Instructions to use swadeshb/tivd-gsm8k-colocate with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use swadeshb/tivd-gsm8k-colocate with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-1.7B") model = PeftModel.from_pretrained(base_model, "swadeshb/tivd-gsm8k-colocate") - Transformers
How to use swadeshb/tivd-gsm8k-colocate with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="swadeshb/tivd-gsm8k-colocate") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("swadeshb/tivd-gsm8k-colocate", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use swadeshb/tivd-gsm8k-colocate with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "swadeshb/tivd-gsm8k-colocate" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "swadeshb/tivd-gsm8k-colocate", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/swadeshb/tivd-gsm8k-colocate
- SGLang
How to use swadeshb/tivd-gsm8k-colocate 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 "swadeshb/tivd-gsm8k-colocate" \ --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": "swadeshb/tivd-gsm8k-colocate", "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 "swadeshb/tivd-gsm8k-colocate" \ --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": "swadeshb/tivd-gsm8k-colocate", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use swadeshb/tivd-gsm8k-colocate with Docker Model Runner:
docker model run hf.co/swadeshb/tivd-gsm8k-colocate
Model save
Browse files- README.md +59 -0
- tivd_runtime_config.json +202 -0
README.md
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---
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library_name: peft
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license: apache-2.0
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base_model: Qwen/Qwen3-1.7B
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tags:
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- base_model:adapter:Qwen/Qwen3-1.7B
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- lora
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- transformers
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pipeline_tag: text-generation
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model-index:
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- name: tivd-gsm8k-colocate
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# tivd-gsm8k-colocate
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This model is a fine-tuned version of [Qwen/Qwen3-1.7B](https://huggingface.co/Qwen/Qwen3-1.7B) on an unknown dataset.
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 1e-05
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- train_batch_size: 3
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- eval_batch_size: 8
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- seed: 42
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- gradient_accumulation_steps: 8
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- total_train_batch_size: 24
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- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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- lr_scheduler_type: linear
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- num_epochs: 3.0
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### Training results
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### Framework versions
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- PEFT 0.18.1
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- Transformers 4.57.6
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- Pytorch 2.9.1+cu128
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| 58 |
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- Datasets 4.8.4
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- Tokenizers 0.22.2
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tivd_runtime_config.json
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{
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| 2 |
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"accelerator_config": {
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| 3 |
+
"dispatch_batches": null,
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| 4 |
+
"even_batches": true,
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| 5 |
+
"gradient_accumulation_kwargs": null,
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| 6 |
+
"non_blocking": false,
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| 7 |
+
"split_batches": false,
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| 8 |
+
"use_seedable_sampler": true
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},
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| 10 |
+
"adafactor": false,
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| 11 |
+
"adam_beta1": 0.9,
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| 12 |
+
"adam_beta2": 0.999,
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| 13 |
