Instructions to use oneblackmage/pixelated-kickstand with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use oneblackmage/pixelated-kickstand with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("LatitudeGames/Wayfarer-2-12B") model = PeftModel.from_pretrained(base_model, "oneblackmage/pixelated-kickstand") - Transformers
How to use oneblackmage/pixelated-kickstand with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="oneblackmage/pixelated-kickstand") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("oneblackmage/pixelated-kickstand", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use oneblackmage/pixelated-kickstand with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "oneblackmage/pixelated-kickstand" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "oneblackmage/pixelated-kickstand", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/oneblackmage/pixelated-kickstand
- SGLang
How to use oneblackmage/pixelated-kickstand 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 "oneblackmage/pixelated-kickstand" \ --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": "oneblackmage/pixelated-kickstand", "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 "oneblackmage/pixelated-kickstand" \ --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": "oneblackmage/pixelated-kickstand", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Unsloth Studio new
How to use oneblackmage/pixelated-kickstand with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for oneblackmage/pixelated-kickstand to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for oneblackmage/pixelated-kickstand to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for oneblackmage/pixelated-kickstand to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="oneblackmage/pixelated-kickstand", max_seq_length=2048, ) - Docker Model Runner
How to use oneblackmage/pixelated-kickstand with Docker Model Runner:
docker model run hf.co/oneblackmage/pixelated-kickstand
Upload folder using huggingface_hub
Browse files- adapter_config.json +4 -4
- adapter_model.safetensors +1 -1
adapter_config.json
CHANGED
|
@@ -33,13 +33,13 @@
|
|
| 33 |
"rank_pattern": {},
|
| 34 |
"revision": null,
|
| 35 |
"target_modules": [
|
| 36 |
-
"q_proj",
|
| 37 |
-
"k_proj",
|
| 38 |
"o_proj",
|
|
|
|
| 39 |
"v_proj",
|
|
|
|
|
|
|
| 40 |
"gate_proj",
|
| 41 |
-
"
|
| 42 |
-
"up_proj"
|
| 43 |
],
|
| 44 |
"target_parameters": null,
|
| 45 |
"task_type": "CAUSAL_LM",
|
|
|
|
| 33 |
"rank_pattern": {},
|
| 34 |
"revision": null,
|
| 35 |
"target_modules": [
|
|
|
|
|
|
|
| 36 |
"o_proj",
|
| 37 |
+
"down_proj",
|
| 38 |
"v_proj",
|
| 39 |
+
"up_proj",
|
| 40 |
+
"k_proj",
|
| 41 |
"gate_proj",
|
| 42 |
+
"q_proj"
|
|
|
|
| 43 |
],
|
| 44 |
"target_parameters": null,
|
| 45 |
"task_type": "CAUSAL_LM",
|
adapter_model.safetensors
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 228140600
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:5aeb1cfbb0d75eeaa49250fd57c9a4d8a95da5d52b195f6d9d6a28a8a7fb4172
|
| 3 |
size 228140600
|