Instructions to use codefuse-ai/CodeFuse-CodeLlama-34B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use codefuse-ai/CodeFuse-CodeLlama-34B with Transformers:

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

pipe = pipeline("text-generation", model="codefuse-ai/CodeFuse-CodeLlama-34B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("codefuse-ai/CodeFuse-CodeLlama-34B")
model = AutoModelForCausalLM.from_pretrained("codefuse-ai/CodeFuse-CodeLlama-34B")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use codefuse-ai/CodeFuse-CodeLlama-34B with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/codefuse-ai/CodeFuse-CodeLlama-34B

SGLang

How to use codefuse-ai/CodeFuse-CodeLlama-34B 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 "codefuse-ai/CodeFuse-CodeLlama-34B" \
    --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": "codefuse-ai/CodeFuse-CodeLlama-34B",
		"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 "codefuse-ai/CodeFuse-CodeLlama-34B" \
        --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": "codefuse-ai/CodeFuse-CodeLlama-34B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use codefuse-ai/CodeFuse-CodeLlama-34B with Docker Model Runner:
```
docker model run hf.co/codefuse-ai/CodeFuse-CodeLlama-34B
```

codefuse-admin commited on Sep 10, 2023

Commit

7b92b5b

1 Parent(s): fbd6063

Update README.md

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README.md +26 -10

README.md CHANGED Viewed

@@ -26,11 +26,19 @@ The context length of finetuning is 4K while it is able to be finetuned by 16k c
 ## Performance
-| Model                         | HumanEval(pass@1) |
-| :---------------------------- | :---------------: |
-| CodeLlama-34b                 |   48.8%(greedy decoding)   |
-| CodeLlama-34b-Python          |   53.7%(greedy decoding)   |
-| **CodeFuse-CodeLlama-34B** | **74.4%**(greedy decoding) |
 <br>
@@ -130,11 +138,19 @@ CodeFuse-CodeLlama34B-MFT 是一个通过QLoRA对基座模型CodeLlama-34b-Pytho
 ## 评测表现(代码)
-| 模型                         | HumanEval(pass@1) |
-| :---------------------------- | :---------------: |
-| CodeLlama-34b                 |   48.8%(greedy decoding)   |
-| CodeLlama-34b-Python          |   53.7%(greedy decoding)   |
-| **CodeFuse-CodeLlama-34B** | **74.4%**(greedy decoding) |
 <br>
 ## Requirements

 ## Performance
+| Model                       | HumanEval(pass@1) |  Date   |
+|:----------------------------|:-----------------:|:-------:|
+| **CodeFuse-CodeLlama-34B**  |     **74.4%**      | 2023.9  |
+| WizardCoder-Python-34B-V1.0 |       73.2%       | 2023.8  |
+| GPT-4(zero-shot)            |       67.0%       | 2023.3  |
+| PanGu-Coder2 15B            |       61.6%       | 2023.8  |
+| CodeLlama-34b-Python        |       53.7%       | 2023.8  |
+| CodeLlama-34b               |       48.8%       | 2023.8  |
+| GPT-3.5(zero-shot)          |       48.1%       | 2022.11 |
+| OctoCoder                   |       46.2%       | 2023.8  |
+| StarCoder-15B               |       33.6%       | 2023.5  |
+| LLaMA 2 70B(zero-shot)      |       29.9%       | 2023.7  |
 <br>
 ## 评测表现(代码)
+| 模型                          | HumanEval(pass@1) |   日期    |
+|:----------------------------|:-----------------:|:-------:|
+| **CodeFuse-CodeLlama-34B**  |     **74.4%**      | 2023.9  |
+| WizardCoder-Python-34B-V1.0 |       73.2%       | 2023.8  |
+| GPT-4(zero-shot)            |       67.0%       | 2023.3  |
+| PanGu-Coder2 15B            |       61.6%       | 2023.8  |
+| CodeLlama-34b-Python        |       53.7%       | 2023.8  |
+| CodeLlama-34b               |       48.8%       | 2023.8  |
+| GPT-3.5(zero-shot)          |       48.1%       | 2022.11 |
+| OctoCoder                   |       46.2%       | 2023.8  |
+| StarCoder-15B               |       33.6%       | 2023.5  |
+| LLaMA 2 70B(zero-shot)      |       29.9%       | 2023.7  |
 <br>
 ## Requirements