Text Generation
Transformers
Safetensors
qwen2
Generated from Trainer
rl-swarm
grpo
gensyn
I am deadly deadly dingo
trl
conversational
text-generation-inference
Instructions to use haedahae/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-deadly_deadly_dingo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use haedahae/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-deadly_deadly_dingo with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="haedahae/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-deadly_deadly_dingo") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("haedahae/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-deadly_deadly_dingo") model = AutoModelForCausalLM.from_pretrained("haedahae/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-deadly_deadly_dingo", 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=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use haedahae/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-deadly_deadly_dingo with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "haedahae/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-deadly_deadly_dingo" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "haedahae/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-deadly_deadly_dingo", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/haedahae/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-deadly_deadly_dingo
- SGLang
How to use haedahae/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-deadly_deadly_dingo 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 "haedahae/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-deadly_deadly_dingo" \ --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": "haedahae/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-deadly_deadly_dingo", "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 "haedahae/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-deadly_deadly_dingo" \ --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": "haedahae/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-deadly_deadly_dingo", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use haedahae/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-deadly_deadly_dingo with Docker Model Runner:
docker model run hf.co/haedahae/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-deadly_deadly_dingo
File size: 8,306 Bytes
320cb20 ecba1a4 320cb20 97ed018 ecba1a4 97ed018 320cb20 b43f222 97ed018 320cb20 97ed018 ce3c1f4 97ed018 320cb20 97ed018 ecba1a4 97ed018 320cb20 97ed018 2e6d6d8 320cb20 97ed018 320cb20 97ed018 320cb20 97ed018 ecba1a4 97ed018 320cb20 97ed018 320cb20 bd88469 97ed018 320cb20 97ed018 320cb20 97ed018 ecba1a4 97ed018 320cb20 97ed018 320cb20 97ed018 0d3f722 97ed018 320cb20 97ed018 ecba1a4 97ed018 320cb20 97ed018 320cb20 97ed018 320cb20 2e6d6d8 97ed018 320cb20 97ed018 ecba1a4 97ed018 320cb20 97ed018 320cb20 97ed018 1b94f9a 97ed018 320cb20 97ed018 ecba1a4 97ed018 320cb20 97ed018 320cb20 2e6d6d8 97ed018 320cb20 97ed018 320cb20 97ed018 ecba1a4 97ed018 320cb20 97ed018 2e6d6d8 320cb20 2e6d6d8 97ed018 cdd3d03 97ed018 320cb20 97ed018 ecba1a4 97ed018 320cb20 97ed018 320cb20 97ed018 320cb20 97ed018 320cb20 97ed018 ecba1a4 97ed018 320cb20 97ed018 320cb20 bd88469 97ed018 320cb20 97ed018 320cb20 ecba1a4 320cb20 97ed018 2676d72 320cb20 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 | {
"best_global_step": null,
"best_metric": null,
"best_model_checkpoint": null,
"epoch": 1.0,
