Instructions to use Hjgugugjhuhjggg/mergekit-passthrough-cijkogj with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Hjgugugjhuhjggg/mergekit-passthrough-cijkogj with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("Hjgugugjhuhjggg/mergekit-passthrough-cijkogj")
model = AutoModelForMultimodalLM.from_pretrained("Hjgugugjhuhjggg/mergekit-passthrough-cijkogj")
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 Hjgugugjhuhjggg/mergekit-passthrough-cijkogj with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Hjgugugjhuhjggg/mergekit-passthrough-cijkogj

SGLang

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

Docker Model Runner
How to use Hjgugugjhuhjggg/mergekit-passthrough-cijkogj with Docker Model Runner:
```
docker model run hf.co/Hjgugugjhuhjggg/mergekit-passthrough-cijkogj
```
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.

Merge Details

Merge Method

This model was merged using the passthrough merge method using huihui-ai/Llama-3.2-1B-Instruct-abliterated as a base.

Models Merged

The following models were included in the merge:

lilmeaty/testing_semifinal

Configuration

The following YAML configuration was used to produce this model:

slices:
  - sources:
      - model: lilmeaty/testing_semifinal
        layer_range: [27, 28]
        parameters:
          weight: 0.3
          density: 0.2
          gamma: 0.005
          normalize: true
          int8_mask: true
          random_seed: 42
          temperature: 0.5
          top_p: 0.65
          inference: true
          max_tokens: 300
          stream: true
          quantization:
            - method: int8
              value: 60
            - method: int4
              value: 40
merge_method: passthrough
base_model: huihui-ai/Llama-3.2-1B-Instruct-abliterated
dtype: float16
compression:
  pruning: 
    enabled: true
    sparsity: 0.95
  distillation:
    enabled: true
    temperature: 0.7
    model_type: "distilled"
  quantization:
    enabled: true
    methods:
      - int8
      - int4
inference_optimizations:
  caching:
    enabled: true
    cache_size: 1000
  batching:
    enabled: true
    batch_size: 8
  parallelism:
    enabled: true
    workers: 4
  asynchronous:
    enabled: true
    max_concurrent_tasks: 5
  tensor_cores:
    enabled: true
  gpu:
    enabled: true
    device: cuda
  model_sharding:
    enabled: true
    shards: 2
  memory_optimization:
    enabled: true
    strategy: "offload"
  tensor_compression:
    enabled: true
    method: "tensor_factorization"
mixture_of_experts:
  enabled: true
  num_experts: 4
  gating_strategy: top_k
  top_k: 2
  load_balancing:
    enabled: true
    balance_factor: 0.5
  expert_capacity:
    max_tokens_per_expert: 512
  dynamic_routing:
    enabled: true
    routing_threshold: 0.1
  routing_optimizations:
    enabled: true
    cache_routing: true
model_sparsity:
  enabled: true
  sparsity_pattern: "block"
  mask_method: "random"
  pruning_factor: 0.98
auto_tuning:
  enabled: true
  batch_size_adaptation:
    enabled: true
    factor: 0.8
    max_batch_size: 32
  temperature_scheduling:
    enabled: true
    start_temp: 1.0
    end_temp: 0.5
    schedule: "linear"

Downloads last month: 5

Safetensors

Model size

0.9B params

Tensor type

F16

Model tree for Hjgugugjhuhjggg/mergekit-passthrough-cijkogj

huihui-ai/Llama-3.2-1B-Instruct-abliterated

lilmeaty/testing_semifinal

Merge model

this model

Quantizations

1 model