Instructions to use Andy-ML-And-AI/HyperThinkCode-Qwen3-8B-v1.5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Andy-ML-And-AI/HyperThinkCode-Qwen3-8B-v1.5 with Transformers:

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

pipe = pipeline("text-generation", model="Andy-ML-And-AI/HyperThinkCode-Qwen3-8B-v1.5")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Andy-ML-And-AI/HyperThinkCode-Qwen3-8B-v1.5")
model = AutoModelForCausalLM.from_pretrained("Andy-ML-And-AI/HyperThinkCode-Qwen3-8B-v1.5")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Andy-ML-And-AI/HyperThinkCode-Qwen3-8B-v1.5 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Andy-ML-And-AI/HyperThinkCode-Qwen3-8B-v1.5"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Andy-ML-And-AI/HyperThinkCode-Qwen3-8B-v1.5",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Andy-ML-And-AI/HyperThinkCode-Qwen3-8B-v1.5

SGLang

How to use Andy-ML-And-AI/HyperThinkCode-Qwen3-8B-v1.5 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 "Andy-ML-And-AI/HyperThinkCode-Qwen3-8B-v1.5" \
    --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": "Andy-ML-And-AI/HyperThinkCode-Qwen3-8B-v1.5",
		"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 "Andy-ML-And-AI/HyperThinkCode-Qwen3-8B-v1.5" \
        --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": "Andy-ML-And-AI/HyperThinkCode-Qwen3-8B-v1.5",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio new

How to use Andy-ML-And-AI/HyperThinkCode-Qwen3-8B-v1.5 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 Andy-ML-And-AI/HyperThinkCode-Qwen3-8B-v1.5 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 Andy-ML-And-AI/HyperThinkCode-Qwen3-8B-v1.5 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Andy-ML-And-AI/HyperThinkCode-Qwen3-8B-v1.5 to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="Andy-ML-And-AI/HyperThinkCode-Qwen3-8B-v1.5",
    max_seq_length=2048,
)

Docker Model Runner
How to use Andy-ML-And-AI/HyperThinkCode-Qwen3-8B-v1.5 with Docker Model Runner:
```
docker model run hf.co/Andy-ML-And-AI/HyperThinkCode-Qwen3-8B-v1.5
```

Andy-ML-And-AI commited on 18 days ago

Commit

bc48c43

verified ·

1 Parent(s): 6ccd13d

Update README.md

Browse files

Files changed (1) hide show

README.md +9 -3

README.md CHANGED Viewed

@@ -9,6 +9,7 @@ tags:
 - qlora
 - reasoning
 - code
 license: apache-2.0
 language:
 - en
@@ -21,9 +22,10 @@ library_name: adapter
 pipeline_tag: text-generation
 ---
-# CTD-Qwen3-8B (Code Till Death)
-CTD-Qwen3-8B is a LoRA fine-tune of the Qwen3-8B base model.
 ---
 ## 🛠 Experimental Setup
@@ -36,6 +38,7 @@ CTD-Qwen3-8B is a LoRA fine-tune of the Qwen3-8B base model.
   - MLP: gate, up, down
 - Training time: ~1 hour 17 minutes
 - Total steps: 50
 ---
 ## 🧠 Dataset & Objective
@@ -46,6 +49,7 @@ Training on a specific 30k subset of the
 - Uses chat template with assistant response in the *thinking* field
 - Objective: encourage *thinking over direct response*
 - Sequence length limited to 4096 tokens (for code complexity + VRAM constraints)
 ---
 ## 📉 Training Logs
@@ -59,6 +63,7 @@ With only 50 steps, the loss shows expected variance given model + dataset compl
 | 50   | 0.6785       |
 - Global batch size: 8 (1 device × 8 gradient steps)
 ---
 ## 📊 Evaluation (Ongoing)
@@ -69,6 +74,7 @@ Currently running benchmarks using the **lm-eval** library:
 - GSM8K (Math)
 Comparisons are being made against the base model.
 ---
 ## 🔁 Reproduction
@@ -77,7 +83,7 @@ Comparisons are being made against the base model.
 from unsloth import FastLanguageModel
 model, tokenizer = FastLanguageModel.from_pretrained(
-    model_name = "Andy-ML-And-AI/CTD-Qwen3-8B",
     max_seq_length = 4096,
     load_in_4bit = True,
 )

 - qlora
 - reasoning
 - code
+- hyperthinkcode
 license: apache-2.0
 language:
 - en
 pipeline_tag: text-generation
 ---
+# HyperThinkCode-Qwen3-8B-v1
+HyperThinkCode-Qwen3-8B-v1 is a LoRA fine-tune of the Qwen3-8B base model.
 ---
 ## 🛠 Experimental Setup
   - MLP: gate, up, down
 - Training time: ~1 hour 17 minutes
 - Total steps: 50
 ---
 ## 🧠 Dataset & Objective
 - Uses chat template with assistant response in the *thinking* field
 - Objective: encourage *thinking over direct response*
 - Sequence length limited to 4096 tokens (for code complexity + VRAM constraints)
 ---
 ## 📉 Training Logs
 | 50   | 0.6785       |
 - Global batch size: 8 (1 device × 8 gradient steps)
 ---
 ## 📊 Evaluation (Ongoing)
 - GSM8K (Math)
 Comparisons are being made against the base model.
 ---
 ## 🔁 Reproduction
 from unsloth import FastLanguageModel
 model, tokenizer = FastLanguageModel.from_pretrained(
+    model_name = "Andy-ML-And-AI/HyperThinkCode-Qwen3-8B-v1",
     max_seq_length = 4096,
     load_in_4bit = True,
 )