Instructions to use Dongwei/Rationalyst_reasoning_datasets with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Dongwei/Rationalyst_reasoning_datasets with Transformers:

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

pipe = pipeline("text-generation", model="Dongwei/Rationalyst_reasoning_datasets")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModel

tokenizer = AutoTokenizer.from_pretrained("Dongwei/Rationalyst_reasoning_datasets")
model = AutoModel.from_pretrained("Dongwei/Rationalyst_reasoning_datasets")
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 Dongwei/Rationalyst_reasoning_datasets with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Dongwei/Rationalyst_reasoning_datasets

SGLang

How to use Dongwei/Rationalyst_reasoning_datasets 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 "Dongwei/Rationalyst_reasoning_datasets" \
    --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": "Dongwei/Rationalyst_reasoning_datasets",
		"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 "Dongwei/Rationalyst_reasoning_datasets" \
        --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": "Dongwei/Rationalyst_reasoning_datasets",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Dongwei/Rationalyst_reasoning_datasets with Docker Model Runner:
```
docker model run hf.co/Dongwei/Rationalyst_reasoning_datasets
```

Wrong model upload?

by flaviomerenda - opened Nov 15, 2024

Discussion

flaviomerenda

Nov 15, 2024

Dear authors,

Thank you for sharing this model and for the scientific contribution of your paper. I have been exploring the use of this model, but I consistently encounter issues with inconsistent outputs. Upon investigation, it appears that the problem lies in the configuration file, which specifies the model as LlamaModel instead of LlamaModelForCausalLM. This discrepancy seems to result in missing head weights, leading to the inconsistent outputs observed.

Additionally, when I attempt to load the model using the Transformers library, I encounter the following error:

Some weights of LlamaForCausalLM were not initialized from the model checkpoint at Dongwei/Rationalyst_reasoning_datasets and are newly initialized: ['lm_head.weight']"

When I attempt to load the model using the vLLM library, I encounter the following error:

ValueError: Model architectures ['LlamaModel'] are not supported for now. Supported architectures: ['AquilaModel', 'AquilaForCausalLM', 'BaiChuanForCausalLM', 'BaichuanForCausalLM', 'BloomForCausalLM', 'ChameleonForCausalLM', 'ChameleonForConditionalGeneration', 'ChatGLMModel', 'ChatGLMForConditionalGeneration', 'CohereForCausalLM', 'DbrxForCausalLM', 'DeciLMForCausalLM', 'DeepseekForCausalLM', 'DeepseekV2ForCausalLM', 'FalconForCausalLM', 'FuyuForCausalLM', 'GemmaForCausalLM', 'Gemma2ForCausalLM', 'GPT2LMHeadModel', 'GPTBigCodeForCausalLM', 'GPTJForCausalLM', 'GPTNeoXForCausalLM', 'InternLMForCausalLM', 'InternLM2ForCausalLM', 'JAISLMHeadModel', 'LlamaForCausalLM', 'LlavaForConditionalGeneration', 'LlavaNextForConditionalGeneration', 'LLaMAForCausalLM', 'MistralForCausalLM', 'MixtralForCausalLM', 'QuantMixtralForCausalLM', 'MptForCausalLM', 'MPTForCausalLM', 'MiniCPMForCausalLM', 'OlmoForCausalLM', 'OPTForCausalLM', 'OrionForCausalLM', 'PersimmonForCausalLM', 'PaliGemmaForConditionalGeneration', 'PhiForCausalLM', 'Phi3ForCausalLM', 'Phi3VForCausalLM', 'QWenLMHeadModel', 'Qwen2ForCausalLM', 'Qwen2MoeForCausalLM', 'RWForCausalLM', 'StableLMEpochForCausalLM', 'StableLmForCausalLM', 'Starcoder2ForCausalLM', 'ArcticForCausalLM', 'XverseForCausalLM', 'Phi3SmallForCausalLM', 'MedusaModel', 'MLPSpeculatorPreTrainedModel', 'JambaForCausalLM', 'MistralModel']

Has the model been saved incorrectly? Could you provide a code example demonstrating how to load the model correctly?

Thank you for your support.

Best regards

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