Instructions to use mrsandip/Alfa-Code with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mrsandip/Alfa-Code with Transformers:

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

pipe = pipeline("text-generation", model="mrsandip/Alfa-Code")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("mrsandip/Alfa-Code", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use mrsandip/Alfa-Code 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 mrsandip/Alfa-Code:Q4_0
# Run inference directly in the terminal:
llama cli -hf mrsandip/Alfa-Code:Q4_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf mrsandip/Alfa-Code:Q4_0
# Run inference directly in the terminal:
llama cli -hf mrsandip/Alfa-Code:Q4_0

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf mrsandip/Alfa-Code:Q4_0
# Run inference directly in the terminal:
./build/bin/llama cli -hf mrsandip/Alfa-Code:Q4_0

Use Docker

docker model run hf.co/mrsandip/Alfa-Code:Q4_0

Llama new
LM Studio
Jan

vLLM

How to use mrsandip/Alfa-Code with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/mrsandip/Alfa-Code:Q4_0

SGLang

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

Ollama
How to use mrsandip/Alfa-Code with Ollama:
```
ollama run hf.co/mrsandip/Alfa-Code:Q4_0
```
Unsloth Desktop
Docker Model Runner
How to use mrsandip/Alfa-Code with Docker Model Runner:
```
docker model run hf.co/mrsandip/Alfa-Code:Q4_0
```

Lemonade

How to use mrsandip/Alfa-Code with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull mrsandip/Alfa-Code:Q4_0

Run and chat with the model

lemonade run user.Alfa-Code-Q4_0

List all available models

lemonade list

Atomic Chat

mrsandip commited on Oct 8

Commit

7006612

verified ·

1 Parent(s): 3ede2ee

Upload folder using huggingface_hub

Browse files

Files changed (2) hide show

outputs/base-1B/config.json +2 -2
outputs/base-1B/generation_config.json +1 -1

outputs/base-1B/config.json CHANGED Viewed

@@ -12,7 +12,7 @@
   "hidden_size": 2048,
   "initializer_range": 0.02,
   "intermediate_size": 5632,
-  "max_position_embeddings": 1024,
   "mlp_bias": false,
   "model_type": "llama",
   "num_attention_heads": 32,
@@ -26,7 +26,7 @@
     "rope_type": "default"
   },
   "tie_word_embeddings": false,
-  "transformers_version": "5.18.0",
   "use_cache": false,
   "vocab_size": 32000
 }

   "hidden_size": 2048,
   "initializer_range": 0.02,
   "intermediate_size": 5632,
+  "max_position_embeddings": 2048,
   "mlp_bias": false,
   "model_type": "llama",
   "num_attention_heads": 32,
     "rope_type": "default"
   },
   "tie_word_embeddings": false,
+  "transformers_version": "5.16.1",
   "use_cache": false,
   "vocab_size": 32000
 }

outputs/base-1B/generation_config.json CHANGED Viewed

@@ -4,6 +4,6 @@
   "eos_token_id": 2,
   "output_attentions": false,
   "output_hidden_states": false,
-  "transformers_version": "5.18.0",
   "use_cache": true
 }

   "eos_token_id": 2,
   "output_attentions": false,
   "output_hidden_states": false,
+  "transformers_version": "5.16.1",
   "use_cache": true
 }