Instructions to use fares-boutriga/Damork-tx-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use fares-boutriga/Damork-tx-1 with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-14B-Instruct")
model = PeftModel.from_pretrained(base_model, "fares-boutriga/Damork-tx-1")

Transformers

How to use fares-boutriga/Damork-tx-1 with Transformers:

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

pipe = pipeline("text-generation", model="fares-boutriga/Damork-tx-1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("fares-boutriga/Damork-tx-1")
model = AutoModelForCausalLM.from_pretrained("fares-boutriga/Damork-tx-1")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use fares-boutriga/Damork-tx-1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "fares-boutriga/Damork-tx-1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "fares-boutriga/Damork-tx-1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/fares-boutriga/Damork-tx-1

SGLang

How to use fares-boutriga/Damork-tx-1 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 "fares-boutriga/Damork-tx-1" \
    --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": "fares-boutriga/Damork-tx-1",
		"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 "fares-boutriga/Damork-tx-1" \
        --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": "fares-boutriga/Damork-tx-1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use fares-boutriga/Damork-tx-1 with Docker Model Runner:
```
docker model run hf.co/fares-boutriga/Damork-tx-1
```

See axolotl config

axolotl version: 0.13.0.dev0

base_model: Qwen/Qwen2.5-14B-Instruct

adapter: qlora
load_in_4bit: true
load_in_8bit: false

bf16: true
torch_dtype: bfloat16

bnb_4bit_quant_type: nf4
bnb_4bit_compute_dtype: bfloat16
bnb_4bit_use_double_quant: true

output_dir: ./outputs/mymodel

datasets:
  - path: fares-boutriga/DamorkDataSet1
    type: chat_template
    data_files:
      - damork_dataset.axolotl.train.jsonl

micro_batch_size: 1
gradient_accumulation_steps: 8
sequence_len: 2048
num_epochs: 1
learning_rate: 2e-4

optimizer: adamw_bnb_8bit
lr_scheduler: cosine
weight_decay: 0.0

lora_r: 8
lora_alpha: 16
lora_dropout: 0.05
lora_target_modules:
  - q_proj
  - k_proj
  - v_proj
  - o_proj
  - gate_proj
  - up_proj
  - down_proj

val_set_size: 0.0
train_on_inputs: false

save_safetensors: true
save_only_model: false

outputs/mymodel

This model is a fine-tuned version of Qwen/Qwen2.5-14B-Instruct on the fares-boutriga/DamorkDataSet1 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: 1
eval_batch_size: 1
seed: 42
gradient_accumulation_steps: 8
total_train_batch_size: 8
optimizer: Use OptimizerNames.ADAMW_BNB 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: 2
training_steps: 40

Training results

Framework versions

PEFT 0.17.1
Transformers 4.57.0
Pytorch 2.7.1+cu126
Datasets 4.0.0
Tokenizers 0.22.1

Downloads last month: -

Safetensors

Model size

15B params

Tensor type

BF16

Model tree for fares-boutriga/Damork-tx-1

Base model

Qwen/Qwen2.5-14B

Finetuned

Qwen/Qwen2.5-14B-Instruct

Adapter

(353)

this model