Instructions to use flashback2k/FlashModel-Qwen3.5-9B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use flashback2k/FlashModel-Qwen3.5-9B with Transformers:

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

pipe = pipeline("text-generation", model="flashback2k/FlashModel-Qwen3.5-9B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("flashback2k/FlashModel-Qwen3.5-9B")
model = AutoModelForCausalLM.from_pretrained("flashback2k/FlashModel-Qwen3.5-9B", 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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use flashback2k/FlashModel-Qwen3.5-9B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "flashback2k/FlashModel-Qwen3.5-9B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "flashback2k/FlashModel-Qwen3.5-9B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/flashback2k/FlashModel-Qwen3.5-9B

SGLang

How to use flashback2k/FlashModel-Qwen3.5-9B 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 "flashback2k/FlashModel-Qwen3.5-9B" \
    --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": "flashback2k/FlashModel-Qwen3.5-9B",
		"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 "flashback2k/FlashModel-Qwen3.5-9B" \
        --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": "flashback2k/FlashModel-Qwen3.5-9B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use flashback2k/FlashModel-Qwen3.5-9B with Docker Model Runner:
```
docker model run hf.co/flashback2k/FlashModel-Qwen3.5-9B
```

FlashModel-Qwen3.5-9B / README.md

flashback2k

Model card: usage, training details, limitations

1eb5124 verified 25 days ago

preview code

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	---
	base_model:
	- Qwen/Qwen3.5-9B
	base_model_relation: finetune
	library_name: transformers
	license: apache-2.0
	license_link: https://huggingface.co/Qwen/Qwen3.5-9B/blob/main/LICENSE
	pipeline_tag: text-generation
	language:
	- en
	tags:
	- qwen3.5
	- reasoning
	- tool-calling
	- distillation
	- sft
	- lora
	datasets:
	- nvidia/Nemotron-SFT-Math-v4
	- nvidia/Nemotron-SFT-Competitive-Programming-v2
	- nvidia/Nemotron-SFT-Agentic-v2
	- nvidia/Nemotron-SFT-Instruction-Following-Chat-v3
	- nvidia/Nemotron-SFT-Science-v2
	---

	# ⚡ FlashModel-Qwen3.5-9B

	A reasoning / coding / tool-calling fine-tune of [Qwen/Qwen3.5-9B](https://huggingface.co/Qwen/Qwen3.5-9B),
	distilled from open-weight frontier teachers (DeepSeek-V4-Pro, DeepSeek-R1-0528, GLM-4.6, DeepSeek-V3.2, GPT-OSS-120B, Kimi-K2).

	GGUF quants (llama.cpp, Ollama, LM Studio): [flashback2k/FlashModel-Qwen3.5-9B-GGUF](https://huggingface.co/flashback2k/FlashModel-Qwen3.5-9B-GGUF)

	Merged BF16 weights, text-only (`Qwen3_5ForCausalLM`, no vision tower, no MTP head).

	## Usage

	### Transformers

	```python
	from transformers import AutoModelForCausalLM, AutoTokenizer

	model_id = "flashback2k/FlashModel-Qwen3.5-9B"
	tokenizer = AutoTokenizer.from_pretrained(model_id)
	model = AutoModelForCausalLM.from_pretrained(model_id, dtype="auto", device_map="auto")

	messages = [{"role": "user", "content": "Write a Python function that checks whether a number is prime."}]
	inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
	output = model.generate(inputs, max_new_tokens=4096, do_sample=True, temperature=0.6, top_p=0.95, top_k=20)
	print(tokenizer.decode(output[0][inputs.shape[-1]:], skip_special_tokens=True))
	```

	Qwen3.5 support requires a recent `transformers` release.

	### vLLM / SGLang

	```bash
	vllm serve flashback2k/FlashModel-Qwen3.5-9B --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder
	```

	### Recommended sampling

	These are the base model's official recommendations ([Qwen3.5 model card](https://huggingface.co/Qwen/Qwen3.5-9B)); they were not re-tuned for the fine-tune.

