Instructions to use AbstractPhil/qwen3.5-0.8b-task_3-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AbstractPhil/qwen3.5-0.8b-task_3-lora with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3.5-0.8B")
model = PeftModel.from_pretrained(base_model, "AbstractPhil/qwen3.5-0.8b-task_3-lora")

Transformers

How to use AbstractPhil/qwen3.5-0.8b-task_3-lora with Transformers:

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

pipe = pipeline("text-generation", model="AbstractPhil/qwen3.5-0.8b-task_3-lora")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("AbstractPhil/qwen3.5-0.8b-task_3-lora", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use AbstractPhil/qwen3.5-0.8b-task_3-lora with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AbstractPhil/qwen3.5-0.8b-task_3-lora"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AbstractPhil/qwen3.5-0.8b-task_3-lora",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/AbstractPhil/qwen3.5-0.8b-task_3-lora

SGLang

How to use AbstractPhil/qwen3.5-0.8b-task_3-lora 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 "AbstractPhil/qwen3.5-0.8b-task_3-lora" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AbstractPhil/qwen3.5-0.8b-task_3-lora",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "AbstractPhil/qwen3.5-0.8b-task_3-lora" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AbstractPhil/qwen3.5-0.8b-task_3-lora",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use AbstractPhil/qwen3.5-0.8b-task_3-lora with Docker Model Runner:
```
docker model run hf.co/AbstractPhil/qwen3.5-0.8b-task_3-lora
```

AbstractPhil commited on 10 days ago

Commit

82fcd8a

verified ·

1 Parent(s): b87716c

Model save

Browse files

Files changed (4) hide show

README.md +60 -0
adapter_config.json +48 -0
adapter_model.safetensors +3 -0
training_args.bin +3 -0

README.md ADDED Viewed

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+---
+library_name: peft
+license: apache-2.0
+base_model: Qwen/Qwen3.5-0.8B
+tags:
+- base_model:adapter:Qwen/Qwen3.5-0.8B
+- lora
+- transformers
+pipeline_tag: text-generation
+model-index:
+- name: qwen3.5-0.8b-task_3-lora
+  results: []
+---
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+# qwen3.5-0.8b-task_3-lora
+This model is a fine-tuned version of [Qwen/Qwen3.5-0.8B](https://huggingface.co/Qwen/Qwen3.5-0.8B) on an unknown dataset.
+## Model description
+More information needed
+## Intended uses & limitations
+More information needed
+## Training and evaluation data
+More information needed
+## Training procedure
+### Training hyperparameters
+The following hyperparameters were used during training:
+- learning_rate: 0.0002
+- train_batch_size: 8
+- eval_batch_size: 8
+- seed: 42
+- gradient_accumulation_steps: 2
+- total_train_batch_size: 16
+- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: cosine
+- lr_scheduler_warmup_steps: 0.03
+- num_epochs: 2
+### Training results
+### Framework versions
+- PEFT 0.19.1
+- Transformers 5.8.1
+- Pytorch 2.10.0+cu128
+- Datasets 4.8.5
+- Tokenizers 0.22.2

adapter_config.json ADDED Viewed

	@@ -0,0 +1,48 @@

+{
+  "alora_invocation_tokens": null,
+  "alpha_pattern": {},
+  "arrow_config": null,
+  "auto_mapping": null,
+  "base_model_name_or_path": "Qwen/Qwen3.5-0.8B",
+  "bias": "none",
+  "corda_config": null,
+  "ensure_weight_tying": false,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": true,
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 64,
+  "lora_bias": false,
+  "lora_dropout": 0.05,
+  "lora_ga_config": null,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "peft_version": "0.19.1",
+  "qalora_group_size": 16,
+  "r": 32,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "gate_proj",
+    "o_proj",
+    "v_proj",
+    "q_proj",
+    "down_proj",
+    "k_proj",
+    "up_proj"
+  ],
+  "target_parameters": null,
+  "task_type": "CAUSAL_LM",
+  "trainable_token_indices": null,
+  "use_bdlora": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false
+}

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