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
```

Andy-ML-And-AI commited on 21 days ago

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@@ -10,200 +10,60 @@ tags:
 - trl
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
 ## Model Details
-### Model Description
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-## Uses
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-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-## How to Get Started with the Model
-Use the code below to get started with the model.
-[More Information Needed]
-## Training Details
-### Training Data
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-### Training Procedure
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-#### Preprocessing [optional]
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-#### Training Hyperparameters
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-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-- **Hardware Type:** [More Information Needed]
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-## Technical Specifications [optional]
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-### Framework versions
-- PEFT 0.18.1

 - trl
 ---
+# 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
+## Training Progress
+This is a partial checkpoint. Training was stopped early at step 209 with a training loss of ~1.90. Full training is pending.
+## Usage
+```python
+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))
+```
+## Notes
+- This adapter must be loaded on top of the original `Qwen/Qwen3-1.7B` base model
+- Partial checkpoint — loss was still descending at time of save, full run pending
+## License
+Inherits the license of Qwen/Qwen3-1.7B. Adapter weights are released under MIT.