	\| Mode \| temperature \| top_p \| top_k \| min_p \| presence_penalty \|
	\|:--\|--:\|--:\|--:\|--:\|--:\|
	\| Thinking, general \| 1.0 \| 0.95 \| 20 \| 0.0 \| 1.5 \|
	\| Thinking, precise coding \| 0.6 \| 0.95 \| 20 \| 0.0 \| 0.0 \|
	\| Non-thinking, general \| 0.7 \| 0.8 \| 20 \| 0.0 \| 1.5 \|

	Always pass `--jinja` so the embedded Qwen3.5 chat template (thinking blocks, tool calls) is used.

	### Reasoning-budget tag

	Training system prompts started with a reasoning-budget tag chosen from the length of the teacher's reasoning:

	```
	<\|reasoning_budget\|>medium<\|/reasoning_budget\|>
	```

	Values: `off`, `low`, `medium`, `high`, `xhigh`, `max`. The tag's effect on output length has not been measured yet; treat it as experimental.

	### Reasoning-budget tag

	Training system prompts started with a reasoning-budget tag chosen from the length of the teacher's reasoning:

	```
	<\|reasoning_budget\|>medium<\|/reasoning_budget\|>
	```

	Values: `off`, `low`, `medium`, `high`, `xhigh`, `max`. The tag's effect on output length has not been measured yet; treat it as experimental.

	## About the fine-tune

	\| \| \|
	\|:--\|:--\|
	\| Base \| Qwen/Qwen3.5-9B \|
	\| Method \| LoRA r=128 (RSLoRA, α=32) on attention + MLP projections, merged \|
	\| Data \| 9,638 examples / 45M tokens, loss on assistant turns only \|
	\| Context in training \| up to 16,384 tokens (longer examples dropped, never truncated) \|
	\| Schedule \| 1 epoch, 600 steps, lr 5e-5 cosine \|
	\| Held-out eval loss \| 0.5745 (step 100) → 0.5586 (step 600) \|

	### Training data and teachers

	Only open-weight teachers whose licenses allow training on their outputs:

	\| Share \| Domain \| Dataset \| Teacher \|
	\|--:\|:--\|:--\|:--\|
	\| 40% \| Math (answers verified against references) \| [nvidia/Nemotron-SFT-Math-v4](https://huggingface.co/datasets/nvidia/Nemotron-SFT-Math-v4) \| DeepSeek-V4-Pro \|
	\| 22% \| Competitive programming (Python) \| [nvidia/Nemotron-SFT-Competitive-Programming-v2](https://huggingface.co/datasets/nvidia/Nemotron-SFT-Competitive-Programming-v2) \| DeepSeek-R1-0528 \|
	\| 16% \| Multi-turn tool calling (judge-filtered) \| [nvidia/Nemotron-SFT-Agentic-v2](https://huggingface.co/datasets/nvidia/Nemotron-SFT-Agentic-v2) \| GLM-4.6 / DeepSeek-V3.2 \|
	\| 11% \| Instruction following \| [nvidia/Nemotron-SFT-Instruction-Following-Chat-v3](https://huggingface.co/datasets/nvidia/Nemotron-SFT-Instruction-Following-Chat-v3) \| GPT-OSS-120B \|
	\| 11% \| Science reasoning \| [nvidia/Nemotron-SFT-Science-v2](https://huggingface.co/datasets/nvidia/Nemotron-SFT-Science-v2) \| GPT-OSS / Kimi-K2 / DeepSeek-V3.2 \|

	Datasets © NVIDIA, CC BY 4.0 (some Math StackExchange-derived samples CC BY-SA 4.0).

	The training run's step count, sample count and eval-loss history are in [`training_metadata.json`](training_metadata.json).

	## ⚠️ Status and known limitations

	- No benchmarks yet. Lower held-out loss means the model imitates the teachers more closely; it does not by itself prove
	it beats the stock Qwen3.5-9B. Comparative evals are planned and will be added here.
	- English-centric. All training data was English. On non-English prompts (e.g. Russian) the model often reasons in English.
	- Math and code examples longer than 16k tokens were excluded, which skews those domains toward shorter problems.
	- Inherits the base model's limitations and biases.

	## Credits

	[Qwen team](https://huggingface.co/Qwen) for Qwen3.5 · [NVIDIA](https://huggingface.co/nvidia) for the Nemotron SFT datasets ·
	DeepSeek, Zhipu AI (GLM), OpenAI (GPT-OSS) and Moonshot AI (Kimi) for open-weight teachers ·
	[ggml-org/llama.cpp](https://github.com/ggml-org/llama.cpp).