Instructions to use CodeSoft/sorbet-v2-25m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use CodeSoft/sorbet-v2-25m with Transformers:

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

pipe = pipeline("text-generation", model="CodeSoft/sorbet-v2-25m")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("CodeSoft/sorbet-v2-25m")
model = AutoModelForCausalLM.from_pretrained("CodeSoft/sorbet-v2-25m", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use CodeSoft/sorbet-v2-25m 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 CodeSoft/sorbet-v2-25m:Q4_K_XL
# Run inference directly in the terminal:
llama cli -hf CodeSoft/sorbet-v2-25m:Q4_K_XL

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf CodeSoft/sorbet-v2-25m:Q4_K_XL
# Run inference directly in the terminal:
llama cli -hf CodeSoft/sorbet-v2-25m:Q4_K_XL

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 CodeSoft/sorbet-v2-25m:Q4_K_XL
# Run inference directly in the terminal:
./llama-cli -hf CodeSoft/sorbet-v2-25m:Q4_K_XL

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 CodeSoft/sorbet-v2-25m:Q4_K_XL
# Run inference directly in the terminal:
./build/bin/llama-cli -hf CodeSoft/sorbet-v2-25m:Q4_K_XL

Use Docker

docker model run hf.co/CodeSoft/sorbet-v2-25m:Q4_K_XL

LM Studio
Jan

vLLM

How to use CodeSoft/sorbet-v2-25m with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "CodeSoft/sorbet-v2-25m"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "CodeSoft/sorbet-v2-25m",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/CodeSoft/sorbet-v2-25m:Q4_K_XL

SGLang

How to use CodeSoft/sorbet-v2-25m 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 "CodeSoft/sorbet-v2-25m" \
    --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": "CodeSoft/sorbet-v2-25m",
		"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 "CodeSoft/sorbet-v2-25m" \
        --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": "CodeSoft/sorbet-v2-25m",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use CodeSoft/sorbet-v2-25m with Ollama:
```
ollama run hf.co/CodeSoft/sorbet-v2-25m:Q4_K_XL
```
Unsloth Desktop
Docker Model Runner
How to use CodeSoft/sorbet-v2-25m with Docker Model Runner:
```
docker model run hf.co/CodeSoft/sorbet-v2-25m:Q4_K_XL
```

Lemonade

How to use CodeSoft/sorbet-v2-25m with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull CodeSoft/sorbet-v2-25m:Q4_K_XL

Run and chat with the model

lemonade run user.sorbet-v2-25m-Q4_K_XL

List all available models

lemonade list

Atomic Chat

CodeSoft commited on 7 days ago

Commit

debed95

verified ·

1 Parent(s): 36200e4

Add hfviewer architecture card

Browse files

Embed the hfviewer architecture visualization card in README.md.

Files changed (1) hide show

README.md +10 -0

README.md CHANGED Viewed

@@ -16,6 +16,16 @@ library_name: transformers
 # Sorbet-v2-25M
 ~25M-parameter Qwen2-style decoder LM, warm-started from Sorbet-25M and continued-trained in two legs totaling ~12hrs on a single RTX 5060 Ti (16GB). Scores at or above Sorbet-25M on every benchmark below while cutting validation perplexity by ~6%.
 ## Architecture

 # Sorbet-v2-25M
+## Architecture graph
+<a href="https://hfviewer.com/CodeSoft/sorbet-v2-25m?utm_source=huggingface&amp;utm_medium=embedded_model_card&amp;utm_campaign=CodeSoft_sorbet-v2-25m_card" target="_blank" rel="noopener">
+  <img
+    src="https://hfviewer.com/api/card.svg?source=CodeSoft%2Fsorbet-v2-25m&amp;granularity=0"
+    alt="Architecture graph for CodeSoft/sorbet-v2-25m. Open in hfviewer"
+    width="100%"
+  />
+</a>
 ~25M-parameter Qwen2-style decoder LM, warm-started from Sorbet-25M and continued-trained in two legs totaling ~12hrs on a single RTX 5060 Ti (16GB). Scores at or above Sorbet-25M on every benchmark below while cutting validation perplexity by ~6%.
 ## Architecture