Instructions to use CoNDeNse-AI/GLM-5.1-Qwen3-1.7B-CoNDeNse with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use CoNDeNse-AI/GLM-5.1-Qwen3-1.7B-CoNDeNse with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-1.7B")
model = PeftModel.from_pretrained(base_model, "CoNDeNse-AI/GLM-5.1-Qwen3-1.7B-CoNDeNse")

Transformers

How to use CoNDeNse-AI/GLM-5.1-Qwen3-1.7B-CoNDeNse with Transformers:

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

pipe = pipeline("text-generation", model="CoNDeNse-AI/GLM-5.1-Qwen3-1.7B-CoNDeNse")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("CoNDeNse-AI/GLM-5.1-Qwen3-1.7B-CoNDeNse")
model = AutoModelForCausalLM.from_pretrained("CoNDeNse-AI/GLM-5.1-Qwen3-1.7B-CoNDeNse")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use CoNDeNse-AI/GLM-5.1-Qwen3-1.7B-CoNDeNse with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "CoNDeNse-AI/GLM-5.1-Qwen3-1.7B-CoNDeNse"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "CoNDeNse-AI/GLM-5.1-Qwen3-1.7B-CoNDeNse",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/CoNDeNse-AI/GLM-5.1-Qwen3-1.7B-CoNDeNse

SGLang

How to use CoNDeNse-AI/GLM-5.1-Qwen3-1.7B-CoNDeNse 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 "CoNDeNse-AI/GLM-5.1-Qwen3-1.7B-CoNDeNse" \
    --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": "CoNDeNse-AI/GLM-5.1-Qwen3-1.7B-CoNDeNse",
		"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 "CoNDeNse-AI/GLM-5.1-Qwen3-1.7B-CoNDeNse" \
        --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": "CoNDeNse-AI/GLM-5.1-Qwen3-1.7B-CoNDeNse",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use CoNDeNse-AI/GLM-5.1-Qwen3-1.7B-CoNDeNse 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 CoNDeNse-AI/GLM-5.1-Qwen3-1.7B-CoNDeNse 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 CoNDeNse-AI/GLM-5.1-Qwen3-1.7B-CoNDeNse to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for CoNDeNse-AI/GLM-5.1-Qwen3-1.7B-CoNDeNse to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="CoNDeNse-AI/GLM-5.1-Qwen3-1.7B-CoNDeNse",
    max_seq_length=2048,
)

Docker Model Runner
How to use CoNDeNse-AI/GLM-5.1-Qwen3-1.7B-CoNDeNse with Docker Model Runner:
```
docker model run hf.co/CoNDeNse-AI/GLM-5.1-Qwen3-1.7B-CoNDeNse
```

GLM-5.1-Qwen3-1.7B-CoNDeNse / README.md

Andy-ML-And-AI

Update README.md

59e2c18 verified 15 days ago

1.61 kB

base_model: Qwen/Qwen3-1.7B
library_name: peft
pipeline_tag: text-generation
tags:
  - base_model:adapter:Qwen/Qwen3-1.7B
  - lora
  - sft
  - transformers
  - trl
  - unsloth

CoNDeNse-AI/GLM-5.1-Qwen3-1.7B-CoNDeNse

Part of the CoNDeNse project — compressing the reasoning capability of large models into small, deployable ones.

Model Details

Base model: Qwen/Qwen3-1.7B
Method: LoRA (r=32, α=64)
Target modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
Dtype: float16

Training

Dataset: Jackrong/GLM-5.1-Reasoning-1M-Cleaned (75,000 examples)
Optimizer: AdamW 8-bit
Learning rate: 2e-4 with cosine scheduler
Batch size: 1 × 16 gradient accumulation (effective batch = 16)
Max sequence length: 4096
Packing: enabled

Notes

May HALLUCINATE

Usage

from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel
import torch

base_model = AutoModelForCausalLM.from_pretrained(
    "Qwen/Qwen3-1.7B",
    torch_dtype=torch.float16,
    device_map="cuda",
)

tokenizer = AutoTokenizer.from_pretrained("CoNDeNse-AI/GLM-5.1-Qwen3-1.7B-CoNDeNse")
model = PeftModel.from_pretrained(base_model, "CoNDeNse-AI/GLM-5.1-Qwen3-1.7B-CoNDeNse")

prompt = "<|im_start|>user\nYour question here<|im_end|>\n<|im_start|>assistant\n"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

with torch.no_grad():
    outputs = model.generate(**inputs, max_new_tokens=512, temperature=0.7, do_sample=True)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))