Instructions to use neph1/Qwen2.5-Coder-7B-Instruct-Unity with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use neph1/Qwen2.5-Coder-7B-Instruct-Unity with Transformers:

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

pipe = pipeline("text-generation", model="neph1/Qwen2.5-Coder-7B-Instruct-Unity")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

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

tokenizer = AutoTokenizer.from_pretrained("neph1/Qwen2.5-Coder-7B-Instruct-Unity")
model = AutoModelForCausalLM.from_pretrained("neph1/Qwen2.5-Coder-7B-Instruct-Unity", 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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use neph1/Qwen2.5-Coder-7B-Instruct-Unity with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "neph1/Qwen2.5-Coder-7B-Instruct-Unity"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "neph1/Qwen2.5-Coder-7B-Instruct-Unity",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/neph1/Qwen2.5-Coder-7B-Instruct-Unity

SGLang

How to use neph1/Qwen2.5-Coder-7B-Instruct-Unity 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 "neph1/Qwen2.5-Coder-7B-Instruct-Unity" \
    --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": "neph1/Qwen2.5-Coder-7B-Instruct-Unity",
		"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 "neph1/Qwen2.5-Coder-7B-Instruct-Unity" \
        --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": "neph1/Qwen2.5-Coder-7B-Instruct-Unity",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Desktop
Docker Model Runner
How to use neph1/Qwen2.5-Coder-7B-Instruct-Unity with Docker Model Runner:
```
docker model run hf.co/neph1/Qwen2.5-Coder-7B-Instruct-Unity
```

neph1 commited on Dec 15, 2024

Commit

6427b16

verified ·

1 Parent(s): e8f46b4

Update README.md

Browse files

Files changed (1) hide show

README.md +26 -14

README.md CHANGED Viewed

@@ -19,8 +19,12 @@ tags:
 Qwen2.5-Coder-7B-Instruct trained on a merged dataset of Unity3d q&a from these two datasets:
 [ibranze/codellama_unity3d_v2](https://huggingface.co/datasets/ibranze/codellama_unity3d_v2) (Full)
-[Hypersniper/unity_api_2022_3](https://huggingface.co/datasets/Hypersniper/unity_api_2022_3) (5%)
 15062 rows in total with a 10% validation split.
 Trained with native chat template (minus tools usage, see this issue: https://github.com/unslothai/unsloth/issues/1053). With a little superficial testing done, it seems to respond well to the mistral template.
@@ -42,7 +46,7 @@ This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unsloth
 # Training details
-About 1 epoch.
 Rank: 128
@@ -74,15 +78,23 @@ TrainingArguments(
 Step 	Training Loss 	Validation Loss
-10 	2.097300 	1.165832
-20 	1.058100 	1.013441
-30 	0.898500 	0.969640
-40 	0.866600 	0.943687
-50 	0.847300 	0.926879
-60 	0.838200 	0.903914
-70 	0.797600 	0.888580
-80 	0.777700 	0.873389
-90 	0.793900 	0.859501
-100 	0.725500 	0.846339
-110 	0.739400 	0.843786
-120 	0.675200 	0.833775

 Qwen2.5-Coder-7B-Instruct trained on a merged dataset of Unity3d q&a from these two datasets:
 [ibranze/codellama_unity3d_v2](https://huggingface.co/datasets/ibranze/codellama_unity3d_v2) (Full)
+[Hypersniper/unity_api_2022_3](https://huggingface.co/datasets/Hypersniper/unity_api_2022_3) (10%)
+preview 2:
+26210 rows, of which ca 1000 are from my own multi response dataset
+preview 1:
 15062 rows in total with a 10% validation split.
 Trained with native chat template (minus tools usage, see this issue: https://github.com/unslothai/unsloth/issues/1053). With a little superficial testing done, it seems to respond well to the mistral template.
 # Training details
+About 1.5 epochs. It's probably a bit overfitting and I should introduce some general coding questions to my validation set to ensure it doesn't lose too much general performance.
 Rank: 128
 Step 	Training Loss 	Validation Loss
+20 	2.043000 	1.197104
+40 	1.087300 	0.933553
+60 	0.942200 	0.890801
+80 	0.865600 	0.866198
+100 	0.851400 	0.849733
+120 	0.812900 	0.837039
+140 	0.812400 	0.827064
+160 	0.817300 	0.818410
+180 	0.802600 	0.810163
+200 	0.788600 	0.803399