Instructions to use DreadPoor/Sunk_Cost_Fallacy-8B-Model_Stock with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DreadPoor/Sunk_Cost_Fallacy-8B-Model_Stock with Transformers:

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

pipe = pipeline("text-generation", model="DreadPoor/Sunk_Cost_Fallacy-8B-Model_Stock")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("DreadPoor/Sunk_Cost_Fallacy-8B-Model_Stock")
model = AutoModelForCausalLM.from_pretrained("DreadPoor/Sunk_Cost_Fallacy-8B-Model_Stock")
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

vLLM

How to use DreadPoor/Sunk_Cost_Fallacy-8B-Model_Stock with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/DreadPoor/Sunk_Cost_Fallacy-8B-Model_Stock

SGLang

How to use DreadPoor/Sunk_Cost_Fallacy-8B-Model_Stock 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 "DreadPoor/Sunk_Cost_Fallacy-8B-Model_Stock" \
    --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": "DreadPoor/Sunk_Cost_Fallacy-8B-Model_Stock",
		"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 "DreadPoor/Sunk_Cost_Fallacy-8B-Model_Stock" \
        --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": "DreadPoor/Sunk_Cost_Fallacy-8B-Model_Stock",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use DreadPoor/Sunk_Cost_Fallacy-8B-Model_Stock with Docker Model Runner:
```
docker model run hf.co/DreadPoor/Sunk_Cost_Fallacy-8B-Model_Stock
```

Sunk_Cost_Fallacy-8B-Model_Stock / README.md

DreadPoor

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	---
	base_model:
	- SentientAGI/Dobby-Mini-Unhinged-Llama-3.1-8B
	- DreadPoor/Aspire-8B-model_stock
	- DreadPoor/Rusted_Gold-8B-LINEAR
	- kromcomp/L3-Umbral-Mind-r128-LoRA
	- Yuma42/Llama3.1-IgneousIguana-8B
	- DreadPoor/ichor_1.1-8B-Model_Stock
	library_name: transformers
	tags:
	- mergekit
	- merge

	---
	# merge

	This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).

	## Merge Details
	### Merge Method

	This model was merged using the [Model Stock](https://arxiv.org/abs/2403.19522) merge method using [SentientAGI/Dobby-Mini-Unhinged-Llama-3.1-8B](https://huggingface.co/SentientAGI/Dobby-Mini-Unhinged-Llama-3.1-8B) as a base.

	### Models Merged

	The following models were included in the merge:
	* [DreadPoor/Aspire-8B-model_stock](https://huggingface.co/DreadPoor/Aspire-8B-model_stock)
	* [DreadPoor/Rusted_Gold-8B-LINEAR](https://huggingface.co/DreadPoor/Rusted_Gold-8B-LINEAR) + [kromcomp/L3-Umbral-Mind-r128-LoRA](https://huggingface.co/kromcomp/L3-Umbral-Mind-r128-LoRA)
	* [Yuma42/Llama3.1-IgneousIguana-8B](https://huggingface.co/Yuma42/Llama3.1-IgneousIguana-8B)
	* [DreadPoor/ichor_1.1-8B-Model_Stock](https://huggingface.co/DreadPoor/ichor_1.1-8B-Model_Stock)

	### Configuration

	The following YAML configuration was used to produce this model:

	```yaml
	models:
	- model: DreadPoor/Rusted_Gold-8B-LINEAR+kromcomp/L3-Umbral-Mind-r128-LoRA
	- model: DreadPoor/ichor_1.1-8B-Model_Stock
	- model: DreadPoor/Aspire-8B-model_stock
	- model: Yuma42/Llama3.1-IgneousIguana-8B
	merge_method: model_stock
	base_model: SentientAGI/Dobby-Mini-Unhinged-Llama-3.1-8B
	normalize: false
	int8_mask: true
	dtype: bfloat16

	```