Text Generation
Transformers
Safetensors
llama
mergekit
mergekitty
Merge
conversational
text-generation-inference
Instructions to use KaraKaraWarehouse/BlenderCartel-llama33-70B-Pt1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KaraKaraWarehouse/BlenderCartel-llama33-70B-Pt1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="KaraKaraWarehouse/BlenderCartel-llama33-70B-Pt1") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("KaraKaraWarehouse/BlenderCartel-llama33-70B-Pt1") model = AutoModelForCausalLM.from_pretrained("KaraKaraWarehouse/BlenderCartel-llama33-70B-Pt1", 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]:])) - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use KaraKaraWarehouse/BlenderCartel-llama33-70B-Pt1 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "KaraKaraWarehouse/BlenderCartel-llama33-70B-Pt1" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "KaraKaraWarehouse/BlenderCartel-llama33-70B-Pt1", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/KaraKaraWarehouse/BlenderCartel-llama33-70B-Pt1
- SGLang
How to use KaraKaraWarehouse/BlenderCartel-llama33-70B-Pt1 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 "KaraKaraWarehouse/BlenderCartel-llama33-70B-Pt1" \ --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": "KaraKaraWarehouse/BlenderCartel-llama33-70B-Pt1", "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 "KaraKaraWarehouse/BlenderCartel-llama33-70B-Pt1" \ --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": "KaraKaraWarehouse/BlenderCartel-llama33-70B-Pt1", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use KaraKaraWarehouse/BlenderCartel-llama33-70B-Pt1 with Docker Model Runner:
docker model run hf.co/KaraKaraWarehouse/BlenderCartel-llama33-70B-Pt1
metadata
base_model:
- nbeerbower/Llama3.1-Gutenberg-Doppel-70B
- TheDrummer/Fallen-Llama-3.3-70B-v1
- Blackroot/Mirai-3.0-70B
- EVA-UNIT-01/EVA-LLaMA-3.33-70B-v0.0
- tdrussell/Llama-3-70B-Instruct-Storywriter
- TheDrummer/Anubis-70B-v1.1
- deepcogito/cogito-v2-preview-llama-70B
- Sao10K/70B-L3.3-mhnnn-x1
- NeverSleep/Lumimaid-v0.2-70B
- Doctor-Shotgun/L3.3-70B-Magnum-Diamond
- Black-Ink-Guild/Pernicious_Prophecy_70B
- Ppoyaa/MythoNemo-L3.1-70B-v1.0
- nvidia/Llama-3.1-Nemotron-70B-Instruct-HF
- zerofata/L3.3-GeneticLemonade-Final-v2-70B
- marcelbinz/Llama-3.1-Centaur-70B
- LatitudeGames/Wayfarer-Large-70B-Llama-3.3
library_name: transformers
tags:
- mergekit
- mergekitty
- merge
BlenderCartel-llama33-70B-Pt1
This is a merge of pre-trained language models created using mergekitty.
Merge Details
Merge Method
This model was merged using the SCE merge method using deepcogito/cogito-v2-preview-llama-70B as a base.
Models Merged
The following models were included in the merge:
- nbeerbower/Llama3.1-Gutenberg-Doppel-70B
- TheDrummer/Fallen-Llama-3.3-70B-v1
- Blackroot/Mirai-3.0-70B
- EVA-UNIT-01/EVA-LLaMA-3.33-70B-v0.0
- tdrussell/Llama-3-70B-Instruct-Storywriter
- TheDrummer/Anubis-70B-v1.1
- Sao10K/70B-L3.3-mhnnn-x1
- NeverSleep/Lumimaid-v0.2-70B
- Doctor-Shotgun/L3.3-70B-Magnum-Diamond
- Black-Ink-Guild/Pernicious_Prophecy_70B
- Ppoyaa/MythoNemo-L3.1-70B-v1.0
- nvidia/Llama-3.1-Nemotron-70B-Instruct-HF
- zerofata/L3.3-GeneticLemonade-Final-v2-70B
- marcelbinz/Llama-3.1-Centaur-70B
- LatitudeGames/Wayfarer-Large-70B-Llama-3.3
Configuration
The following YAML configuration was used to produce this model:
models:
# Mirai is Mirai.
- model: Blackroot/Mirai-3.0-70B
# Narration
- model: EVA-UNIT-01/EVA-LLaMA-3.33-70B-v0.0
# Claude 3 Sonnet/Opus prose style and quality
- model: Doctor-Shotgun/L3.3-70B-Magnum-Diamond
# "For the *Action*"?
- model: marcelbinz/Llama-3.1-Centaur-70B
# Better writing style, "creativity" shift. (fiction books)
- model: tdrussell/Llama-3-70B-Instruct-Storywriter
# Roleplaying and Story Writing
- model: Ppoyaa/MythoNemo-L3.1-70B-v1.0
# Classics (NeverSleep)
- model: NeverSleep/Lumimaid-v0.2-70B
# Sao10K
- model: Sao10K/70B-L3.3-mhnnn-x1
# Medical
- model: Black-Ink-Guild/Pernicious_Prophecy_70B
# Dialogue reinforcement
- model: LatitudeGames/Wayfarer-Large-70B-Llama-3.3
# Extra details
- model: TheDrummer/Anubis-70B-v1.1
# "Meanness"
- model: TheDrummer/Fallen-Llama-3.3-70B-v1
# Antique history
- model: nbeerbower/Llama3.1-Gutenberg-Doppel-70B
# Normalization?
- model: nvidia/Llama-3.1-Nemotron-70B-Instruct-HF
# ERP/RP enhancement + Anime tilt
- model: zerofata/L3.3-GeneticLemonade-Final-v2-70B
merge_method: sce
base_model: deepcogito/cogito-v2-preview-llama-70B
select_topk: 0.2
parameters:
normalize: true
dtype: bfloat16