Instructions to use cloudbjorn/merged_Qwen3.5-9B_no_robots_0328_2008 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use cloudbjorn/merged_Qwen3.5-9B_no_robots_0328_2008 with Transformers:

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

pipe = pipeline("text-generation", model="cloudbjorn/merged_Qwen3.5-9B_no_robots_0328_2008")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("cloudbjorn/merged_Qwen3.5-9B_no_robots_0328_2008")
model = AutoModelForCausalLM.from_pretrained("cloudbjorn/merged_Qwen3.5-9B_no_robots_0328_2008", device_map="auto")
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 Settings

vLLM

How to use cloudbjorn/merged_Qwen3.5-9B_no_robots_0328_2008 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "cloudbjorn/merged_Qwen3.5-9B_no_robots_0328_2008"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "cloudbjorn/merged_Qwen3.5-9B_no_robots_0328_2008",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/cloudbjorn/merged_Qwen3.5-9B_no_robots_0328_2008

SGLang

How to use cloudbjorn/merged_Qwen3.5-9B_no_robots_0328_2008 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 "cloudbjorn/merged_Qwen3.5-9B_no_robots_0328_2008" \
    --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": "cloudbjorn/merged_Qwen3.5-9B_no_robots_0328_2008",
		"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 "cloudbjorn/merged_Qwen3.5-9B_no_robots_0328_2008" \
        --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": "cloudbjorn/merged_Qwen3.5-9B_no_robots_0328_2008",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use cloudbjorn/merged_Qwen3.5-9B_no_robots_0328_2008 with Docker Model Runner:
```
docker model run hf.co/cloudbjorn/merged_Qwen3.5-9B_no_robots_0328_2008
```

merged_Qwen3.5-9B_no_robots_0328_2008

This is a fine-tuned and merged version of the Qwen3.5 9B model, trained on the HuggingFaceH4/no_robots dataset.

This model serves as a demonstration artifact generated by the Eschaton Engine, a managed training infrastructure built for Cloudbjorn. It was trained using pre-configured, highly optimized Hugging Face scripts designed to democratize fine-tuning on dynamic cloud compute.

Model Capabilities

Massive Context Window: Supports up to 262,144 tokens.
Advanced Formatting: The native chat template supports structured <tool_call> generation and <think> reasoning blocks.
Precision: bfloat16

Training Details

Base Model: Qwen3.5-9B (Qwen3_5ForCausalLM)
Dataset: HuggingFaceH4/no_robots
Training Framework: Eschaton Engine (Cloudbjorn)
Format: Merged (Base + LoRA)

Training Precision:

Quantization: 4-bit (NF4) via BitsAndBytes
Compute Dtype: bfloat16

LoRA Parameters:

r: 16
lora_alpha: 16
target_modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
lora_dropout: 0.05

Training Hyperparameters:

Optimizer: 8-bit Paged AdamW
Effective Batch Size: 32 (Dynamically scaled)
Learning Rate: 2e-4
LR Scheduler: Linear
Epochs: 1
Training Sequence Length: 2048
Warmup Steps: 50
Weight Decay: 0.01

Downloads last month: 9

Safetensors

Model size

9B params

Tensor type

BF16

Model tree for cloudbjorn/merged_Qwen3.5-9B_no_robots_0328_2008

Base model

Qwen/Qwen3.5-9B-Base

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Qwen/Qwen3.5-9B

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cloudbjorn
/

merged_Qwen3.5-9B_no_robots_0328_2008

merged_Qwen3.5-9B_no_robots_0328_2008

Model Capabilities

Training Details

Model tree for cloudbjorn/merged_Qwen3.5-9B_no_robots_0328_2008

Dataset used to train cloudbjorn/merged_Qwen3.5-9B_no_robots_0328_2008