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

Libraries

How to use lilmeaty/kaska with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("lilmeaty/kaska")
model = AutoModelForCausalLM.from_pretrained("lilmeaty/kaska", 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]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use lilmeaty/kaska with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/lilmeaty/kaska

SGLang

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

Docker Model Runner
How to use lilmeaty/kaska with Docker Model Runner:
```
docker model run hf.co/lilmeaty/kaska
```

Hjgugugjhuhjggg commited on Nov 4, 2024

Commit

31c73e7

verified ·

1 Parent(s): a7dcbb1

Update config.json

Browse files

Files changed (1) hide show

config.json +5 -4

config.json CHANGED Viewed

@@ -1,14 +1,15 @@
 {
   "_name_or_path": "lilmeaty/ksksksk",
   "architectures": [
-    "MistralForCausalLM"
   ],
   "attention_dropout": 0.0,
   "bos_token_id": 1,
   "eos_token_id": 2,
   "head_dim": 128,
   "hidden_act": "silu",
-  "hidden_size": 3072,
   "initializer_range": 0.02,
   "intermediate_size": 14336,
   "max_position_embeddings": 131072,
@@ -18,10 +19,10 @@
   "num_key_value_heads": 8,
   "rms_norm_eps": 1e-05,
   "rope_theta": 10000.0,
-  "sliding_window": 3072,
   "tie_word_embeddings": false,
   "torch_dtype": "bfloat16",
   "transformers_version": "4.46.1",
   "use_cache": false,
-  "vocab_size": 128256
 }

 {
   "_name_or_path": "lilmeaty/ksksksk",
   "architectures": [
+    "MistralForCausalLM",
   ],
   "attention_dropout": 0.0,
   "bos_token_id": 1,
   "eos_token_id": 2,
   "head_dim": 128,
   "hidden_act": "silu",
+  "hidden_size": 4096,
   "initializer_range": 0.02,
   "intermediate_size": 14336,
   "max_position_embeddings": 131072,
   "num_key_value_heads": 8,
   "rms_norm_eps": 1e-05,
   "rope_theta": 10000.0,
+  "sliding_window": 4096,
   "tie_word_embeddings": false,
   "torch_dtype": "bfloat16",
   "transformers_version": "4.46.1",
   "use_cache": false,
+  "vocab_size": 32000
 }