNL/Ziya-Coding-34B-v1.0",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/IDEA-CCNL/Ziya-Coding-34B-v1.0

SGLang

How to use IDEA-CCNL/Ziya-Coding-34B-v1.0 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 "IDEA-CCNL/Ziya-Coding-34B-v1.0" \
    --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": "IDEA-CCNL/Ziya-Coding-34B-v1.0",
		"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 "IDEA-CCNL/Ziya-Coding-34B-v1.0" \
        --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": "IDEA-CCNL/Ziya-Coding-34B-v1.0",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use IDEA-CCNL/Ziya-Coding-34B-v1.0 with Docker Model Runner:
```
docker model run hf.co/IDEA-CCNL/Ziya-Coding-34B-v1.0
```

Update README.md

by pskun - opened Nov 3, 2023

base: refs/heads/main

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README.md CHANGED Viewed

@@ -111,6 +111,16 @@ output = tokenizer.batch_decode(generate_ids)[0]
 print(output)
 ```
 ## 引用 Citation
 如果您在您的工作中使用了我们的模型，可以引用我们的[论文](https://arxiv.org/abs/2210.08590)：

 print(output)
 ```
+## 量化 Quantization
+感谢社区优秀的工作，您可以使用社区开发者为Ziya-Coding-34B-v1.0训练的量化版本。
+Thanks to the excellent work of the community, you can use the quantized version trained by community developers for Ziya-Coding-34B-v1.0.
+- [GPTQ](https://huggingface.co/TheBloke/Ziya-Coding-34B-v1.0-GPTQ)
+- [AWQ](https://huggingface.co/TheBloke/Ziya-Coding-34B-v1.0-AWQ)
+- [GGUF](https://huggingface.co/TheBloke/Ziya-Coding-34B-v1.0-GGUF)
 ## 引用 Citation
 如果您在您的工作中使用了我们的模型，可以引用我们的[论文](https://arxiv.org/abs/2210.08590)：