Text Generation
Transformers
TensorBoard
Safetensors
qwen3
Generated from Trainer
axolotl
trl
grpo
conversational
text-generation-inference
Instructions to use dada22231/1eec3dfe-b42d-44d4-b250-e03587e527dd with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dada22231/1eec3dfe-b42d-44d4-b250-e03587e527dd with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="dada22231/1eec3dfe-b42d-44d4-b250-e03587e527dd") 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("dada22231/1eec3dfe-b42d-44d4-b250-e03587e527dd") model = AutoModelForCausalLM.from_pretrained("dada22231/1eec3dfe-b42d-44d4-b250-e03587e527dd", 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 dada22231/1eec3dfe-b42d-44d4-b250-e03587e527dd with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "dada22231/1eec3dfe-b42d-44d4-b250-e03587e527dd" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "dada22231/1eec3dfe-b42d-44d4-b250-e03587e527dd", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/dada22231/1eec3dfe-b42d-44d4-b250-e03587e527dd
- SGLang
How to use dada22231/1eec3dfe-b42d-44d4-b250-e03587e527dd 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 "dada22231/1eec3dfe-b42d-44d4-b250-e03587e527dd" \ --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": "dada22231/1eec3dfe-b42d-44d4-b250-e03587e527dd", "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 "dada22231/1eec3dfe-b42d-44d4-b250-e03587e527dd" \ --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": "dada22231/1eec3dfe-b42d-44d4-b250-e03587e527dd", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use dada22231/1eec3dfe-b42d-44d4-b250-e03587e527dd with Docker Model Runner:
docker model run hf.co/dada22231/1eec3dfe-b42d-44d4-b250-e03587e527dd
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Download README.md from dada22231/1eec3dfe-b42d-44d4-b250-e03587e527dd: direct link, hf CLI and curl.
- Browser
- Download file 4.12 kB
-
https://huggingface.co/dada22231/1eec3dfe-b42d-44d4-b250-e03587e527dd/resolve/8bf901637a33ee7fa775e27c8c19542c917eb723/README.md
- Command line
-
hf download hf://dada22231/1eec3dfe-b42d-44d4-b250-e03587e527dd@8bf901637a33ee7fa775e27c8c19542c917eb723/README.md
-
curl -L -o README.md https://huggingface.co/dada22231/1eec3dfe-b42d-44d4-b250-e03587e527dd/resolve/8bf901637a33ee7fa775e27c8c19542c917eb723/README.md
4.12 kB
metadata
library_name: peft
license: other
base_model: Qwen/Qwen2.5-3B-Instruct
tags:
- axolotl
- generated_from_trainer
- trl
- grpo
model-index:
- name: ebbfdd3e-6a3f-401d-9cc0-4d03a358be64
results: []
See axolotl config
axolotl version: 0.10.0.dev0
adapter: lora
adapter_config:
base_model_name_or_path: Qwen/Qwen2.5-3B-Instruct
inference_mode: false
lora_alpha: 256
lora_dropout: 0.05
r: 128
task_type: CAUSAL_LM
base_model: Qwen/Qwen2.5-3B-Instruct
base_model_name_or_path: Qwen/Qwen2.5-3B-Instruct
bf16: true
chat_template: llama3
dataloader_num_workers: 0
dataloader_pin_memory: false
dataset_prepared_path: null
datasets:
- data_files:
- 0bc630b0fd660cf4_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/
type:
field_instruction: instruct
field_output: output
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
ddp_broadcast_buffers: false
ddp_bucket_cap_mb: 25
ddp_timeout: 7200
debug: null
deepspeed: null
evaluation_strategy: 'no'
flash_attention: true
flash_attn_cross_entropy: true
flash_attn_rms_norm: true
fp16: false
fsdp: null
fsdp_config: null
gpu_memory_limit: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
gradient_checkpointing_kwargs:
use_reentrant: false
group_by_length: false
hub_model_id: dada22231/ebbfdd3e-6a3f-401d-9cc0-4d03a358be64
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0002
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 256
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_modules_to_save:
- embed_tokens
- lm_head
lora_r: 128
lora_target_linear: true
lr_scheduler: constant_with_warmup
max_memory: null
max_steps: 1500
micro_batch_size: 8
mlflow_experiment_name: /tmp/0bc630b0fd660cf4_train_data.json
model_type: AutoModelForCausalLM
optimizer: adamw_torch_fused
output_dir: ./outputs
pad_to_sequence_len: true
peft:
base_model_name_or_path: Qwen/Qwen2.5-3B-Instruct
push_to_hub: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: true
save_only_model: true
save_safetensors: true
save_steps: 75
save_strategy: steps
save_total_limit: 5
sequence_len: 4096
special_tokens: null
strict: false
tf32: true
tokenizer_type: AutoTokenizer
torch_compile: false
torch_compile_backend: inductor
train_on_inputs: false
trust_remote_code: true
val_set_size: 0
wandb_entity: null
wandb_mode: online
wandb_name: 9b662779-43ad-43c1-909a-c215f8ccbfa7
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 9b662779-43ad-43c1-909a-c215f8ccbfa7
warmup_steps: 150
weight_decay: 0.01
xformers_attention: null
ebbfdd3e-6a3f-401d-9cc0-4d03a358be64
This model is a fine-tuned version of Qwen/Qwen2.5-3B-Instruct 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: 4
- total_train_batch_size: 32
- 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: constant_with_warmup
- lr_scheduler_warmup_steps: 150
- training_steps: 1500
Training results
Framework versions
- PEFT 0.15.2
- Transformers 4.52.3
- Pytorch 2.5.1+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1