Text Generation
Transformers
Safetensors
English
qwen3_5_moe
image-text-to-text
mira
mid-training
data-selection
rubric-scorer
source-aware
Mixture of Experts
qwen3
conversational
Instructions to use whw06/MIRA-Text-Group3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use whw06/MIRA-Text-Group3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="whw06/MIRA-Text-Group3") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] pipe(text=messages)# Load model directly from transformers import AutoProcessor, AutoModelForImageTextToText processor = AutoProcessor.from_pretrained("whw06/MIRA-Text-Group3") model = AutoModelForImageTextToText.from_pretrained("whw06/MIRA-Text-Group3") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] inputs = processor.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(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use whw06/MIRA-Text-Group3 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "whw06/MIRA-Text-Group3" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "whw06/MIRA-Text-Group3", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/whw06/MIRA-Text-Group3
- SGLang
How to use whw06/MIRA-Text-Group3 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 "whw06/MIRA-Text-Group3" \ --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": "whw06/MIRA-Text-Group3", "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 "whw06/MIRA-Text-Group3" \ --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": "whw06/MIRA-Text-Group3", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use whw06/MIRA-Text-Group3 with Docker Model Runner:
docker model run hf.co/whw06/MIRA-Text-Group3
| { | |
| "best_global_step": null, | |
| "best_metric": null, | |
| "best_model_checkpoint": null, | |
| "epoch": 2.0, | |
| "eval_steps": 500, | |
| "global_step": 334, | |
| "is_hyper_param_search": false, | |
| "is_local_process_zero": true, | |
| "is_world_process_zero": true, | |
| "log_history": [ | |
| { | |
| "epoch": 0.06010518407212622, | |
| "grad_norm": 2.776237700093034, | |
| "learning_rate": 2.647058823529412e-06, | |
| "loss": 1.2284340858459473, | |
| "step": 10 | |
| }, | |
| { | |
| "epoch": 0.12021036814425244, | |
| "grad_norm": 1.1013368513651545, | |
| "learning_rate": 4.999508937232514e-06, | |
| "loss": 0.953094482421875, | |
| "step": 20 | |
| }, | |
| { | |
| "epoch": 0.18031555221637866, | |
| "grad_norm": 0.7678503876226538, | |
| "learning_rate": 4.982341987865914e-06, | |
| "loss": 0.7880845069885254, | |
| "step": 30 | |
| }, | |
| { | |
| "epoch": 0.24042073628850488, | |
| "grad_norm": 0.4960089758415, | |
| "learning_rate": 4.940814473856278e-06, | |
| "loss": 0.7320358276367187, | |
| "step": 40 | |
| }, | |
| { | |
| "epoch": 0.3005259203606311, | |
| "grad_norm": 0.4611853514743128, | |
| "learning_rate": 4.875333927161104e-06, | |
| "loss": 0.7054203033447266, | |
| "step": 50 | |
| }, | |
| { | |
| "epoch": 0.3606311044327573, | |
| "grad_norm": 0.45095741292705066, | |
| "learning_rate": 4.7865429438039955e-06, | |
| "loss": 0.687248706817627, | |
| "step": 60 | |
| }, | |
| { | |
| "epoch": 0.42073628850488354, | |
| "grad_norm": 0.445510626195826, | |
| "learning_rate": 4.6753128777323e-06, | |
| "loss": 0.6785623550415039, | |
| "step": 70 | |
| }, | |
| { | |
| "epoch": 0.48084147257700977, | |
| "grad_norm": 0.4218098042597313, | |
| "learning_rate": 4.542735289749498e-06, | |
| "loss": 0.66799898147583, | |
| "step": 80 | |
| }, | |
| { | |
| "epoch": 0.540946656649136, | |
| "grad_norm": 0.42965824692515564, | |
| "learning_rate": 4.390111235438606e-06, | |
| "loss": 0.6619597434997558, | |
| "step": 90 | |
| }, | |
| { | |
| "epoch": 0.6010518407212622, | |
| "grad_norm": 0.43461658184031327, | |
| "learning_rate": 4.218938497199996e-06, | |
| "loss": 0.6566213130950928, | |
| "step": 100 | |
| }, | |
| { | |
| "epoch": 0.6611570247933884, | |
| "grad_norm": 0.4516606558101725, | |
| "learning_rate": 4.03089688570253e-06, | |
| "loss": 0.649503517150879, | |
| "step": 110 | |
| }, | |
| { | |
| "epoch": 0.7212622088655146, | |
| "grad_norm": 0.44000894914700206, | |
| "learning_rate": 3.827831754992854e-06, | |
| "loss": 0.6441732883453369, | |
| "step": 120 | |
| }, | |
| { | |
| "epoch": 0.7813673929376409, | |
| "grad_norm": 0.46937207230196937, | |
| "learning_rate": 3.611735893037967e-06, | |
| "loss": 0.6436738491058349, | |
