Instructions to use raincandy-u/Coder1.8-ORPO-TEST with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use raincandy-u/Coder1.8-ORPO-TEST with Transformers:

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

pipe = pipeline("text-generation", model="raincandy-u/Coder1.8-ORPO-TEST")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("raincandy-u/Coder1.8-ORPO-TEST")
model = AutoModelForCausalLM.from_pretrained("raincandy-u/Coder1.8-ORPO-TEST", 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 raincandy-u/Coder1.8-ORPO-TEST with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "raincandy-u/Coder1.8-ORPO-TEST"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "raincandy-u/Coder1.8-ORPO-TEST",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/raincandy-u/Coder1.8-ORPO-TEST

SGLang

How to use raincandy-u/Coder1.8-ORPO-TEST 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 "raincandy-u/Coder1.8-ORPO-TEST" \
    --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": "raincandy-u/Coder1.8-ORPO-TEST",
		"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 "raincandy-u/Coder1.8-ORPO-TEST" \
        --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": "raincandy-u/Coder1.8-ORPO-TEST",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use raincandy-u/Coder1.8-ORPO-TEST with Docker Model Runner:
```
docker model run hf.co/raincandy-u/Coder1.8-ORPO-TEST
```

Coder1.8-ORPO-TEST / README.md

raincandy-u

Adding Evaluation Results (#1)

dde8c4b verified over 2 years ago

preview code

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	---
	language:
	- en
	license: other
	tags:
	- code
	datasets:
	- reciprocate/dpo_ultra-capybara-code_filtered-best
	license_name: tongyi-qianwen
	license_link: https://huggingface.co/Qwen/Qwen1.5-7B-Chat/blob/main/LICENSE
	pipeline_tag: text-generation
	model-index:
	- name: Coder1.8-ORPO-TEST
	results:
	- task:
	type: text-generation
	name: Text Generation
	dataset:
	name: AI2 Reasoning Challenge (25-Shot)
	type: ai2_arc
	config: ARC-Challenge
	split: test
	args:
	num_few_shot: 25
	metrics:
	- type: acc_norm
	value: 38.82
	name: normalized accuracy
	source:
	url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=raincandy-u/Coder1.8-ORPO-TEST
	name: Open LLM Leaderboard
	- task:
	type: text-generation
	name: Text Generation
	dataset:
	name: HellaSwag (10-Shot)
	type: hellaswag
	split: validation
	args:
	num_few_shot: 10
	metrics:
	- type: acc_norm
	value: 60.48
	name: normalized accuracy
	source:
	url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=raincandy-u/Coder1.8-ORPO-TEST
	name: Open LLM Leaderboard
	- task:
	type: text-generation
	name: Text Generation
	dataset:
	name: MMLU (5-Shot)
	type: cais/mmlu
	config: all
	split: test
	args:
	num_few_shot: 5
	metrics:
	- type: acc
	value: 46.7
	name: accuracy
	source:
	url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=raincandy-u/Coder1.8-ORPO-TEST
	name: Open LLM Leaderboard
	- task:
	type: text-generation
	name: Text Generation
	dataset:
	name: TruthfulQA (0-shot)
	type: truthful_qa
	config: multiple_choice
	split: validation
	args:
	num_few_shot: 0
	metrics:
	- type: mc2
	value: 41.38
	source:
	url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=raincandy-u/Coder1.8-ORPO-TEST
	name: Open LLM Leaderboard
	- task:
	type: text-generation
	name: Text Generation
	dataset:
	name: Winogrande (5-shot)
	type: winogrande
	config: winogrande_xl
	split: validation
	args:
	num_few_shot: 5
	metrics:
	- type: acc
	value: 59.75
	name: accuracy
	source:
	url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=raincandy-u/Coder1.8-ORPO-TEST
	name: Open LLM Leaderboard
	- task:
	type: text-generation
	name: Text Generation
	dataset:
	name: GSM8k (5-shot)
	type: gsm8k
	config: main
	split: test
	args:
	num_few_shot: 5
	metrics:
	- type: acc
	value: 27.45
	name: accuracy
	source:
	url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=raincandy-u/Coder1.8-ORPO-TEST
	name: Open LLM Leaderboard
	---

	# Coder1.8-ORPO-TEST

	## Model Description

	Test model for ORPO finetune method, trained on ~20k code examples for 1 epoch on 2 x A40 cards with 4-bit QLora (lora rank=lora alpha=16).

	## Disclaimer

	This is a test model and may generate incorrect responses. Use at your own risk.

	## Train Details

	- Base: Qwen1.5-1.8B
	- Training Data: ~20k [code examples](https://huggingface.co/datasets/reciprocate/dpo_ultra-capybara-code_filtered-best)
	- Epochs: 1
	- Method: ORPO
	- Hardware: 2 x A40
	- Quantization: 4-bit QLora
	- Lora Rank/Alpha: 16

	# Limitations

	Limited training data and quantization may impact performance.

	# Join the Discussion

	Have questions or feedback? Join our Discord server [Here](https://discord.gg/KugcbJX5).
	# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
	Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_raincandy-u__Coder1.8-ORPO-TEST)

	\| Metric \|Value\|
	\|---------------------------------\|----:\|
	\|Avg. \|45.76\|
	\|AI2 Reasoning Challenge (25-Shot)\|38.82\|
	\|HellaSwag (10-Shot) \|60.48\|
	\|MMLU (5-Shot) \|46.70\|
	\|TruthfulQA (0-shot) \|41.38\|
	\|Winogrande (5-shot) \|59.75\|
	\|GSM8k (5-shot) \|27.45\|