+
"adam_epsilon": 1e-08,
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| 14 |
+
"auto_find_batch_size": false,
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| 15 |
+
"auto_launch_vllm_server": false,
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| 16 |
+
"average_tokens_across_devices": true,
|
| 17 |
+
"batch_eval_metrics": false,
|
| 18 |
+
"bf16": true,
|
| 19 |
+
"bf16_full_eval": false,
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| 20 |
+
"bootstrap_truncated_completions": true,
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| 21 |
+
"data_seed": null,
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| 22 |
+
"dataloader_drop_last": false,
|
| 23 |
+
"dataloader_num_workers": 0,
|
| 24 |
+
"dataloader_persistent_workers": false,
|
| 25 |
+
"dataloader_pin_memory": true,
|
| 26 |
+
"dataloader_prefetch_factor": null,
|
| 27 |
+
"ddp_backend": null,
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| 28 |
+
"ddp_broadcast_buffers": null,
|
| 29 |
+
"ddp_bucket_cap_mb": null,
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| 30 |
+
"ddp_find_unused_parameters": null,
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| 31 |
+
"ddp_timeout": 1800,
|
| 32 |
+
"debug": [],
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| 33 |
+
"deepspeed": null,
|
| 34 |
+
"difficulty_threshold": 6.0,
|
| 35 |
+
"disable_tqdm": false,
|
| 36 |
+
"do_eval": false,
|
| 37 |
+
"do_predict": false,
|
| 38 |
+
"do_train": false,
|
| 39 |
+
"enable_thinking": true,
|
| 40 |
+
"eval_accumulation_steps": null,
|
| 41 |
+
"eval_delay": 0,
|
| 42 |
+
"eval_do_concat_batches": true,
|
| 43 |
+
"eval_on_start": false,
|
| 44 |
+
"eval_steps": null,
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| 45 |
+
"eval_strategy": "no",
|
| 46 |
+
"eval_use_gather_object": false,
|
| 47 |
+
"fp16": false,
|
| 48 |
+
"fp16_backend": "auto",
|
| 49 |
+
"fp16_full_eval": false,
|
| 50 |
+
"fp16_opt_level": "O1",
|
| 51 |
+
"fsdp": [],
|
| 52 |
+
"fsdp_config": {
|
| 53 |
+
"min_num_params": 0,
|
| 54 |
+
"xla": false,
|
| 55 |
+
"xla_fsdp_grad_ckpt": false,
|
| 56 |
+
"xla_fsdp_v2": false
|
| 57 |
+
},
|
| 58 |
+
"fsdp_min_num_params": 0,
|
| 59 |
+
"fsdp_transformer_layer_cls_to_wrap": null,
|
| 60 |
+
"full_determinism": false,
|
| 61 |
+
"gamma": 1.0,
|
| 62 |
+
"gradient_accumulation_steps": 8,
|
| 63 |
+
"gradient_checkpointing": false,
|
| 64 |
+
"gradient_checkpointing_kwargs": null,
|
| 65 |
+
"greater_is_better": null,
|
| 66 |
+
"group_by_length": false,
|
| 67 |
+
"half_precision_backend": "auto",
|
| 68 |
+
"hub_always_push": false,
|
| 69 |
+
"hub_model_id": "swadeshb/tivd-gsm8k-colocate",
|
| 70 |
+
"hub_private_repo": false,
|
| 71 |
+
"hub_revision": null,
|
| 72 |
+
"hub_strategy": "every_save",
|
| 73 |
+
"hub_token": "<HUB_TOKEN>",
|
| 74 |
+
"ignore_data_skip": false,
|
| 75 |
+
"include_for_metrics": [],
|
| 76 |
+
"include_inputs_for_metrics": false,
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| 77 |
+
"include_num_input_tokens_seen": "no",
|
| 78 |
+
"include_tokens_per_second": false,
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| 79 |
+
"jit_mode_eval": false,
|
| 80 |
+
"label_names": [],
|
| 81 |
+
"label_smoothing_factor": 0.0,
|
| 82 |
+
"learning_rate": 1e-05,
|
| 83 |
+
"length_column_name": "length",
|
| 84 |
+
"liger_kernel_config": null,
|
| 85 |
+
"load_best_model_at_end": false,
|
| 86 |
+
"local_rank": 0,
|
| 87 |
+
"log_completions": true,
|
| 88 |
+
"log_completions_every_n_steps": 50,
|
| 89 |
+
"log_level": "passive",
|
| 90 |
+
"log_level_replica": "warning",
|
| 91 |
+
"log_on_each_node": true,
|
| 92 |
+
"logging_dir": "outputs/tivd_gsm8k_colocate/runs/Apr15_08-45-31_e2e-72-102",
|
| 93 |
+
"logging_first_step": false,
|
| 94 |
+
"logging_nan_inf_filter": true,
|
| 95 |
+
"logging_steps": 1.0,
|
| 96 |
+
"logging_strategy": "steps",
|
| 97 |
+
"lora_alpha": 32,
|
| 98 |
+
"lora_dropout": 0.05,
|
| 99 |
+
"lora_r": 16,
|
| 100 |
+
"lora_target_modules": "q_proj,k_proj,v_proj,o_proj,gate_proj,up_proj,down_proj",
|
| 101 |
+
"lr_scheduler_kwargs": null,
|
| 102 |
+
"lr_scheduler_type": "linear",
|
| 103 |
+
"max_completion_length": 2048,
|
| 104 |
+
"max_grad_norm": 1.0,
|
| 105 |
+
"max_prompt_length": 768,
|
| 106 |
+
"max_steps": -1,
|
| 107 |
+
"metric_for_best_model": null,
|
| 108 |
+
"min_p": 0.0,
|
| 109 |
+
"mp_parameters": "",
|
| 110 |
+
"neftune_noise_alpha": null,
|
| 111 |
+
"no_cuda": false,
|
| 112 |
+
"num_generations": 8,
|
| 113 |
+
"num_train_epochs": 3.0,
|
| 114 |
+
"optim": "adamw_torch_fused",
|
| 115 |
+
"optim_args": null,
|
| 116 |
+
"optim_target_modules": null,
|
| 117 |
+
"output_dir": "outputs/tivd_gsm8k_colocate",
|
| 118 |
+
"overwrite_output_dir": false,
|
| 119 |
+
"parallelism_config": null,
|
| 120 |
+
"past_index": -1,
|
| 121 |
+
"per_device_eval_batch_size": 8,
|
| 122 |
+
"per_device_train_batch_size": 3,
|
| 123 |
+
"per_gpu_eval_batch_size": null,
|
