Instructions to use Nohobby/L3.3-Prikol-70B-v0.2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Nohobby/L3.3-Prikol-70B-v0.2 with Transformers:

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

pipe = pipeline("text-generation", model="Nohobby/L3.3-Prikol-70B-v0.2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Nohobby/L3.3-Prikol-70B-v0.2")
model = AutoModelForCausalLM.from_pretrained("Nohobby/L3.3-Prikol-70B-v0.2")
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 Nohobby/L3.3-Prikol-70B-v0.2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Nohobby/L3.3-Prikol-70B-v0.2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Nohobby/L3.3-Prikol-70B-v0.2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Nohobby/L3.3-Prikol-70B-v0.2

SGLang

How to use Nohobby/L3.3-Prikol-70B-v0.2 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 "Nohobby/L3.3-Prikol-70B-v0.2" \
    --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": "Nohobby/L3.3-Prikol-70B-v0.2",
		"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 "Nohobby/L3.3-Prikol-70B-v0.2" \
        --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": "Nohobby/L3.3-Prikol-70B-v0.2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Nohobby/L3.3-Prikol-70B-v0.2 with Docker Model Runner:
```
docker model run hf.co/Nohobby/L3.3-Prikol-70B-v0.2
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Prikol

I don't even know anymore

Overview

A merge of some Llama 3.3 models because um uh yeah

Went extra schizo on the recipe, hoping for an extra fun result, and... Well, I guess it's an overall improvement over the previous revision. It's a tiny bit smarter, has even more distinct swipes and nice dialogues, but for some reason it's damn sloppy.

I've published the second step of this merge as a separate model, and I'd say the results are more interesting, but not as usable as this one. https://huggingface.co/Nohobby/AbominationSnowPig

Prompt format: Llama3 OR Llama3 Context and ChatML Instruct. It actually works a bit better this way

Samplers: This kinda works but I'm weird

Quants

Static | Imatrix

Merge Details

Merging Steps

Step1

models:
  - model: pankajmathur/orca_mini_v9_3_70B
  - model: EVA-UNIT-01/EVA-LLaMA-3.33-70B-v0.1
    parameters:
      weight: 1
      density: 0.55
      gamma: 0.03
  - model: Undi95/Sushi-v1.4
    parameters:
      weight: 0.069
      gamma: 0.001
      density: 0.911
merge_method: breadcrumbs
base_model: pankajmathur/orca_mini_v9_3_70B
parameters:
  int8_mask: true
  rescale: true
  normalize: true
dtype: bfloat16
tokenizer_source: base

Step2 (AbominationSnowPig)

dtype: bfloat16
tokenizer_source: base
merge_method: nuslerp
parameters:
  nuslerp_row_wise: true
models:
  - model: unsloth/Llama-3.3-70B-Instruct
    parameters:
      weight:
        - filter: v_proj
          value: [0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0]
        - filter: o_proj
          value: [1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1]
        - filter: up_proj
          value: [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
        - filter: gate_proj
          value: [0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0]
        - filter: down_proj
          value: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
        - value: 0
  - model: Step1
    parameters:
      weight:
        - filter: v_proj
          value: [1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1]
        - filter: o_proj
          value: [0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0]
        - filter: up_proj
          value: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
        - filter: gate_proj
          value: [1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1]
        - filter: down_proj
          value: [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
        - value: 1

Step3

base_model: AbominationSnowPig
merge_method: model_stock
dtype: bfloat16
models:
  - model: Sao10K/70B-L3.3-Cirrus-x1
  - model: Nohobby/L3.3-Prikol-70B-v0.1a

Downloads last month: -

Safetensors

Model size

71B params

Tensor type

BF16

Model tree for Nohobby/L3.3-Prikol-70B-v0.2

EVA-UNIT-01/EVA-LLaMA-3.33-70B-v0.1

Nohobby/L3.3-Prikol-70B-v0.1a

Sao10K/70B-L3.3-Cirrus-x1

Undi95/Sushi-v1.4

pankajmathur/orca_mini_v9_3_70B

unsloth/Llama-3.3-70B-Instruct

Merge model

this model

Merges

2 models

Quantizations

3 models