"eval_steps": 500,
"global_step": 20,
"is_hyper_param_search": false,
"is_local_process_zero": true,
"is_world_process_zero": true,
"log_history": [
{
"completion_length": 193.34375,
"epoch": 0.1,
"grad_norm": 25.437278747558594,
"kl": 0.0,
"learning_rate": 4.965903258506806e-07,
"loss": 0.0,
"reward": 3.6266137938946486,
"reward_std": 0.8307934795739129,
"rewards/concensus_correctness_reward_func": 0.9024999998509884,
"rewards/consensus_reward_func": 0.875,
"rewards/cumulative_reward_2": 0.0,
"rewards/final_correctness_reward_func": 0.1875,
"rewards/question_recreation_reward_func": 0.6582388319075108,
"rewards/soft_format_reward_func": 0.0,
"rewards/strict_format_reward_func": 0.234375,
"rewards/xmlcount_reward_func": 0.7689999984577298,
"step": 2
},
{
"completion_length": 228.46875,
"epoch": 0.2,
"grad_norm": 39.24359130859375,
"kl": 1.5343358425889164,
"learning_rate": 4.698684378016222e-07,
"loss": 0.0015,
"reward": 6.112734526395798,
"reward_std": 1.0056872393470258,
"rewards/concensus_correctness_reward_func": 1.8086874820291996,
"rewards/consensus_reward_func": 1.75,
"rewards/cumulative_reward_2": 0.0,
"rewards/final_correctness_reward_func": 0.25,
"rewards/question_recreation_reward_func": 0.9244220145046711,
"rewards/soft_format_reward_func": 0.0,
"rewards/strict_format_reward_func": 0.328125,
"rewards/xmlcount_reward_func": 1.0515000000596046,
"step": 4
},
{
"completion_length": 166.5625,
"epoch": 0.3,
"grad_norm": 138.03665161132812,
"kl": 2.744431208819151,
"learning_rate": 4.193203929064353e-07,
"loss": 0.0027,
"reward": 5.025258347392082,
"reward_std": 0.2811972763593076,
"rewards/concensus_correctness_reward_func": 1.3959999848157167,
"rewards/consensus_reward_func": 1.5,
"rewards/cumulative_reward_2": 0.0,
"rewards/final_correctness_reward_func": 0.0,
"rewards/question_recreation_reward_func": 0.7735082694562152,
"rewards/soft_format_reward_func": 0.0,
"rewards/strict_format_reward_func": 0.328125,
"rewards/xmlcount_reward_func": 1.027624998241663,
"step": 6
},
{
"completion_length": 173.75,
"epoch": 0.4,
"grad_norm": 67.41569519042969,
"kl": 6.022588826715946,
"learning_rate": 3.5042385616324236e-07,
"loss": 0.006,
"reward": 6.697861954569817,
"reward_std": 0.6546344570815563,
"rewards/concensus_correctness_reward_func": 1.997437495738268,
"rewards/consensus_reward_func": 1.8125,
"rewards/cumulative_reward_2": 0.0,
"rewards/final_correctness_reward_func": 0.375,
"rewards/question_recreation_reward_func": 0.913518188521266,
"rewards/soft_format_reward_func": 0.0,
"rewards/strict_format_reward_func": 0.421875,
"rewards/xmlcount_reward_func": 1.1775312554091215,
"step": 8
},
{
"completion_length": 136.65625,
"epoch": 0.5,
"grad_norm": 332.54534912109375,
"kl": 17.14331202954054,
"learning_rate": 2.706448363680831e-07,
"loss": 0.0171,
"reward": 7.414227694272995,
"reward_std": 0.496162755342084,
"rewards/concensus_correctness_reward_func": 2.4219999983906746,
"rewards/consensus_reward_func": 1.9375,
"rewards/cumulative_reward_2": 0.0,
"rewards/final_correctness_reward_func": 0.625,
"rewards/question_recreation_reward_func": 0.7981027774512768,
"rewards/soft_format_reward_func": 0.0,
"rewards/strict_format_reward_func": 0.4375,
"rewards/xmlcount_reward_func": 1.1941250003874302,
"step": 10
},
{
"completion_length": 160.6875,
"epoch": 0.6,
"grad_norm": 29.822416305541992,
"kl": 28.22160056978464,
"learning_rate": 1.886286282148002e-07,
"loss": 0.0282,
"reward": 6.131470635533333,
"reward_std": 1.1960517894112854,
