Instructions to use ermiaazarkhalili/Qwen3.5-9B-SFT-Fable5-Glint with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ermiaazarkhalili/Qwen3.5-9B-SFT-Fable5-Glint with Transformers:

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

pipe = pipeline("text-generation", model="ermiaazarkhalili/Qwen3.5-9B-SFT-Fable5-Glint")
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)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("ermiaazarkhalili/Qwen3.5-9B-SFT-Fable5-Glint")
model = AutoModelForMultimodalLM.from_pretrained("ermiaazarkhalili/Qwen3.5-9B-SFT-Fable5-Glint", 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=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ermiaazarkhalili/Qwen3.5-9B-SFT-Fable5-Glint with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ermiaazarkhalili/Qwen3.5-9B-SFT-Fable5-Glint"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ermiaazarkhalili/Qwen3.5-9B-SFT-Fable5-Glint",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ermiaazarkhalili/Qwen3.5-9B-SFT-Fable5-Glint

SGLang

How to use ermiaazarkhalili/Qwen3.5-9B-SFT-Fable5-Glint 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 "ermiaazarkhalili/Qwen3.5-9B-SFT-Fable5-Glint" \
    --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": "ermiaazarkhalili/Qwen3.5-9B-SFT-Fable5-Glint",
		"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 "ermiaazarkhalili/Qwen3.5-9B-SFT-Fable5-Glint" \
        --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": "ermiaazarkhalili/Qwen3.5-9B-SFT-Fable5-Glint",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Desktop
Docker Model Runner
How to use ermiaazarkhalili/Qwen3.5-9B-SFT-Fable5-Glint with Docker Model Runner:
```
docker model run hf.co/ermiaazarkhalili/Qwen3.5-9B-SFT-Fable5-Glint
```

Qwen3.5-9B-SFT-Fable5-Glint

A LoRA fine-tune of unsloth/Qwen3.5-9B, supervised fine-tuned on ermiaazarkhalili/Fable-5-Glint-Clean (private).


Base model	`unsloth/Qwen3.5-9B`
Architecture	`Qwen3_5ForConditionalGeneration`
Parameters	9.7B
Training data	`ermiaazarkhalili/Fable-5-Glint-Clean` (private)
Method	LoRA supervised fine-tuning via Unsloth + TRL
License	`apache-2.0` (inherited from the base model)

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "ermiaazarkhalili/Qwen3.5-9B-SFT-Fable5-Glint"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, dtype='auto', device_map='auto')

messages = [{"role": "user", "content": "Explain gradient checkpointing in two sentences."}]
inputs = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True, return_tensors='pt'
).to(model.device)

outputs = model.generate(inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Training configuration

Setting	Value
LoRA rank (r)	16
LoRA alpha	16
Learning rate	0.0002
Epochs	3
Effective batch size	8 (1 x 8 grad accum)
Max sequence length	4096
Base precision	4-bit (QLoRA)
Target modules	`q_proj`, `k_proj`, `v_proj`, `o_proj`, `gate_proj`, `up_proj`, `down_proj`, `out_proj`

Held-out evaluation

Next-token accuracy on a deterministic held-out split of ermiaazarkhalili/Fable-5-Glint-Clean (n = 199 samples), scored against the base model unsloth/Qwen3.5-9B. Assistant tokens only; both models are scored identically.

Metric	Base	This model	Δ
Top-1 accuracy	0.6461	0.7329	+0.0869
Top-5 accuracy	0.8842	0.9385	+0.0543

A delta measures how far fine-tuning moved this model from its own starting point; it is not a ranking against other models, which start from different baselines.

Observed training loss

Measured from our SLURM logs for this configuration. These are training-loss observations only — see the held-out evaluation above for measured accuracy.

SLURM job	Steps	First loss	Final loss
`46021013`	1,554	0.9945	0.6509
`45987993`	300	1.1245	0.8734

Limitations

No benchmark evaluation has been run on this checkpoint. The only reported numbers are training-loss observations.
Inherits the biases, knowledge cutoff and failure modes of the base model.
Fine-tuned on a single instruction-following dataset; behaviour outside that distribution is untested.
LoRA adapters were merged into the base weights, so the merged model cannot be detached from this fine-tune.

Reproducing

Trained by notebooks/fable_distillation_qwen35-9b_fable-glint_unsloth.ipynb, executed non-interactively with papermill on a SLURM H100 partition (Unsloth + TRL, LoRA).

Card generated from the training run's own configuration and logs by scripts/generate_hub_model_card.py.