Instructions to use powermove72/IceShark-Coder-9b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use powermove72/IceShark-Coder-9b with Transformers:

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

pipe = pipeline("text-generation", model="powermove72/IceShark-Coder-9b")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("powermove72/IceShark-Coder-9b")
model = AutoModelForCausalLM.from_pretrained("powermove72/IceShark-Coder-9b")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use powermove72/IceShark-Coder-9b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "powermove72/IceShark-Coder-9b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "powermove72/IceShark-Coder-9b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/powermove72/IceShark-Coder-9b

SGLang

How to use powermove72/IceShark-Coder-9b 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 "powermove72/IceShark-Coder-9b" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "powermove72/IceShark-Coder-9b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "powermove72/IceShark-Coder-9b" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "powermove72/IceShark-Coder-9b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use powermove72/IceShark-Coder-9b with Docker Model Runner:
```
docker model run hf.co/powermove72/IceShark-Coder-9b
```

IceShark-Coder-9b

IceShark-Coder-9b is a merge of the following models using LazyMergekit:

🧩 Configuration

slices:
  - sources:
    - model: S-miguel/The-Trinity-Coder-7B
      layer_range: [0, 16]
  - sources:
    - model: powermove72/Shark-1
      layer_range: [8, 32]
merge_method: passthrough
tokenizer_source: union
dtype: float16
    ```

## 💻 Usage

```python
!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "powermove72/IceShark-Coder-9b"
messages = [{"role": "user", "content": "What is a large language model?"}]

tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    torch_dtype=torch.float16,
    device_map="auto",
)

outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])

Downloads last month: 1

Safetensors

Model size

9B params

Tensor type

F16

Model tree for powermove72/IceShark-Coder-9b

S-miguel/The-Trinity-Coder-7B

powermove72/Shark-1

Merge model

this model