Instructions to use Mintzs/thinklite-draft-LHM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Mintzs/thinklite-draft-LHM with Transformers:

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

pipe = pipeline("image-text-to-text", model="Mintzs/thinklite-draft-LHM")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("Mintzs/thinklite-draft-LHM")
model = AutoModelForMultimodalLM.from_pretrained("Mintzs/thinklite-draft-LHM", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Mintzs/thinklite-draft-LHM 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 Mintzs/thinklite-draft-LHM:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Mintzs/thinklite-draft-LHM:Q4_K_M

Install from WinGet (Windows)

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

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 Mintzs/thinklite-draft-LHM:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama cli -hf Mintzs/thinklite-draft-LHM:Q4_K_M

Use Docker

docker model run hf.co/Mintzs/thinklite-draft-LHM:Q4_K_M

Llama new
LM Studio
Jan

vLLM

How to use Mintzs/thinklite-draft-LHM with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Mintzs/thinklite-draft-LHM"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Mintzs/thinklite-draft-LHM",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/Mintzs/thinklite-draft-LHM:Q4_K_M

SGLang

How to use Mintzs/thinklite-draft-LHM 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 "Mintzs/thinklite-draft-LHM" \
    --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": "Mintzs/thinklite-draft-LHM",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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 "Mintzs/thinklite-draft-LHM" \
        --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": "Mintzs/thinklite-draft-LHM",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Ollama
How to use Mintzs/thinklite-draft-LHM with Ollama:
```
ollama run hf.co/Mintzs/thinklite-draft-LHM:Q4_K_M
```
Unsloth Desktop

How to use Mintzs/thinklite-draft-LHM with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Mintzs/thinklite-draft-LHM:Q4_K_M

Configure the model in Pi

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

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use Mintzs/thinklite-draft-LHM with Docker Model Runner:
```
docker model run hf.co/Mintzs/thinklite-draft-LHM:Q4_K_M
```

Lemonade

How to use Mintzs/thinklite-draft-LHM with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Mintzs/thinklite-draft-LHM:Q4_K_M

Run and chat with the model

lemonade run user.thinklite-draft-LHM-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use Mintzs/thinklite-draft-LHM with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Mintzs/thinklite-draft-LHM: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 Mintzs/thinklite-draft-LHM:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Mintzs/thinklite-draft-LHM with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Mintzs/thinklite-draft-LHM: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 "Mintzs/thinklite-draft-LHM: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"