"rewards/concensus_correctness_reward_func": 1.8389999866485596,
"rewards/consensus_reward_func": 1.625,
"rewards/cumulative_reward_2": 0.0,
"rewards/final_correctness_reward_func": 0.3125,
"rewards/question_recreation_reward_func": 0.8451581280678511,
"rewards/soft_format_reward_func": 0.0,
"rewards/strict_format_reward_func": 0.375,
"rewards/xmlcount_reward_func": 1.1348125003278255,
"step": 12
},
{
"completion_length": 183.0,
"epoch": 0.7,
"grad_norm": 36.994346618652344,
"kl": 8.928391239605844,
"learning_rate": 1.1326296046939333e-07,
"loss": 0.0089,
"reward": 6.382866933941841,
"reward_std": 0.9713909234851599,
"rewards/concensus_correctness_reward_func": 2.0543750189244747,
"rewards/consensus_reward_func": 1.6875,
"rewards/cumulative_reward_2": 0.0,
"rewards/final_correctness_reward_func": 0.375,
"rewards/question_recreation_reward_func": 0.8999295085668564,
"rewards/soft_format_reward_func": 0.0,
"rewards/strict_format_reward_func": 0.359375,
"rewards/xmlcount_reward_func": 1.0066875014454126,
"step": 14
},
{
"completion_length": 148.59375,
"epoch": 0.8,
"grad_norm": 33.646080017089844,
"kl": 0.7313799187541008,
"learning_rate": 5.271487265090163e-08,
"loss": 0.0007,
"reward": 6.244778722524643,
"reward_std": 0.8219166721682996,
"rewards/concensus_correctness_reward_func": 1.810937486588955,
"rewards/consensus_reward_func": 1.875,
"rewards/cumulative_reward_2": 0.0,
"rewards/final_correctness_reward_func": 0.1875,
"rewards/question_recreation_reward_func": 0.8629974937066436,
"rewards/soft_format_reward_func": 0.0,
"rewards/strict_format_reward_func": 0.359375,
"rewards/xmlcount_reward_func": 1.1489687487483025,
"step": 16
},
{
"completion_length": 125.125,
"epoch": 0.9,
"grad_norm": 263.7424011230469,
"kl": 0.666266948916018,
"learning_rate": 1.3545689574841341e-08,
"loss": 0.0007,
"reward": 5.990852370858192,
"reward_std": 0.24256473960122094,
"rewards/concensus_correctness_reward_func": 1.8007499873638153,
"rewards/consensus_reward_func": 1.75,
"rewards/cumulative_reward_2": 0.0,
"rewards/final_correctness_reward_func": 0.0,
"rewards/question_recreation_reward_func": 0.7682274281978607,
"rewards/soft_format_reward_func": 0.0,
"rewards/strict_format_reward_func": 0.46875,
"rewards/xmlcount_reward_func": 1.203125,
"step": 18
},
{
"completion_length": 130.875,
"epoch": 1.0,
"grad_norm": 75.74906921386719,
"kl": 1.873372424626723,
"learning_rate": 0.0,
"loss": 0.0019,
"reward": 6.883746221661568,
"reward_std": 0.35684540566580836,
"rewards/concensus_correctness_reward_func": 2.11268749833107,
"rewards/consensus_reward_func": 1.8125,
"rewards/cumulative_reward_2": 0.0,
"rewards/final_correctness_reward_func": 0.375,
"rewards/question_recreation_reward_func": 0.9117461740970612,
"rewards/soft_format_reward_func": 0.0,
"rewards/strict_format_reward_func": 0.453125,
"rewards/xmlcount_reward_func": 1.218687504529953,
"step": 20
},
{
"epoch": 1.0,
"step": 20,
"total_flos": 0.0,
"train_loss": 0.006785672821570188,
"train_runtime": 1090.6448,
"train_samples_per_second": 0.293,
"train_steps_per_second": 0.018
}
],
"logging_steps": 2,
"max_steps": 20,
"num_input_tokens_seen": 0,
"num_train_epochs": 1,
"save_steps": 25,
"stateful_callbacks": {
"TrainerControl": {
"args": {
"should_epoch_stop": false,
"should_evaluate": false,
"should_log": false,
"should_save": true,
"should_training_stop": true
},
"attributes": {}
}
},
"total_flos": 0.0,
"train_batch_size": 2,
"trial_name": null,
"trial_params": null
}
|