| "step": 130 | |
| }, | |
| { | |
| "epoch": 0.8414725770097671, | |
| "grad_norm": 0.43338172376594786, | |
| "learning_rate": 3.3847299654189947e-06, | |
| "loss": 0.638824462890625, | |
| "step": 140 | |
| }, | |
| { | |
| "epoch": 0.9015777610818934, | |
| "grad_norm": 0.41244075171530503, | |
| "learning_rate": 3.1490417040927513e-06, | |
| "loss": 0.6347045421600341, | |
| "step": 150 | |
| }, | |
| { | |
| "epoch": 0.9616829451540195, | |
| "grad_norm": 0.4435736854495128, | |
| "learning_rate": 2.9069840454530583e-06, | |
| "loss": 0.6339764595031738, | |
| "step": 160 | |
| }, | |
| { | |
| "epoch": 1.018031555221638, | |
| "grad_norm": 0.43042109834301273, | |
| "learning_rate": 2.660932432234823e-06, | |
| "loss": 0.6272397994995117, | |
| "step": 170 | |
| }, | |
| { | |
| "epoch": 1.0781367392937642, | |
| "grad_norm": 0.4130061435691091, | |
| "learning_rate": 2.413301502009591e-06, | |
| "loss": 0.6114815235137939, | |
| "step": 180 | |
| }, | |
| { | |
| "epoch": 1.1382419233658903, | |
| "grad_norm": 0.4038777948776202, | |
| "learning_rate": 2.166521391040963e-06, | |
| "loss": 0.6114835739135742, | |
| "step": 190 | |
| }, | |
| { | |
| "epoch": 1.1983471074380165, | |
| "grad_norm": 0.40817003614353453, | |
| "learning_rate": 1.923013886042956e-06, | |
| "loss": 0.6095846176147461, | |
| "step": 200 | |
| }, | |
| { | |
| "epoch": 1.2584522915101428, | |
| "grad_norm": 0.40324569219607864, | |
| "learning_rate": 1.6851686578770263e-06, | |
| "loss": 0.6101284980773926, | |
| "step": 210 | |
| }, | |
| { | |
| "epoch": 1.3185574755822689, | |
| "grad_norm": 0.40166556982490487, | |
| "learning_rate": 1.4553198104193094e-06, | |
| "loss": 0.6100864410400391, | |
| "step": 220 | |
| }, | |
| { | |
| "epoch": 1.3786626596543952, | |
| "grad_norm": 0.3809282869819938, | |
| "learning_rate": 1.235722974736756e-06, | |
| "loss": 0.6077391147613526, | |
| "step": 230 | |
| }, | |
| { | |
| "epoch": 1.4387678437265214, | |
| "grad_norm": 0.38079334134428333, | |
| "learning_rate": 1.0285331733593778e-06, | |
| "loss": 0.6076976776123046, | |
| "step": 240 | |
| }, | |
| { | |
| "epoch": 1.4988730277986475, | |
| "grad_norm": 0.38514315862275184, | |
| "learning_rate": 8.357836718785018e-07, | |
| "loss": 0.6098727226257324, | |
| "step": 250 | |
| }, | |
| { | |
| "epoch": 1.558978211870774, | |
| "grad_norm": 1.080984219484949, | |
| "learning_rate": 6.593660254117104e-07, | |
| "loss": 0.6043878555297851, | |
| "step": 260 | |
| }, | |
| { | |
| "epoch": 1.6190833959429, | |
| "grad_norm": 0.3893836987691287, | |
| "learning_rate": 5.010115157493198e-07, | |
| "loss": 0.6059476375579834, | |
| "step": 270 | |
| }, | |
| { | |
| "epoch": 1.6791885800150261, | |
| "grad_norm": 0.37390375440579593, | |
| "learning_rate": 3.622741613497047e-07, | |
| "loss": 0.6050002098083496, | |
| "step": 280 | |
| }, | |
| { | |
| "epoch": 1.7392937640871526, | |
| "grad_norm": 0.3823417162941356, | |
| "learning_rate": 2.4451546691559305e-07, | |
| "loss": 0.6037191867828369, | |
| "step": 290 | |
| }, | |
| { | |
| "epoch": 1.7993989481592787, | |
| "grad_norm": 0.39472217562650863, | |
| "learning_rate": 1.4889106221196686e-07, | |
| "loss": 0.5994118213653564, | |
| "step": 300 | |
| }, | |
| { | |
| "epoch": 1.859504132231405, | |
| "grad_norm": 0.4433640201370672, | |
| "learning_rate": 7.633936124608998e-08, | |
| "loss": 0.6043890953063965, | |
| "step": 310 | |
| }, | |
| { | |
| "epoch": 1.9196093163035313, | |
| "grad_norm": 0.3775912358838846, | |
| "learning_rate": 2.7572353103262617e-08, | |
| "loss": 0.6042394638061523, | |
| "step": 320 | |
| }, | |
| { | |
| "epoch": 1.9797145003756573, | |
| "grad_norm": 0.36394036662336054, | |
| "learning_rate": 3.0686148128050707e-09, | |
| "loss": 0.6040500640869141, | |
| "step": 330 | |
| } | |
| ], | |
| "logging_steps": 10, | |
| "max_steps": 334, | |
| "num_input_tokens_seen": 0, | |
| "num_train_epochs": 2, | |
| "save_steps": 100, | |
| "stateful_callbacks": { | |
| "TrainerControl": { | |
| "args": { | |
| "should_epoch_stop": false, | |
| "should_evaluate": false, | |
| "should_log": false, | |
| "should_save": true, | |
| "should_training_stop": true | |
| }, | |
| "attributes": {} | |
| } | |
| }, | |
| "total_flos": 2.0054478564622336e+16, | |
| "train_batch_size": 1, | |
| "trial_name": null, | |
| "trial_params": null | |
| } | |