Instructions to use allura-forge/qwen3.5-9b-seed2.0-claude-dist-v2-qlora-ckpts with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use allura-forge/qwen3.5-9b-seed2.0-claude-dist-v2-qlora-ckpts with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3.5-9B")
model = PeftModel.from_pretrained(base_model, "allura-forge/qwen3.5-9b-seed2.0-claude-dist-v2-qlora-ckpts")

Transformers

How to use allura-forge/qwen3.5-9b-seed2.0-claude-dist-v2-qlora-ckpts with Transformers:

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

pipe = pipeline("text-generation", model="allura-forge/qwen3.5-9b-seed2.0-claude-dist-v2-qlora-ckpts")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("allura-forge/qwen3.5-9b-seed2.0-claude-dist-v2-qlora-ckpts", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use allura-forge/qwen3.5-9b-seed2.0-claude-dist-v2-qlora-ckpts with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "allura-forge/qwen3.5-9b-seed2.0-claude-dist-v2-qlora-ckpts"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "allura-forge/qwen3.5-9b-seed2.0-claude-dist-v2-qlora-ckpts",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/allura-forge/qwen3.5-9b-seed2.0-claude-dist-v2-qlora-ckpts

SGLang

How to use allura-forge/qwen3.5-9b-seed2.0-claude-dist-v2-qlora-ckpts 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 "allura-forge/qwen3.5-9b-seed2.0-claude-dist-v2-qlora-ckpts" \
    --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": "allura-forge/qwen3.5-9b-seed2.0-claude-dist-v2-qlora-ckpts",
		"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 "allura-forge/qwen3.5-9b-seed2.0-claude-dist-v2-qlora-ckpts" \
        --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": "allura-forge/qwen3.5-9b-seed2.0-claude-dist-v2-qlora-ckpts",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use allura-forge/qwen3.5-9b-seed2.0-claude-dist-v2-qlora-ckpts with Docker Model Runner:
```
docker model run hf.co/allura-forge/qwen3.5-9b-seed2.0-claude-dist-v2-qlora-ckpts
```

output-fizz-v2

This model is a fine-tuned version of Qwen/Qwen3.5-9B.

W&B run: https://wandb.ai/cooawoo-personal/huggingface/runs/jelbo6gf

Training procedure

Hyperparameters

Parameter	Value
Learning rate	`0.0002`
LR scheduler	SchedulerType.CONSTANT
Per-device batch size	2
Gradient accumulation	4
Effective batch size	8
Epochs	2
Max sequence length	4096
Optimizer	OptimizerNames.ADAMW_TORCH
Weight decay	0.01
Warmup ratio	0.05
Max gradient norm	1.0
Precision	bf16
Loss type	nll
Chunked cross-entropy	yes

LoRA configuration

Parameter	Value
Rank (r)	64
Alpha	512
Target modules	attn.proj, down_proj, gate_proj, in_proj_a, in_proj_b, in_proj_qkv, in_proj_z, k_proj, linear_fc1, linear_fc2, o_proj, out_proj, q_proj, qkv, up_proj, v_proj
Quantization	4-bit (nf4)

Dataset statistics

Dataset	Samples	Total tokens	Trainable tokens
allura-forge/doubao-seed2.0-claude-distill-v1-qwen3.5-format	3,644	7,187,856	6,625,087

Training config

model_name_or_path: Qwen/Qwen3.5-9B
bf16: true
gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
use_liger: true
use_cce: true
max_length: 4096
learning_rate: 0.0002
warmup_ratio: 0.05
weight_decay: 0.01
lr_scheduler_type: constant
per_device_train_batch_size: 2
gradient_accumulation_steps: 4
optim: adamw_torch
max_grad_norm: 1.0
use_peft: true
load_in_4bit: true
lora_r: 64
lora_alpha: 512
lora_dropout: 0.0
use_rslora: false
logging_steps: 1
disable_tqdm: false
save_strategy: steps
save_steps: 500
save_total_limit: null
report_to: wandb
output_dir: output-fizz-v2
data_config: data.yaml
prepared_dataset: prepared
num_train_epochs: 2
saves_per_epoch: 1
run_name: qwen35-9b-qlora-fizz

Data config

datasets:
- path: allura-forge/doubao-seed2.0-claude-distill-v1-qwen3.5-format
  type: conversational
  truncation_strategy: drop
shuffle_datasets: true
shuffle_combined: true
shuffle_seed: 42
eval_split: 0.0
split_seed: 42
assistant_only_loss: true

Framework versions

PEFT 0.18.1
Loft: 0.1.0
Transformers: 5.2.0
Pytorch: 2.6.0
Datasets: 4.6.1
Tokenizers: 0.22.2

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Model tree for allura-forge/qwen3.5-9b-seed2.0-claude-dist-v2-qlora-ckpts

Base model

Qwen/Qwen3.5-9B-Base

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Qwen/Qwen3.5-9B

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