Instructions to use codefuse-ai/CodeFuse-DeepSeek-33B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use codefuse-ai/CodeFuse-DeepSeek-33B with Transformers:

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

pipe = pipeline("text-generation", model="codefuse-ai/CodeFuse-DeepSeek-33B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("codefuse-ai/CodeFuse-DeepSeek-33B")
model = AutoModelForCausalLM.from_pretrained("codefuse-ai/CodeFuse-DeepSeek-33B", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use codefuse-ai/CodeFuse-DeepSeek-33B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "codefuse-ai/CodeFuse-DeepSeek-33B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "codefuse-ai/CodeFuse-DeepSeek-33B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/codefuse-ai/CodeFuse-DeepSeek-33B

SGLang

How to use codefuse-ai/CodeFuse-DeepSeek-33B 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-DeepSeek-33B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "codefuse-ai/CodeFuse-DeepSeek-33B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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-DeepSeek-33B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "codefuse-ai/CodeFuse-DeepSeek-33B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use codefuse-ai/CodeFuse-DeepSeek-33B with Docker Model Runner:
```
docker model run hf.co/codefuse-ai/CodeFuse-DeepSeek-33B
```

codefuse-admin commited on Jan 12, 2024

Commit

b5959bb

verified ·

1 Parent(s): 13b678f

Update README.md

Browse files

Files changed (1) hide show

README.md +11 -18

README.md CHANGED Viewed

@@ -1,21 +1,14 @@
 ---
-frameworks:
-- Pytorch
-license: apache-2.0
-tasks:
-- text-generation
 ---
 # Model Card for CodeFuse-DeepSeek-33B
-<p align="center">
-    <img src="https://modelscope.cn/api/v1/models/codefuse-ai/CodeFuse-DeepSeek-33B/repo?Revision=master&FilePath=LOGO.jpg&View=true" width="800"/>
-<p>
 [[中文]](#chinese)    [[English]](#english)
-#### Clone with HTTP
-```bash
- git clone https://www.modelscope.cn/codefuse-ai/CodeFuse-DeepSeek-33B.git
-```
 <a id="english"></a>
@@ -27,7 +20,7 @@ CodeFuse-DeepSeek-33B is a 33B Code-LLM finetuned by QLoRA on multiple code-rela
 ## News and Updates
-🔥🔥🔥 2024-01-12 CodeFuse-DeepSeek-33B has been released, achiving a pass@1 (greedy decoding) score of 78.65% on HumanEval.
   🔥🔥 2023-11-10 CodeFuse-CodeGeeX2-6B has been released, achieving a pass@1 (greedy decoding) score of 45.12% on HumanEval, which is a 9.22% increase compared to CodeGeeX2 35.9%.
@@ -39,7 +32,7 @@ CodeFuse-DeepSeek-33B is a 33B Code-LLM finetuned by QLoRA on multiple code-rela
 🔥🔥🔥 2023-09-26 We are pleased to announce the release of the [4-bit quantized version](https://modelscope.cn/models/codefuse-ai/CodeFuse-CodeLlama-34B-4bits/summary) of [CodeFuse-CodeLlama-34B](https://modelscope.cn/models/codefuse-ai/CodeFuse-CodeLlama-34B/summary). Despite the quantization process, the model still achieves a remarkable 73.8% accuracy (greedy decoding) on the HumanEval pass@1 metric.
-🔥🔥🔥 2023-09-11 [CodeFuse-CodeLlama34B](https://modelscope.cn/models/codefuse-ai/CodeFuse-CodeLlama-34B/summary) has achived 74.4% of pass@1 (greedy decoding) on HumanEval, which is SOTA results for openspurced LLMs at present.
 <br>
@@ -133,10 +126,10 @@ In this format, the system section is optional and the conversation can be eithe
 ```python
 import torch
-from modelscope import AutoTokenizer, AutoModelForCausalLM, GenerationConfig, snapshot_download
 def load_model_tokenizer(model_path):
-    tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True, use_fast=False, legacy=False)
     tokenizer.eos_token = "<｜end▁of▁sentence｜>"
     tokenizer.pad_token = "<｜end▁of▁sentence｜>"
     tokenizer.eos_token_id = tokenizer.convert_tokens_to_ids(tokenizer.eos_token)
@@ -295,10 +288,10 @@ User prompt...
 ```python
 import torch
-from modelscope import AutoTokenizer, AutoModelForCausalLM, GenerationConfig, snapshot_download
 def load_model_tokenizer(model_path):
-    tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True, use_fast=False, legacy=False)
     tokenizer.eos_token = "<｜end▁of▁sentence｜>"
     tokenizer.pad_token = "<｜end▁of▁sentence｜>"
     tokenizer.eos_token_id = tokenizer.convert_tokens_to_ids(tokenizer.eos_token)

 ---
+license: other
+tasks:
+- code-generation
 ---
 # Model Card for CodeFuse-DeepSeek-33B
+![logo](LOGO.jpg)
 [[中文]](#chinese)    [[English]](#english)
 <a id="english"></a>
 ## News and Updates
+🔥🔥🔥 2024-01-12 CodeFuse-DeepSeek-33B has been released, achieving a pass@1 (greedy decoding) score of 78.65% on HumanEval.
   🔥🔥 2023-11-10 CodeFuse-CodeGeeX2-6B has been released, achieving a pass@1 (greedy decoding) score of 45.12% on HumanEval, which is a 9.22% increase compared to CodeGeeX2 35.9%.
 🔥🔥🔥 2023-09-26 We are pleased to announce the release of the [4-bit quantized version](https://modelscope.cn/models/codefuse-ai/CodeFuse-CodeLlama-34B-4bits/summary) of [CodeFuse-CodeLlama-34B](https://modelscope.cn/models/codefuse-ai/CodeFuse-CodeLlama-34B/summary). Despite the quantization process, the model still achieves a remarkable 73.8% accuracy (greedy decoding) on the HumanEval pass@1 metric.
+🔥🔥🔥 2023-09-11 [CodeFuse-CodeLlama34B](https://modelscope.cn/models/codefuse-ai/CodeFuse-CodeLlama-34B/summary) has achieved 74.4% of pass@1 (greedy decoding) on HumanEval, which is SOTA results for openspurced LLMs at present.
 <br>
 ```python
 import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig
 def load_model_tokenizer(model_path):
+    tokenizer = AutoTokenizer.from_pretrained("codefuse-ai/CodeFuse-DeepSeek-33B", trust_remote_code=True, use_fast=False, legacy=False)
     tokenizer.eos_token = "<｜end▁of▁sentence｜>"
     tokenizer.pad_token = "<｜end▁of▁sentence｜>"
     tokenizer.eos_token_id = tokenizer.convert_tokens_to_ids(tokenizer.eos_token)
 ```python
 import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig
 def load_model_tokenizer(model_path):
+    tokenizer = AutoTokenizer.from_pretrained("codefuse-ai/CodeFuse-DeepSeek-33B", trust_remote_code=True, use_fast=False, legacy=False)
     tokenizer.eos_token = "<｜end▁of▁sentence｜>"
     tokenizer.pad_token = "<｜end▁of▁sentence｜>"
     tokenizer.eos_token_id = tokenizer.convert_tokens_to_ids(tokenizer.eos_token)