Mintzs commited on 12 days ago

Commit

86abbb2

verified ·

1 Parent(s): 22776cd

main: model card links to Sparset/thinklite-27B

Browse files

Files changed (1) hide show

README.md +12 -12

README.md CHANGED Viewed

@@ -54,15 +54,15 @@ Pick the branch (Hugging Face calls it a *revision*) wherever you load the model
 - **Download with the CLI:**
   ```bash
-  hf download Mintzs/thinklite-27B --revision low --local-dir Thinklite-27B-Low
-  hf download Mintzs/thinklite-27B --revision max --local-dir Thinklite-27B-Max
-  hf download Mintzs/thinklite-27B --local-dir Thinklite-27B-High          # main
   ```
-- **Python:** `snapshot_download("Mintzs/thinklite-27B", revision="low")`, or pass `revision="low"` / `revision="max"` to any
   `from_pretrained(...)` call.
 - **vLLM:** add `--revision low` or `--revision max` to `vllm serve` (examples under Usage).
-- **Git:** `git clone -b low https://huggingface.co/Mintzs/thinklite-27B` (requires `git lfs`).
 Without a revision you always get `main`, which is Thinklite-27B High.
@@ -165,16 +165,16 @@ times are shown relative to the base model timed in that run (same speedup, comm
 **vLLM**
 ```bash
-vllm serve Mintzs/thinklite-27B --max-model-len 135168 --kv-cache-dtype fp8                    # Thinklite-27B High
-vllm serve Mintzs/thinklite-27B --revision low --max-model-len 135168 --kv-cache-dtype fp8     # Thinklite-27B Low
-vllm serve Mintzs/thinklite-27B --revision max --max-model-len 135168 --kv-cache-dtype fp8     # Thinklite-27B Max
 ```
 ```python
 from openai import OpenAI
 client = OpenAI(base_url="http://localhost:8000/v1", api_key="-")
 reply = client.chat.completions.create(
-    model="Mintzs/thinklite-27B",
     messages=[{"role": "user", "content": "How many primes are there below 100?"}],
     extra_body={"chat_template_kwargs": {"reasoning_effort": "low"}},
 )
@@ -185,8 +185,8 @@ print(reply.choices[0].message.content)
 ```python
 from transformers import AutoModelForImageTextToText, AutoProcessor
-model = AutoModelForImageTextToText.from_pretrained("Mintzs/thinklite-27B", revision="main", dtype="auto", device_map="auto")
-processor = AutoProcessor.from_pretrained("Mintzs/thinklite-27B", revision="main")
 ```
 Use `revision="low"` or `revision="max"` for the other versions.
@@ -194,7 +194,7 @@ Use `revision="low"` or `revision="max"` for the other versions.
 **llama.cpp (Thinklite-27B High, GGUF)**
 ```bash
-llama-server -hf Mintzs/thinklite-27B:Q4_K_M --jinja -c 32768 --chat-template-kwargs '{"reasoning_effort": "low"}'
 # or with local files:
 llama-server -m Thinklite-27B-High-Q4_K_M.gguf --mmproj mmproj-Thinklite-27B-High-F16.gguf --jinja -c 32768
 ```

 - **Download with the CLI:**
   ```bash
+  hf download Sparset/thinklite-27B --revision low --local-dir Thinklite-27B-Low
+  hf download Sparset/thinklite-27B --revision max --local-dir Thinklite-27B-Max
+  hf download Sparset/thinklite-27B --local-dir Thinklite-27B-High          # main
   ```
+- **Python:** `snapshot_download("Sparset/thinklite-27B", revision="low")`, or pass `revision="low"` / `revision="max"` to any
   `from_pretrained(...)` call.
 - **vLLM:** add `--revision low` or `--revision max` to `vllm serve` (examples under Usage).
+- **Git:** `git clone -b low https://huggingface.co/Sparset/thinklite-27B` (requires `git lfs`).
 Without a revision you always get `main`, which is Thinklite-27B High.
 **vLLM**
 ```bash
+vllm serve Sparset/thinklite-27B --max-model-len 135168 --kv-cache-dtype fp8                    # Thinklite-27B High
+vllm serve Sparset/thinklite-27B --revision low --max-model-len 135168 --kv-cache-dtype fp8     # Thinklite-27B Low
+vllm serve Sparset/thinklite-27B --revision max --max-model-len 135168 --kv-cache-dtype fp8     # Thinklite-27B Max
 ```
 ```python
 from openai import OpenAI
 client = OpenAI(base_url="http://localhost:8000/v1", api_key="-")
 reply = client.chat.completions.create(
+    model="Sparset/thinklite-27B",
     messages=[{"role": "user", "content": "How many primes are there below 100?"}],
     extra_body={"chat_template_kwargs": {"reasoning_effort": "low"}},
 )
 ```python
 from transformers import AutoModelForImageTextToText, AutoProcessor
+model = AutoModelForImageTextToText.from_pretrained("Sparset/thinklite-27B", revision="main", dtype="auto", device_map="auto")
+processor = AutoProcessor.from_pretrained("Sparset/thinklite-27B", revision="main")
 ```
 Use `revision="low"` or `revision="max"` for the other versions.
 **llama.cpp (Thinklite-27B High, GGUF)**
 ```bash
+llama-server -hf Sparset/thinklite-27B:Q4_K_M --jinja -c 32768 --chat-template-kwargs '{"reasoning_effort": "low"}'
 # or with local files:
 llama-server -m Thinklite-27B-High-Q4_K_M.gguf --mmproj mmproj-Thinklite-27B-High-F16.gguf --jinja -c 32768
 ```