| 124 |
+
"per_gpu_train_batch_size": null,
|
| 125 |
+
"prediction_loss_only": false,
|
| 126 |
+
"project": "huggingface",
|
| 127 |
+
"push_to_hub": true,
|
| 128 |
+
"push_to_hub_model_id": null,
|
| 129 |
+
"push_to_hub_organization": null,
|
| 130 |
+
"push_to_hub_token": "<PUSH_TO_HUB_TOKEN>",
|
| 131 |
+
"ray_scope": "last",
|
| 132 |
+
"remove_unused_columns": false,
|
| 133 |
+
"repetition_penalty": 1.0,
|
| 134 |
+
"report_to": [
|
| 135 |
+
"wandb"
|
| 136 |
+
],
|
| 137 |
+
"restore_callback_states_from_checkpoint": false,
|
| 138 |
+
"resume_from_checkpoint": null,
|
| 139 |
+
"run_name": null,
|
| 140 |
+
"save_on_each_node": false,
|
| 141 |
+
"save_only_model": false,
|
| 142 |
+
"save_safetensors": true,
|
| 143 |
+
"save_steps": 100,
|
| 144 |
+
"save_strategy": "steps",
|
| 145 |
+
"save_total_limit": 1,
|
| 146 |
+
"score_batch_size": 3,
|
| 147 |
+
"seed": 42,
|
| 148 |
+
"skip_memory_metrics": true,
|
| 149 |
+
"soft_value_temperature": 1.0,
|
| 150 |
+
"steps_per_generation": 1,
|
| 151 |
+
"student_load_in_4bit": false,
|
| 152 |
+
"student_model_name_or_path": "Qwen/Qwen3-1.7B",
|
| 153 |
+
"system_prompt": "You are a careful mathematical reasoner. Solve the problem step by step. End with a concise final answer in the form Final Answer: \\boxed{...}.",
|
| 154 |
+
"target_ema_decay": 0.99,
|
| 155 |
+
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|
| 156 |
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|
| 157 |
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|
| 158 |
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|
| 159 |
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|
| 160 |
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|
| 161 |
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|
| 162 |
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|
| 163 |
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|
| 164 |
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|
| 165 |
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|
| 166 |
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| 167 |
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|
| 168 |
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|
| 169 |
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| 170 |
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|
| 171 |
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|
| 172 |
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"trackio_space_id": "trackio",
|
| 173 |
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"trust_remote_code": true,
|
| 174 |
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"use_cpu": false,
|
| 175 |
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"use_legacy_prediction_loop": false,
|
| 176 |
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"use_liger_kernel": false,
|
| 177 |
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|
| 178 |
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|
| 179 |
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|
| 180 |
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"vllm_cuda_visible_devices": "1",
|
| 181 |
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"vllm_enable_prefix_caching": true,
|
| 182 |
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|
| 183 |
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| 184 |
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"vllm_fail_if_world_size_gt_1": true,
|
| 185 |
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"vllm_gpu_memory_utilization": 0.3,
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| 186 |
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"vllm_group_port": null,
|
| 187 |
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"vllm_kv_cache_dtype": "fp8",
|
| 188 |
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"vllm_max_model_length": 3072,
|
| 189 |
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"vllm_mode": "colocate",
|
| 190 |
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"vllm_model_impl": "vllm",
|
| 191 |
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"vllm_quantization": "bitsandbytes",
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| 192 |
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| 193 |
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"vllm_server_host": "127.0.0.1",
|
| 194 |
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|
| 195 |
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|
| 196 |
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| 197 |
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|
| 198 |
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|
| 199 |
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|
| 200 |
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|
| 201 |
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"weight_decay": 0.0
|
| 202 |
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}
|