Instructions to use Cornerss/classicalChinese with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Cornerss/classicalChinese with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Cornerss/classicalChinese", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Cornerss/classicalChinese 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 Cornerss/classicalChinese:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Cornerss/classicalChinese:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Cornerss/classicalChinese:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Cornerss/classicalChinese:Q4_K_M

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 Cornerss/classicalChinese:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Cornerss/classicalChinese:Q4_K_M

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 Cornerss/classicalChinese:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Cornerss/classicalChinese:Q4_K_M

Use Docker

docker model run hf.co/Cornerss/classicalChinese:Q4_K_M

LM Studio
Jan
Ollama
How to use Cornerss/classicalChinese with Ollama:
```
ollama run hf.co/Cornerss/classicalChinese:Q4_K_M
```

Unsloth Studio

How to use Cornerss/classicalChinese with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Cornerss/classicalChinese to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Cornerss/classicalChinese to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Cornerss/classicalChinese to start chatting

How to use Cornerss/classicalChinese with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Cornerss/classicalChinese:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "Cornerss/classicalChinese:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use Cornerss/classicalChinese with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Cornerss/classicalChinese:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "Cornerss/classicalChinese:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use Cornerss/classicalChinese with Docker Model Runner:
```
docker model run hf.co/Cornerss/classicalChinese:Q4_K_M
```

Lemonade

How to use Cornerss/classicalChinese with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Cornerss/classicalChinese:Q4_K_M

Run and chat with the model

lemonade run user.classicalChinese-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use Cornerss/classicalChinese with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Cornerss/classicalChinese:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default Cornerss/classicalChinese:Q4_K_M

Run Hermes

hermes

Atomic Chat

Cornerss commited on Feb 1, 2025

Commit

70d983f

verified ·

1 Parent(s): 6dff3b6

Training in progress, step 100

Browse files

Files changed (5) hide show

adapter_config.json +5 -5
adapter_model.safetensors +1 -1
runs/Feb01_08-12-11_48bd3ce87c49/events.out.tfevents.1738397541.48bd3ce87c49.944.1 +3 -0
runs/Feb01_08-15-29_48bd3ce87c49/events.out.tfevents.1738397736.48bd3ce87c49.8923.0 +3 -0
training_args.bin +1 -1

adapter_config.json CHANGED Viewed

@@ -23,13 +23,13 @@
   "rank_pattern": {},
   "revision": null,
   "target_modules": [
-    "k_proj",
-    "up_proj",
     "o_proj",
     "v_proj",
-    "q_proj",
-    "down_proj",
-    "gate_proj"
   ],
   "task_type": "CAUSAL_LM",
   "use_dora": false,

   "rank_pattern": {},
   "revision": null,
   "target_modules": [
+    "q_proj",
+    "gate_proj",
     "o_proj",
+    "up_proj",
     "v_proj",
+    "k_proj",
+    "down_proj"
   ],
   "task_type": "CAUSAL_LM",
   "use_dora": false,

adapter_model.safetensors CHANGED Viewed

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:17625d59fb0d32c0f4f3b65ddb92641f4fb3b552d0676e4e320774982807a2eb
 size 167832240

 version https://git-lfs.github.com/spec/v1
+oid sha256:04acad0de89ca3caa851eed6036a48fa131b070ed57f7ff9da6c0f82f9514481
 size 167832240

runs/Feb01_08-12-11_48bd3ce87c49/events.out.tfevents.1738397541.48bd3ce87c49.944.1 ADDED Viewed

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:95b225467c8d61869ddc8a0ea6afc7c692861eeda6d1b055cc2a4cdc37017dae
+size 6585

runs/Feb01_08-15-29_48bd3ce87c49/events.out.tfevents.1738397736.48bd3ce87c49.8923.0 ADDED Viewed

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:c6fdddda7c63809640472ff66868a8d72fa8e3c0bc1d392dbea0b1a354fa2aca
+size 27219

training_args.bin CHANGED Viewed

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:a93199f578a7e80373bbcb6024a81ed39d6368e85757192073d82136789c62ba
 size 5688

 version https://git-lfs.github.com/spec/v1
+oid sha256:037107be1246e46f28b2f7b3b2357f40a8234e4b88682d5c1c3a4a08ff3cd2b4
 size 5688