Instructions to use Yoesph/Haphazardv1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Yoesph/Haphazardv1 with Transformers:

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

pipe = pipeline("text-generation", model="Yoesph/Haphazardv1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Yoesph/Haphazardv1")
model = AutoModelForCausalLM.from_pretrained("Yoesph/Haphazardv1")
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 Yoesph/Haphazardv1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Yoesph/Haphazardv1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Yoesph/Haphazardv1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Yoesph/Haphazardv1

SGLang

How to use Yoesph/Haphazardv1 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 "Yoesph/Haphazardv1" \
    --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": "Yoesph/Haphazardv1",
		"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 "Yoesph/Haphazardv1" \
        --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": "Yoesph/Haphazardv1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Yoesph/Haphazardv1 with Docker Model Runner:
```
docker model run hf.co/Yoesph/Haphazardv1
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

merge

This is a merge of pre-trained language models created using mergekit.

Recommended Settings

Instruct Template, Text Completion, Context Template, Banned Tokens, Alternatively people have reported good results with all Mistralception.

Merge Details

Merge Method

This model was merged using the Model Stock merge method using mistralai/Mistral-Small-24B-Instruct-2501 as a base.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

base_model: mistralai/Mistral-Small-24B-Instruct-2501
merge_method: model_stock
dtype: bfloat16
models:
  - model: TheDrummer/Cydonia-24B-v2
  - model: PocketDoc/Dans-DangerousWinds-V1.1.1-24b
  - model: arcee-ai/Arcee-Blitz
  - model: ReadyArt/Forgotten-Safeword-24B-V2.2