Instructions to use Weinel-Ventures/Sharp-Coder-V0.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Weinel-Ventures/Sharp-Coder-V0.1 with Transformers:

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

pipe = pipeline("text-generation", model="Weinel-Ventures/Sharp-Coder-V0.1")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Weinel-Ventures/Sharp-Coder-V0.1")
model = AutoModelForCausalLM.from_pretrained("Weinel-Ventures/Sharp-Coder-V0.1", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Weinel-Ventures/Sharp-Coder-V0.1 with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Weinel-Ventures/Sharp-Coder-V0.1:F16
# Run inference directly in the terminal:
llama cli -hf Weinel-Ventures/Sharp-Coder-V0.1:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Weinel-Ventures/Sharp-Coder-V0.1:F16
# Run inference directly in the terminal:
llama cli -hf Weinel-Ventures/Sharp-Coder-V0.1:F16

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf Weinel-Ventures/Sharp-Coder-V0.1:F16
# Run inference directly in the terminal:
./llama-cli -hf Weinel-Ventures/Sharp-Coder-V0.1:F16

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf Weinel-Ventures/Sharp-Coder-V0.1:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Weinel-Ventures/Sharp-Coder-V0.1:F16

Use Docker

docker model run hf.co/Weinel-Ventures/Sharp-Coder-V0.1:F16

LM Studio
Jan

vLLM

How to use Weinel-Ventures/Sharp-Coder-V0.1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Weinel-Ventures/Sharp-Coder-V0.1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Weinel-Ventures/Sharp-Coder-V0.1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Weinel-Ventures/Sharp-Coder-V0.1:F16

SGLang

How to use Weinel-Ventures/Sharp-Coder-V0.1 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 "Weinel-Ventures/Sharp-Coder-V0.1" \
    --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": "Weinel-Ventures/Sharp-Coder-V0.1",
		"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 "Weinel-Ventures/Sharp-Coder-V0.1" \
        --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": "Weinel-Ventures/Sharp-Coder-V0.1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use Weinel-Ventures/Sharp-Coder-V0.1 with Ollama:
```
ollama run hf.co/Weinel-Ventures/Sharp-Coder-V0.1:F16
```
Unsloth Desktop
Docker Model Runner
How to use Weinel-Ventures/Sharp-Coder-V0.1 with Docker Model Runner:
```
docker model run hf.co/Weinel-Ventures/Sharp-Coder-V0.1:F16
```

Lemonade

How to use Weinel-Ventures/Sharp-Coder-V0.1 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Weinel-Ventures/Sharp-Coder-V0.1:F16

Run and chat with the model

lemonade run user.Sharp-Coder-V0.1-F16

List all available models

lemonade list

Atomic Chat

Sharp-Coder-V0.1

File size: 451 Bytes

6945b15

{
  "name": "Sharp Coder V5",
  "architecture": "GPT2LMHeadModel-compatible",
  "parameters": 160977408,
  "context_length": 1024,
  "vocab_size": 24000,
  "hidden_size": 768,
  "layers": 20,
  "heads": 12,
  "ff_size": 3072,
  "source_checkpoint": "C:\\MyCodeAI\\v5\\checkpoints\\v5_best_model.pt",
  "source_step": 33200,
  "source_val_loss": 2.3804687189095186,
  "training_end_token": "<|endoftext|>",
  "training_end_token_id": 4
}